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Abstract The current industrial wireless sensor network (IWSN) cluster routing methods suffer from energy inefficiency. Designing efficient cluster-based routing protocols is crucial for improving network performance and energy efficiency. Therefore, this paper first designs a new clustering model to achieve efficient cluster head (CH) selection and data transmission performance by comprehensively considering multiple key factors such as CH energy, base station (BS) distance, packet loss rate, and data delay. Based on this clustering model, a novel cluster routing protocol based on Gaussian mutation adaptive artificial fish swarm algorithm (GAAFSA) is proposed. At the same time, a new Gaussian mutation strategy and an adaptive strategy were introduced to effectively promote the protocol to avoid local optima and prevent premature convergence. The GAAFSA based cluster routing protocol was experimentally compared with five popular schemes, namely CMSTR, D2CRP, EEHCHR, ESCVAD and BAFSA. The results showed that the proposed protocol outperformed the other four schemes in terms of network energy consumption, system lifetime, data transmission reliability, and latency. Specifically, GAAFSA has improved network lifespan by at least 15.68%, BS received packets by at least 7.46%, and reduced packet loss rate by at least 15.28%. Therefore, GAAFSA effectively optimizes network performance and extends network lifespan, greatly reducing energy loss within the network and significantly improving network quality of service (QoS).

Abstract The demand for high-capacity batteries with long cycle life and safety has been increasing owing to the expanding mid-to-large battery market. Li–S batteries are suitable energy-storage devices because of their reversibility, high theoretical capacity, and inexpensive construction materials. However, their performance is limited by various factors, including the shuttle effect and dendrite growth at the anode. Here, an integrated electrode for use in all-solid-state (ASS) Li–S batteries was formed via hot pressing. In detail, S particles dispersed in a functionalized reduced graphite oxide (rGO) cathode with a binder-less polymer electrolyte (PE) and a dual-anion ionic liquid-containing cross-linked poly(ethylene oxide)–Li bis(fluoromethanesulfonyl)imide–N-butyl-N-methylpyrrolidinium bis(trifluoromethanesulfonyl) imide-based solid polymer electrolyte (SPE, PEO–LiFSI0.1(Pyr14TFSI)0.4) were hot-pressed into an integrated electrode, which serves as both the cathode and electrolyte. The resulting S/rGO-based solid-state Li–S batteries exhibited more stable performance than Li–S batteries using liquid electrolytes did, indicating that the dual-anion SPE layer effectively suppressed dendritic Li formation and the shuttle effect with high ionic conductivity. At 0.1 C, the battery discharge capacities were 957 and 576 mAh g−1 in the first cycle and after 100 cycles, respectively. At 1 C, the reversible capacity was 590 and 417 mAh g−1 in the first cycle and after 100 cycles, respectively (capacity retention = 71%). Therefore, the proposed S/rGO/PE//LiFSI0.1(Pyr14TFSI)0.4-integrated electrodes are beneficial for ASS Li–S batteries.

Abstract Background Colorectal cancer is the third most common cancer and the second leading cause of cancer death. There are limited therapeutic options for the treatment of locally advanced or metastatic colorectal cancers which fail first-line chemotherapy. Phase I/II studies showed that the combined application of the raltitrexed and irinotecan has significant synergistic effect and acceptable toxicity. However, most of these previous studies have relatively small sample size. Methods This is a prospective open-label, single-arm, multi-center, Phase II trial. Brief inclusion criteria: patients were aged 18 to 75 years with locally advanced or metastatic colorectal cancer after failure of 5-FU and oxaliplatin therapy. Enrolled patients received raltitrexed (3 mg/m2, d1) and irinotecan (180 mg/m2, d1) each 21-day cycle until disease progression or unacceptable toxicity. The primary endpoint was progression-free survival, and the secondary endpoints were disease control rate, objective response rate, overall survival and safety. Results A total of 108 patients were enrolled between September 2016 and May 2020. The median age was 61 years, ECOG 1 score accounts for 67.6%, the rest were ECOG 0. A total of 502 cycles were completed, with an average of 4.6 cycles and a median of 4 cycles. 108 patients were evaluated, with an objective response rate of 17.6%, and disease control rate of 76.9%. The median follow-up time was 27 months (range:3.1–61.0 m) at data cut-off on March 2023. Median progression-free survival was 4.9 months (95% CI 4.1–5.7) and median overall survival was 13.1 months (95% CI 12.2–15.5). The most common adverse events that were elevated are alanine aminotransferase increased, aspartate aminotransferase increased, fatigue, diarrhoea, neutrocytopenia, thrombocytopenia, hypohemoglobin, and leukocytopenia. Most of the adverse events were Grade I/II, which were relieved after symptomatic treatment, and there were no treatment-related cardiotoxicities and deaths. Conclusions The combination of raltitrexed and irinotecan as second-line treatment for mCRC could be a reliable option after failure of standard 5-Fu-first-line chemotherapy in locally advanced or metastatic colorectal cancers, especially for patients with 5-FU intolerance (cardiac events or DPD deficiency patients). Trial registration ClinicalTrials.gov identifier: NCT03053167, registration date was 14/2/2017.

Microscale energetic materials have garnered considerable attention because they can be integrated in microelectromechanical systems for several potential applications. Hexanitrostilbene (HNS) has been widely utilized in ignition and initiation devices owing to its excellent thermal and shock stability and high sensitivity to short pulse shock waves. To meet the energy release requirements of micro-detonation devices, HNS sticks were prepared using direct ink writing and their reaction and flame propagation properties were studied. HNS particles sized 17.5 µm to 5 µm and ∼500 nm was prepared. Then, the viscosity of HNS-based inks prepared via acoustic resonance technology decreased when the particle size decreased or when the HNS content reduced from 97 wt.%, 95 wt.%, and 92 wt.%, to 90 wt.%. Additionally, the HNS-based inks with different binders exhibited different viscosity. The effect of the inner diameter of needle (0.4 mm, 0.6 mm, and 0.9 mm), printing rate (13 mm/s, 15 mm/s, 17 mm/s, 19 mm/s), and pressure (0.05 MPa, 0.1 MPa, 0.15 MPa, 0.2 MPa) on the width of HNS sticks was also elucidated. HNS-based sticks with a diameter of 0.9 mm underwent self-sustaining combustion reactions, and the burning rate increased from 5.1 mm/s to 6.8 mm/s as the particle size decreased from 17.5 µm to 500 nm. Overall, this work provides an effective approach to prepare microscale HNS for integration into micro-energetic devices.

Abstract Artificial rabbits optimization (ARO) is a metaheuristic algorithm based on the survival strategy of rabbits proposed in 2022. ARO has favorable optimization performance, but it still has some shortcomings, such as weak exploitation capacity, easy to fall into local optima, and serious decline of population diversity at the later stage. In order to solve these problems, we propose an improved multi-strategy artificial rabbits optimization, called IMARO, based on ARO algorithm. In this paper, a roulette fitness distance balanced hiding strategy is proposed so that rabbits can find better locations to hide more reasonably. Meanwhile, in order to improve the deficiency of ARO which is easy to fall into local optimum, an improved non-monopoly search strategy based on Gaussian and Cauchy operators is designed to improve the ability of the algorithm to obtain the global optimal solution. Finally, a covariance restart strategy is designed to improve population diversity when the exploitation is stagnant and to improve the convergence accuracy and convergence speed of ARO. The performance of IMARO is verified by comparing original ARO algorithm with six basic algorithms and seven improved algorithms. The results of CEC2014, CEC2017, CEC2022 show that IMARO has a good exploitation and exploration ability and can effectively get rid of local optimum. Moreover, IMARO produces optimal results on six real-world engineering problems, further demonstrating its efficiency in solving real-world optimization challenges.

Abstract Background Nonalcoholic fatty liver disease (NAFLD) is a chronic disease with an increasing incidence, which can further develop into liver fibrosis and hepatocellular carcinoma at the end stage. Alantolactone (Ala), a sesquiterpene lactone isolated from Asteraceae, has shown anti-inflammatory effects in different models. However, the therapeutic effect of Ala on NAFLD is not clear. Methods C57BL/6 mice were fed a high-fat diet (HFD) to induce NAFLD. After 16 weeks, Ala was administered by gavage to observe its effect on NAFLD. RNA sequencing of liver tissues was performed to investigate the mechanism. In vitro, mouse cell line AML-12 was pretreated with Ala to resist palmitic acid (PA)-induced inflammation, oxidative stress and fibrosis. Results Ala significantly inhibited inflammation, fibrosis and oxidative stress in HFD-induced mice, as well as PA-induced AML-12 cells. Mechanistic studies showed that the effect of Ala was related to the induction of Nrf2 and the inhibition of NF-κB. Taken together, these findings suggested that Ala exerted a liver protective effect on NAFLD by blocking inflammation and oxidative stress. Conclusions The study found that Ala exerted a liver protective effect on NAFLD by blocking inflammation and oxidative stress, suggesting that Ala is an effective therapy for NAFLD.

Background and Objective Chronic kidney disease (CKD) is a major public health issue, and accurate prediction of the progression of kidney failure is critical for clinical decision-making and helps improve patient outcomes. As such, we aimed to develop and externally validate a machine-learned model to predict the progression of CKD using common laboratory variables, demographic characteristics, and an electronic health records database. Methods We developed a predictive model using longitudinal clinical data from a single center for Chinese CKD patients. The cohort included 987 patients who were followed up for more than 24 months. Fifty-three laboratory features were considered for inclusion in the model. The primary outcome in our study was an estimated glomerular filtration rate ≤15 mL/min/1.73 m2 or kidney failure. Machine learning algorithms were applied to the modeling dataset (n = 296), and an external dataset (n = 71) was used for model validation. We assessed model discrimination via area under the curve (AUC) values, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. Results Over a median follow-up period of 3.75 years, 148 patients experienced kidney failure. The optimal model was based on stacking different classifier algorithms with six laboratory features, including 24-h urine protein, potassium, glucose, urea, prealbumin and total protein. The model had considerable predictive power, with AUC values of 0.896 and 0.771 in the validation and external datasets, respectively. This model also accurately predicted the progression of renal function in patients over different follow-up periods after their initial assessment. Conclusions A prediction model that leverages routinely collected laboratory features in the Chinese population can accurately identify patients with CKD at high risk of progressing to kidney failure. An online version of the model can be easily and quickly applied in clinical management and treatment.

The Container Relocation Problem (CRP) is one of the most important optimization problems in maritime container terminals. The objective is to minimize the number of relocation operations for retrieving containers in a sequence. If the container to be retrieved next is not at the top of a stack, unproductive relocations have to be carried out. Due to the large number of containers handled by busy terminals, a slight reduction in relocation rates can result in significant savings in operating costs. Most of the existing heuristics make relocation decisions for the blocking containers one by one, based on simple indicators. In this article, we propose a Three-Stage Heuristic (3SH) that extends the decision horizon to multiple containers to achieve a higher-quality solution. Computational experiments are conducted on 3 sets of benchmark instances, and the results show that the proposed heuristic outperforms the state-of-the-art heuristics documented in the research literature.

Abstract Oxidative stress induced endothelial dysfunction plays a particularly important role in promoting the development of cardiovascular diseases (CVDs). Salvianolic acid A (SalA) is a water-soluble component of traditional Chinese medicine Salvia miltiorrhiza Bunge with anti-oxidant potency. This study aims to explore the regulatory effect of SalA on oxidative injury using an in vitro model of H2O2-induced injury in human umbilical vein endothelial cells (HUVECs). In the study, we determined cell viability, the activities of Lactate dehydrogenase (LDH) and Superoxide dismutase (SOD), cell proliferation rate and intracellular reactive oxygen species (ROS). Flow cytometry was used to detect cell apoptosis. Western-blotting was used to evaluate the expression of cell senescence, apoptosis, autophagy and pyroptosis protein factors. The expression level of miRNA was determined by qRT-PCR. Compared with H2O2-induced HUVECs, SalA promoted cell viability and cell proliferation rate; decreased LDH and ROS levels; and increased SOD activity. SalA also significantly attenuated endothelial senescence, inhibited cell apoptosis, reversed the increase of LC3 II/I ratio and NLRP3 accumulation. Furthermore, miR-204-5p was regulated by SalA. Importantly, miR-204-5p inhibitor had similar effect to that of SalA on H2O2-induced HUVECs. Our results indicated that SalA could alleviate H2O2-induced oxidative injury by downregulating miR-204-5p in HUVECs.

Abstract Background Diabetic cardiomyopathy (DCM), a serious complication of diabetes, leads to structural and functional abnormalities of the heart and ultimately evolves to heart failure. IL-37 exerts a substantial influence on the regulation of inflammation and metabolism. Whether IL-37 is involved in DCM is unknown. Methods The plasma samples were collected from healthy controls, diabetic patients and DCM patients, and the level of IL-37 and its relationship with heart function were observed. The changes in cardiac function, myocardial fibrosis and mitochondrial injury in DCM mice with or without IL-37 intervention were investigated in vivo. By an in vitro co-culture approach involving HG challenge of cardiomyocytes and fibroblasts, the interaction carried out by cardiomyocytes on fibroblast profibrotic activation was studied. Finally, the possible interactive mediator between cardiomyocytes and fibroblasts was explored, and the intervention role of IL-37 and its relevant molecular mechanisms. Results We showed that the level of plasma IL-37 in DCM patients was upregulated compared to that in healthy controls and diabetic patients. Both recombinant IL-37 administration or inducing IL-37 expression alleviated cardiac dysfunction and myocardial fibrosis in DCM mice. Mechanically, hyperglycemia impaired mitochondria through SIRT1/AMPK/PGC1α signaling, resulting in significant cardiomyocyte apoptosis and the release of extracellular vesicles containing mtDNA. Fibroblasts then engulfed these mtDNA-enriched vesicles, thereby activating TLR9 signaling and the cGAS-STING pathway to initiate pro-fibrotic process and adverse remodeling. However, the presence of IL-37 ameliorated mitochondrial injury by preserving the activity of SIRT1-AMPK-PGC1α axis, resulting in a reduction in release of mtDNA-enriched vesicle and ultimately attenuating the progression of DCM. Conclusions Collectively, our study demonstrates a protective role of IL-37 in DCM, offering a promising therapeutic agent for this disease. Graphical abstract Hyperglycemia aggravates mitochondrial injury through SIRT1/AMPK/PGC1α signaling, resulting in significant cardiomyocyte apoptosis and the release of extracellular vesicles containing mtDNA in DCM mice. Fibroblasts then engulf these mtDNA-enriched vesicles, activating TLR9 signaling and the cGAS-STING pathway to initiate profibrotic process and adverse remodeling. However, both exogenous and endogenous IL-37 ameliorate mitochondrial injury by preserving the activity of SIRT1-AMPK-PGC1α axis, and reducing the release of mtDNA-enriched vesicles, which attenuates the progression of DCM

Abstract Arylamines, serving as crucial building blocks in natural products and finding applications in multifunctional materials, are synthesized on a large scale via an electrophilic nitration/reduction sequence. However, the current methods for aromatic C–H amination have not yet attained the same level of versatility as electrophilic nitration. Here we show an extensively investigated transition metal-free and regioselective strategy for the amination of nitrobenzenes, enabling the synthesis of 4-nitro-N-arylamines through C(sp2)-H/N-H cross-coupling between electron-deficient nitroarenes and amines. Mechanistic studies have elucidated that the crucial aspects of these reactions encompass the generation of nitrogen radicals and recombination of nitrobenzene complex radicals. The C(sp2)-N bond formation is demonstrated to be highly effective for primary and secondary arylamines as well as aliphatic amines under mild conditions, exhibiting exceptional tolerance towards diverse functional groups in both nitroarenes and amines (>100 examples with yields up to 96%). Notably, this C(sp2)-H/N-H cross-coupling exhibits exclusive para-selectivity.

Objective To investigate the relationship between grip strength and physical mobility and bone mineral density(BMD) in elderly hospitalized patients. Methods 235 elderly patients(≥ 60 years) treated in the Department of Geriatrics of the Second Affiliated Hospital of Dalian Medical University from October 2020 to October 2021 were selected to assess their grip strength and physical mobility(gait speed, 5 sitting up test time, and up-and-go test time) and analyze their correlation with BMD. Results Univariate analysis showed that grip strength and gait speed were positively correlated with BMD, and up-and-go test time and 5 sitting up test time were negatively correlated with BMD, and this correlation was present in patients of both genders(P

Biomass chitosan (CS) was used as a template, graphitic phase carbon nitride (g-C3N4) with high nitrogen content and certain catalytic activity was used as a dopant, and nano-transition metal cobalt (Co) was used as a catalytic center point. The carbon aerogel (C (CS)/g-C3N4/Co) with a three-dimensional network-like structure was prepared by assembling the three materials through experimental operations such as freeze-drying and high-temperature carbonization. It was demonstrated by scanning and transmission characterization that the CS in the carbon aerogel could provide more active sites for the cobalt nanoparticles, and the doping of graphite-phase carbon nitride as a template dispersed the cobalt nanoparticles and changed the conductivity of the CS. To investigate the catalytic effect of carbon aerogel on ammonium perchlorate (AP), it was investigated by differential thermal analyzer and TG thermal analysis. This carbon aerogel was very effective in catalyzing AP, and the 10 wt% content of the catalyst reduced the AP pyrolysis peak from 703.9 to 595.5 K. And to further investigate the synergistic effect of the three materials, further carbon aerogels such as C (CS)/Co, g-C3N4/Co were prepared and applied to catalyze AP, and the same ratio reduced the AP pyrolysis peak by 98.1 °C and 97.7 °C. This result indicates a synergistic effect of the assembly of the three materials.

Abstract Background Pyroptosis of the renal tubular epithelial cells (RTECs) and interstitial inflammation are central pathological characteristics of acute kidney injury (AKI). Pyroptosis acts as a pro-inflammatory form of programmed cell death and is mainly dependent on activation of the NLRP3 inflammasome. Previous studies revealed that acetyl-CoA synthetase 2 (ACSS2) promotes inflammation during metabolic stress suggesting that ACSS2 might regulate pyroptosis and inflammatory responses of RTECs in AKI. Methods and results The expression of ACSS2 was found to be significantly increased in the renal epithelial cells of mice with lipopolysaccharide (LPS)-induced AKI. Pharmacological and genetic strategies demonstrated that ACSS2 regulated NLRP3-mediated caspase-1 activation and pyroptosis through the stimulation of the KLF5/NF-κB pathway in RTECs. The deletion of ACSS2 attenuated renal tubular pathological injury and inflammatory cell infiltration in an LPS-induced mouse model, and ACSS2-deficient mice displayed impaired NLRP3 activation-mediated pyroptosis and decreased IL-1β production in response to the LPS challenge. In HK-2 cells, ACSS2 deficiency suppressed NLRP3-mediated caspase-1 activation and pyroptosis through the downregulation of the KLF5/NF-κB pathway. The KLF5 inhibitor ML264 suppressed NF-κB activity and NLRP3-mediated caspase-1 activation, thus protecting HK-2 cells from LPS-induced pyroptosis. Conclusion Our results suggested that ACSS2 regulates activation of the NLRP3 inflammasome and pyroptosis by inducing the KLF5/NF-κB pathway in RTECs. These results identified ACSS2 as a potential therapeutic target in AKI. Graphical Abstract We found that the expression of ACSS2 was significantly increased in RTECs in septic AKI. ACSS2-deficient mice displayed resistance to renal damage in this model. ACSS2 regulated NLRP3-mediated caspase-1 activation and pyroptosis through the KLF5/NF-κB pathway in RTECs. Pharmacological inhibition of KLF5 suppressed NLRP3 activation and caspase-1 cleavage by downregulating of NF-κB. Our results suggest that ACSS2 is a pro-pathogenic mediator of cell pyroptosis of RTECs and renal inflammation in sepsis-induced AKI.

Abstract. Background:. Mild traumatic brain injury (mTBI) is a common neurological trauma that can lead to cognitive impairment. The sirtuin-1 (SIRT-1)/peroxisome proliferator-activated receptor gamma coactivator-1α (PGC-1α) pathway has been reported to have neuroprotective effects in rats with craniocerebral injury. We evaluated potential mechanisms underlying electroacupuncture-mediated recovery of cognitive function after mTBI, focusing on the SIRT-1/PGC-1α/mitochondrial pathway. Methods:. We included forty 6-week-old male Sprague-Dawley rats in this study. Rats were randomly divided into four groups: controlled cortical impactor (CCI, n = 10), sham operation (sham, n = 10), electroacupuncture-treated CCI (CCI+EA, n = 10), and electroacupuncture-treated sham (sham+EA, n = 10) group. Randomization was performed by assigning a random number to each rat and using a random number table. The mTBI rat model was established using a controllable cortical impactor. Electroacupuncture therapy was performed on the back of rats, by inserting acupuncture needles to the specific acupoints and setting appropriate parameters for treatment. We evaluated spatial learning and memory functions with the Morris water maze test. We performed quantitative real-time polymerase chain reaction (qRT-PCR), western blotting, adenosine triphosphate (ATP) determination, and mitochondrial respiratory chain complex I (MRCC I) determination on rat hippocampal tissue. We analyzed SIRT-1/PGC-1α expression levels and the results of mitochondrial function assays, and compared differences between groups using bilateral Student’s t-tests. Results:. Compared with the sham group, SIRT-1/PGC-1α expression was downregulated in the hippocampus of CCI group (P

Abstract Background Severe bacterial infections can trigger acute lung injury (ALI) and acute respiratory distress syndrome, with bacterial pathogen‐associated molecular patterns (PAMPs) exacerbating the inflammatory response, particularly in COVID‐19 patients. Cyclic‐di‐GMP (CDG), one of the PAMPs, is synthesized by various Gram‐positve and Gram‐negative bacteria. Previous studies mainly focused on the inflammatory responses triggered by intracellular bacteria‐released CDG. However, how extracellular CDG, which is released by bacterial autolysis or rupture, activates the inflammatory response remains unclear. Methods The interaction between extracellular CDG and myeloid differentiation protein 2 (MD2) was investigated using in vivo and in vitro models. MD2 blockade was achieved using specific inhibitor and genetic knockout mice. Site‐directed mutagenesis, co‐immunoprecipitation, SPR and Bis‐ANS displacement assays were used to identify the potential binding sites of MD2 on CDG. Results Our data show that extracellular CDG directly interacts with MD2, leading to activation of the TLR4 signalling pathway and lung injury. Specific inhibitors or genetic knockout of MD2 in mice significantly alleviated CDG‐induced lung injury. Moreover, isoleucine residues at positions 80 and 94, along with phenylalanine at position 121, are essential for the binding of MD2 to CDG. Conclusion These results reveal that extracellular CDG induces lung injury through direct interaction with MD2 and activation of the TLR4 signalling pathway, providing valuable insights into bacteria‐induced ALI mechanisms and new therapeutic approaches for the treatment of bacterial co‐infection in COVID‐19 patients.

IntroductionPrimary central nervous system lymphoma (PCNSL) is a rare type of non-Hodgkin’s lymphoma that affects brain parenchyma, eyes, cerebrospinal fluid, and spinal cord. Diagnosing PCNSL can be challenging because imaging studies often show similar patterns as other brain tumors, and stereotactic brain lesion biopsy conformation is invasive and not always possible. This study aimed to validate a previous proteomic profiling (PMID: 32610669) of cerebrospinal fluid (CSF) and develop a CSF-based proteomic panel for accurate PCNSL diagnosis and differentiation.MethodsCSF samples were collected from patients of 30 PCNSL, 30 other brain tumors, and 31 tumor-free/benign controls. Liquid chromatography tandem-mass spectrometry targeted proteomics analysis was used to establish CSF-based proteomic panels.ResultsFinal proteomic panels were selected and optimized to diagnose PCNSL from tumor-free controls or other brain tumor lesions with an area under the curve (AUC) of 0.873 (95%CI: 0.723-0.948) and 0.937 (95%CI: 0.807- 0.985), respectively. Pathways analysis showed diagnosis panel features were significantly enriched in pathways related to extracellular matrices-receptor interaction, focal adhesion, and PI3K-Akt signaling, while prion disease, mineral absorption and HIF-1 signaling were significantly enriched with differentiation panel features.DiscussionThis study suggests an accurate clinical test panel for PCNSL diagnosis and differentiation with CSF-based proteomic signatures, which may help overcome the challenges of current diagnostic methods and improve patient outcomes.

Preservation of cultural relics is crucial for cultural sustainability, particularly in the case of cave temples, which are a unique and immovable part of our heritage, nested within mountains. These structures bear immense historical significance and possess considerable economic value, making them vulnerable to criminal activities, notably theft. Establishing a robust physical protection system (PPS) is imperative to safeguard these relics from potential damage. This paper proposes a novel approach for assessing the vulnerability of cave temples' PPS. Based on the fuzzy Petri net (FPN) principle, a comprehensive vulnerability assessment index system was developed within the PPS framework, considering the unique characteristics of cave temples. This study refines the formal definition of the FPN, enhancing its precision and effectiveness for vulnerability assessment. The practicality and effectiveness of the proposed method are verified through simulation experiments. An illustrative example is provided to demonstrate this approach.

Abstract Immune checkpoint inhibitor (ICI) rechallenge in non‐small cell lung cancer (NSCLC) is a promising therapeutic strategy. The situation for ICI rechallenge can be divided into three categories: adverse events (AEs); resistance to ICIs, and rechallenge becomes compulsive because of tumor relapse while the patients had completed a 2 year course of immunotherapy. However, these categories are still controversial and should be explored further. Through voting at the 6th Straits Summit Forum on Lung Cancer, in this study we summarize the consensus of 147 experts in ICI rechallenges. A total of 97.74% experts agreed to rechallenge; 48.87% experts rechallenge with the original drug, and the others rechallenge with a different drug; 40.3% agreed to rechallenge directly after progression; 88.06% experts agreed to ICI rechallenge with a combination regimen; and factors such as previous performance status score, PD‐1 expression, and age should also be considered. Understanding the the clinical studies in ICI rechallenge could bring us one step closer to understanding the consensus. In patients with advanced NSCLC who have suffered recurrent or distant metastasis after immunotherapy, the option of rechallenge with ICIs is a promising treatment option.

Parameter-Efficient Fine-Tuning is widely used to transfer models between different domains. However, for some high-reliability-equipment, the degradation is at a slow rate and continually fluctuates, making it difficult to extract features effectively. Moreover, collecting an integrated source domain for high-reliability-equipment is tough due to the small sample of related datasets. Aiming at the transfer problem of the time-series prediction model, this research proposed an LSTM-fine-tune model, where the parameters of the model are explicitly trained and partly frozen, such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. The algorithm is then benchmarked on sinusoidal functions, where data were randomly generated with different phases and amplitudes. The results show that the LSTM-fine-tune model can learn knowledge from different sinusoidal data and fit a new one quickly with high accuracy. This paper also considers solving two actual problems. One is transferring oxygen concentrator data from the experimental condition to the actual service condition, the results show that the accuracy is largely improved. Moreover, this paper tried to extract more general degradation knowledge from the Wiener process and then transferred it to the degradation data of the oxygen concentrator. The results show that the model quickly achieved higher prediction accuracy with the knowledge. The code and data from the test bed are accessible at https://github.com/panjinxin123/Adaptive-finetuning-in-degradation-time-series-forecasting-via-generating-source-domain.

Abstract Immune checkpoint inhibitors (PD‐1/PD‐L1 and CTLA‐4 blockade) have revolutionized the treatment landscape in non‐small cell lung cancer (NSCLC). Secondary resistance to immunotherapy (IO), which poses a substantial challenge in clinical settings, occurs in several initial responders. Currently, new treatment approaches have been extensively evaluated in investigational studies for these patients to tackle this difficult problem; however, the lack of consistency in clinical definition, uniform criteria for enrollment in clinical trials, and interpretation of results remain significant hurdles to progress. Thus, our expert panel comprehensively synthesized data from current studies to propose a practical clinical definition of secondary resistance to immunotherapy in NSCLC in metastatic and neoadjuvant settings. In addition to patients who received IO alone (including IO‐IO combinations), we also generated a definition for patients treated with chemotherapy plus IO. This consensus aimed to provide guidance for clinical trial design and facilitate future discussions with investigators. It should be noted that additional updates in this consensus are required when new data is available.

The interaction between cis-2 bis(benzofuro)[60]fullerene derivative and NO dominated double gas molecule was studied at the 6–311G(d,p) basis set level by density functional theory B3LYP-D3 method. The geometric structures of six interaction systems were optimized, and the optimized configurations were analyzed by electronic properties, infrared chromatography and independent gradient model to reveal the essence of intermolecular interaction. The results shows that NONO2, NON2, NOCO, NOCO2 and NOHCN are co-adsorbed on the surface of the compound in physical form, and the binding energies are −77.62 kJ/mol, −24.24 kJ/mol, −27.43 kJ/mol, −39.28 kJ/mol and −36.29 kJ/mol, respectively. The van der Waals interaction between gas molecules makes the three molecular interaction system have synergistic adsorption or competitive adsorption effect. Among them, the synergistic effect of NONO2 co-adsorption system is the biggest, and there is a strong attraction between NO and NO2 molecules. Compared with the interaction system of single gas molecule with cis-2 bis(benzofuro) [60]fullerene derivative, the interaction intensity of double gas molecule with the derivative is significantly improved.

Increasingly massive image data is restricted by conditions such as information transmission and reconstruction, and it is increasingly difficult to meet the requirements of speed and integrity in the information age. To solve the urgent problems faced by massive image data in information transmission, this paper proposes a high-quality image compression algorithm based on unsupervised learning. Among them, a content-weighted autoencoder network is proposed to achieve image compression coding on the basis of a smaller bit rate to solve the entropy rate optimization problem. Binary quantizers are used for coding quantization, and importance maps are used to achieve better bit allocation. The compression rate is further controlled and optimized. A multi-scale discriminator suitable for the generative adversarial network image compression framework is designed to solve the problem that the generated compressed image is prone to blurring and distortion. Finally, through training with different weights, the distortion of each scale is minimized, so that the image compression can achieve a higher quality compression and reconstruction effect. The experimental results show that the algorithm model can save the details of the image and greatly compress the memory of the image. Its advantage is that it can expand and compress a large number of images quickly and efficiently and realize the efficient processing of image compression.

By directly measuring multiple molecular features in hundreds to millions of single cells, single-cell techniques allow for comprehensive characterization of the diversity of cells in the heart. These single-cell transcriptome and multi-omic studies are transforming our understanding of heart development and disease. Compared with single-dimensional inspections, the combination of transcriptomes with spatial dimensions and other omics can provide a comprehensive understanding of single-cell functions, microenvironment, dynamic processes, and their interrelationships. In this review, we will introduce the latest advances in cardiac health and disease at single-cell resolution; single-cell detection methods that can be used for transcriptome, genome, epigenome, and proteome analysis; single-cell multi-omics; as well as their future application prospects.

Industrial flue gas and solid waste with the characteristics of large emissions and complicated distributions are two issues for industry, due to that sequential CO2 capture and utilization for flue gas shows high energy-cost penalties and solid waste faces with low reutilization ratio. As a powerful process intensification strategy, chemical looping CO2 capture and in-situ conversion (CL-ICCC) provides an effective solution to handle those two issues. However, current researches on CL-ICCC aim to develop process configurations, design bifunctional materials, and reveal reaction mechanisms, which ignore how to apply CL-ICCC for realizing flue gas purification and solid waste reutilization simultaneously in industry. Herein, after reviewing the current status of CL-ICCC for flue gas upgrade, industrial solid waste reutilization and industrial system integration, a perspective is proposed to highlight CL-ICCC as a promising platform for green and low-carbon industry transition. In the proposed system, industrial flue gas enters the capture reactor and reacts with industrial solid waste derived bifunctional materials to separate CO2 from other impurities. In the conversion reactor, reduction agents are introduced to spill CO2 over bifunctional materials and convert it into value-added chemicals. This review and perspective provide a new pathway to establish a low-carbon and self-digestion industrial system.

Abstract Introduction With advancements in imaging technology, researchers have been able to identify more distinctive imaging features of central serous chorioretinopathy (CSC). However, existing research primarily concentrates on young patients aged 50 years and below, leaving a dearth of studies on elderly CSC patients. Previous studies indicate that elderly CSC patients may exhibit unique imaging characteristics and have a clinical prognosis that significantly differs from younger patients. This study aimed to evaluate the characteristics of retina, choroid structure, and blood flow in elderly patients with chronic CSC (cCSC) examined multimode imaging and try to find new pathogenesis information of it. Methods Using a cut-off age of 50 years, patients with chronic central serous chorioretinopathy were divided into two groups: older and younger. The control group consisted of 40 healthy individuals, with their right eyes assigned. Various clinical features were recorded, including the incidence of ellipsoid zone rupture (EZ-), fibrin in the subretinal fluid (SRF), pachydrusen, subretinal drusenoid deposits (SDD), pigment epithelial detachment (PED), double-layer sign (DLS), and choroidal lipid globule cavern. Measurements were taken for the thickness of the outer nuclear layer (ONL), the length of the extended outer photoreceptor segment (POS), the height and width of SRF, the vascular density of each layer of the retinal capillary plexus, the central macular thickness (CMT), and the subfoveal choroidal thickness (SFCT). Results The proportion of females in the elderly group (43.75%) was significantly higher than that in the youth group (22.41%) (p = 0.034). The degree of hyperopia in the elderly group (1.03 ± 0.73) was higher than that in the youth group (0.26 ± 1.06), with a significant difference in BCVA (p = 0.05). The thickness of SFCT, CMT, ONL in the elderly group, and the length of photoreceptor outer segment in the elderly group were thinner than those in the youth group (p

Abstract Congenital heart disease (CHD) represents a significant contributor to both morbidity and mortality in neonates and children. There’s currently no analogous dried blood spot (DBS) screening for CHD immediately after birth. This study was set to assess the feasibility of using DBS to identify reliable metabolite biomarkers with clinical relevance, with the aim to screen and classify CHD utilizing the DBS. We assembled a cohort of DBS datasets from the California Department of Public Health (CDPH) Biobank, encompassing both normal controls and three pre-defined CHD categories. A DBS-based quantitative metabolomics method was developed using liquid chromatography with tandem mass spectrometry (LC-MS/MS). We conducted a correlation analysis comparing the absolute quantitated metabolite concentration in DBS against the CDPH NBS records to verify the reliability of metabolic profiling. For hydrophilic and hydrophobic metabolites, we executed significant pathway and metabolite analyses respectively. Logistic and LightGBM models were established to aid in CHD discrimination and classification. Consistent and reliable quantification of metabolites were demonstrated in DBS samples stored for up to 15 years. We discerned dysregulated metabolic pathways in CHD patients, including deviations in lipid and energy metabolism, as well as oxidative stress pathways. Furthermore, we identified three metabolites and twelve metabolites as potential biomarkers for CHD assessment and subtypes classifying. This study is the first to confirm the feasibility of validating metabolite profiling results using long-term stored DBS samples. Our findings highlight the potential clinical applications of our DBS-based methods for CHD screening and subtype classification.

Abstract. Background. The outbreak and prevalence of the Omicron variant have threatened human health since March 2022 in mainland China. In this study, we aimed to investigate the clinical characteristics and outcomes of patients with coronavirus disease 2019 (COVID-19) in the Beijing region. Methods. In this retrospective study, we enrolled inpatients admitted for COVID-19 in the Fifth Medical Center of Chinese PLA General Hospital in Beijing between November 10, 2022, and January 30, 2023. Demographic and clinical features and treatment outcomes were comprehensively analyzed. We used logistic regression and linear regression analyses to explore the risk factors associated with disease severity and time of nucleic acid conversion, respectively. Results. A total of 1010 hospitalized patients with COVID-19 were enrolled. The median age was 43.0 years (interquartile range, 28.0–63.0), and patients aged 7 pg/mL, lactic dehydrogenase level >245 U/L, cough, and fever at admission. Age ≥80 years and chronic lung disease were independent risk factors in the nonmild group in elderly patients. In addition, the duration for nucleic acid to turn negative was approximately 5.0 d (interquartile range, 3.0–7.0). Prolonged time of nucleic acid conversion was associated with age ≥60 years, serum urea nitrogen level >8.2 mmol/L, neutrophil count >7 ×109/L, and the presence of a chronic lung disease or carcinoma. Finally, unvaccinated patients accounted for 37.3% of enrolled patients; children and the elder people accounted for approximately half of that. The univariable analysis found that booster doses reduced disease severity and shortened the time of nucleic acid conversion in elderly patients. Conclusions. The outbreak of Omicron rapidly increased the number of patients with COVID-19 in Beijing. In elderly patients, booster doses may reduce disease severity and shorten the time of nucleic acid conversion. Healthcare systems should be optimized before an emerging epidemic outbreak.

Abstract Backgrounds Predicting medications is a crucial task in intelligent healthcare systems, aiding doctors in making informed decisions based on electronic medical records (EMR). However, medication prediction faces challenges due to complex relations within heterogeneous medical data. Existing studies primarily focus on the supervised mining of hierarchical relations between homogeneous codes in medical ontology graphs, such as diagnosis codes. Few studies consider the valuable relations, including synergistic relations between medications, concurrent relations between diseases, and therapeutic relations between medications and diseases from historical EMR. This limitation restricts prediction performance and application scenarios. Methods To address these limitations, we propose KAMPNet, a multi-sourced medical knowledge augmented medication prediction network. KAMPNet captures diverse relations between medical codes using a multi-level graph contrastive learning framework. Firstly, unsupervised graph contrastive learning with a graph attention network encoder captures implicit relations within homogeneous medical codes from the medical ontology graph, generating knowledge augmented medical code embedding vectors. Then, unsupervised graph contrastive learning with a weighted graph convolutional network encoder captures correlative relations between homogeneous or heterogeneous medical codes from the constructed medical codes relation graph, producing relation augmented medical code embedding vectors. Finally, the augmented medical code embedding vectors, along with supervised medical code embedding vectors, are fed into a sequential learning network to capture temporal relations of medical codes and predict medications for patients. Results Experimental results on the public MIMIC-III dataset demonstrate the superior performance of our KAMPNet model over several baseline models, as measured by Jaccard, F1 score, and PR-AUC for medication prediction. Conclusions Our KAMPNet model can effectively capture the valuable relations between medical codes inherent in multi-sourced medical knowledge using the proposed multi-level graph contrastive learning framework. Moreover, The multi-channel sequence learning network facilitates capturing temporal relations between medical codes, enabling comprehensive patient representations for downstream tasks such as medication prediction.

Abstract Motivation Ovarian cancer (OC) is a highly lethal gynecological malignancy. Extensive research has shown that OC cells undergo significant metabolic alterations during tumorigenesis. In this study, we aim to leverage these metabolic changes as potential biomarkers for assessing ovarian cancer. Methods A functional module-based approach was utilized to identify key gene expression pathways that distinguish different stages of ovarian cancer (OC) within a tissue biopsy cohort. This cohort consisted of control samples (n = 79), stage I/II samples (n = 280), and stage III/IV samples (n = 1016). To further explore these altered molecular pathways, minimal spanning tree (MST) analysis was applied, leading to the formulation of metabolic biomarker hypotheses for OC liquid biopsy. To validate, a multiple reaction monitoring (MRM) based quantitative LCMS/MS method was developed. This method allowed for the precise quantification of targeted metabolite biomarkers using an OC blood cohort comprising control samples (n = 464), benign samples (n = 3), and OC samples (n = 13). Results Eleven functional modules were identified as significant differentiators (false discovery rate, FDR

Peripheral nerve injury can lead to progressive muscle atrophy and poor motor function recovery, which is a difficult point of treatment, and the mechanism needs to be further explored. In previous studies, we found that miR-142a-3p was significantly upregulated and persistently highly expressed in denervated mouse skeletal muscle. Here, we show that overexpression of miR-142a-3p inhibited the growth and differentiation of C2C12 myoblast, while knockdown of miR-142a-3p had a promoting effect. In vitro, knockdown of miR-142a-3p in denervated mouse skeletal muscle effectively increased proliferating muscle satellite cells and ameliorated muscle atrophy. Mechanistically, the myoregulator Mef2a was proved to be an important downstream target of miR-142a-3p, and miR-142a-3p regulates skeletal muscle differentiation and regeneration by inhibiting the expression of Mef2a. The co-knockdown of Mef2a and miR-142a-3p effectively alleviated or offset the biological effects of miR-142a-3p knockdown. In conclusion, our data revealed that miR-142a-3p regulates neurogenic skeletal muscle atrophy by targeting Mef2a.

This review is a comprehensive overview of recent advancements in underwater in situ heavy metal voltammetric analyzers (UIHVAs). It explores various types of in situ voltammetric analyzers, including the voltammetric in situ profiling system, submersible integrated multi-channel trace metal sensing probes, vibrating gold microwire electrode voltammetric analyzers, and electrochemical analyzers designed for on-site flow measurements. It also covers electrochemical sensors based on flexible liquid crystal polymers, deep-sea mercury sensors, and other in situ electrochemical analyzers. This review systematically examines the research and development progress of microelectrode arrays, screen-printed, carbon, bismuth, antimony, and lab-on-a-chip electrodes. The final section looks at key trends in the research and development of voltammetric analyzers, highlighting the exploration of novel working electrodes, the integration of smart monitoring and data analysis technologies, and the promotion of interdisciplinary collaboration and innovation. From a global perspective, in situ heavy metal voltammetric analysis technology has demonstrated significant applicability in various fields, such as environmental monitoring, marine science, and biogeochemistry. This technology holds considerable potential for further development. However, extensive research and continuous improvement are required to improve detection performance. We are convinced that with continued technological advances and dedicated research efforts, these challenges can be overcome and will pave the way for the widespread application of UIHVAs.

Multivariate time series modeling has been essential in sensor-based data mining tasks. However, capturing complex dynamics caused by intra-variable (temporal) and inter-variable (spatial) relationships while simultaneously taking into account evolving data distributions is a non-trivial task, which faces accumulated computational overhead and multiple temporal patterns or distribution modes. Most existing methods focus on the former direction without adaptive task-specific learning ability. To this end, we developed a holistic spatial-temporal meta-learning probabilistic inference framework, entitled ST-MeLaPI, for the efficient and versatile learning of complex dynamics. Specifically, first, a multivariate relationship recognition module is utilized to learn task-specific inter-variable dependencies. Then, a multiview meta-learning and probabilistic inference strategy was designed to learn shared parameters while enabling the fast and flexible learning of task-specific parameters for different batches. At the core are spatial dependency-oriented and temporal pattern-oriented meta-learning approximate probabilistic inference modules, which can quickly adapt to changing environments via stochastic neurons at each timestamp. Finally, a gated aggregation scheme is leveraged to realize appropriate information selection for the generative style prediction. We benchmarked our approach against state-of-the-art methods with real-world data. The experimental results demonstrate the superiority of our approach over the baselines.

The football team training algorithm (FTTA) is a new metaheuristic algorithm that was proposed in 2024. The FTTA has better performance but faces challenges such as poor convergence accuracy and ease of falling into local optimality due to limitations such as referring too much to the optimal individual for updating and insufficient perturbation of the optimal agent. To address these concerns, this paper presents an improved football team training algorithm called IFTTA. To enhance the exploration ability in the collective training phase, this paper proposes the fitness distance-balanced collective training strategy. This enables the players to train more rationally in the collective training phase and balances the exploration and exploitation capabilities of the algorithm. To further perturb the optimal agent in FTTA, a non-monopoly extra training strategy is designed to enhance the ability to get rid of the local optimum. In addition, a population restart strategy is then designed to boost the convergence accuracy and population diversity of the algorithm. In this paper, we validate the performance of IFTTA and FTTA as well as six comparison algorithms in CEC2017 test suites. The experimental results show that IFTTA has strong optimization performance. Moreover, several engineering-constrained optimization problems confirm the potential of IFTTA to solve real-world optimization problems.

EEG signals capture information through multi-channel electrodes and hold promising prospects for human emotion recognition. However, the presence of high levels of noise and the diverse nature of EEG signals pose significant challenges, leading to potential overfitting issues that further complicate the extraction of meaningful information. To address this issue, we propose a Granger causal-based spatial–temporal contrastive learning framework, which significantly enhances the ability to capture EEG signal information by modeling rich spatial–temporal relationships. Specifically, in the spatial dimension, we employ a sampling strategy to select positive sample pairs from individuals watching the same video. Subsequently, a Granger causality test is utilized to enhance graph data and construct potential causality for each channel. Finally, a residual graph convolutional neural network is employed to extract features from EEG signals and compute spatial contrast loss. In the temporal dimension, we first apply a frequency domain noise reduction module for data enhancement on each time series. Then, we introduce the Granger–Former model to capture time domain representation and calculate the time contrast loss. We conduct extensive experiments on two publicly available sentiment recognition datasets (DEAP and SEED), achieving 1.65% improvement of the DEAP dataset and 1.55% improvement of the SEED dataset compared to state-of-the-art unsupervised models. Our method outperforms benchmark methods in terms of prediction accuracy as well as interpretability.

3,4-Dinitrofurazanfuroxan (DNTF), as a high-energy-density material, features good thermal stability and wide applications. This study aimed to elucidate the thermal decomposition mechanism of DNTF combined with nitrogen-rich compounds containing N–H. The thermal stabilities of DNTF and its hybrid systems were investigated using differential thermal analysis/thermogravimetry (TG), vacuum stability test, and accelerating rate calorimetry under isothermal, non-isothermal, and adiabatic conditions, respectively. Results showed that the thermal stability and thermal safety of DNTF significantly decreased after combining with nitrogen-rich compounds containing N–H. Calculation results showed that the activation energy of the DNTF hybrid systems was significantly lower than that of DNTF. The TG-IR was used to monitor the generation of fugitive gases during the thermal decomposition of the DNTF/5-aminotetrazole (5-ATZ) hybrid. Moreover, the nitrogen-rich molecules containing N–H interacted extensively with DNTF, and this interaction accelerated the thermal degradation of DNTF.

Abstract The authors focus on the makeup transformation problem, which refers to the transfer of makeup from a reference face to a source face image while maintaining the source makeup‐free face image. In recent years, makeup transformation has become a hot issue and a lot of research has been conducted on this basis, but there are some limitations in the existing methods, mainly due to the lack of consideration of age factor, which makes the final generated face makeup images appear not natural and lack appearance attractiveness. In order to further solve this problem, an age‐compensated makeup transformation framework based on homology continuity is proposed. In order to achieve a stable and controllable age‐compensation effect, the authors design a new coding module that can map the face makeup semantic vector into the higher feature space and achieve age compensation by adjusting the direction of the semantic vector. Finally, in order to comprehensively evaluate the effectiveness of the authors’ proposed method, a large number of qualitative and quantitative experiments have been conducted, and the experimental results show that the authors’ proposed framework outperforms existing methods.

AIM:To evaluate the correlation between axial lengths and anterior segment parameters using swept-source optical coherence tomography(SS-OCT).METHODS:For the cross-sectional clinical study, a total of 109 adult volunteers with different degrees of myopia recruited from January 1, 2022, to March 31, 2022, at the ophthalmology clinic of the First Affiliated Hospital of Zhengzhou University were included. Participants were divided into 4 groups based on axial length(AL): group A(AL≤24.0mm), group B(24.0mm26.0mm). Anterior segment examinations were performed using SS-OCT, including: central corneal thickness(CCT), lens thickness(LT), anterior chamber depth(ACD), anterior chamber width(ACW), angle opening distance(AOD500), angle recess area(ARA500), trabecular iris space area(TISA500), trabecular iris angle(TIA500), crystalline lens rise(CLR). The relationships between these data and AL, spherical equivalent(SE)were analyzed.RESULTS:There was no difference in the comparison of CCT among the four groups(P>0.05). There were differences in SE, LT, ACD, ACW, AOD500, ARA500, TISA500, TIA500 and CLR among the four groups(all P

A series of fullerene anisole derivative stabilizers was synthesized by nucleophilic substitution reaction using hexachlorofullerene and benzyl alcohol as raw materials to extend the service duration of nitrocellulose (NC)-based propellants. Single-crystal X-ray diffraction, nuclear magnetic resonance, high-resolution mass spectrometry, Fourier transform infrared (FT-IR) spectroscopy, and UV–Vis spectroscopy were used to characterize the structures of the synthesized fullerene anisole derivative stabilizers. Methyl violet, differential scanning calorimetry test, isothermal weight loss, vacuum stability test, and adiabatic accelerated test were used to study their compatibility with NC and their ability to stabilize NC. The results show that the designed and synthesized novel fullerene anisole derivative stabilizer has good compatibility with NC, and their overall stabilizing effects on NC are better than those of the traditional stabilizers, diphenylamine (DPA), and N,N′-dimethyl-N,N′-diphenylurea (C2). The stabilizing effects was ranked as: 3b > 2d > 2a > 2c > C2 > 2b > DPA > NC. In addition, FT-IR analysis and electron spin resonance spectroscopy were applied to explore the stability mechanism of fullerene-based stabilizers to NC. The results reveal that the new fullerene stabilizer can adsorb and effectively eliminate the nitrogen oxide free radicals generated by NC degradation; therefore, it can forbid the autocatalytic degradation of NC and stabilize NC.

Interictal epileptiform discharges (IEDs) often co-occur across spatially-separated cortical regions, forming IED networks. However, the factors prompting IED propagation remain unelucidated. We hypothesized that slow oscillations (SOs) might facilitate IED propagation. Here, the amplitude and phase synchronization of SOs preceding propagating and non-propagating IEDs were compared in 22 patients with focal epilepsy undergoing intracranial electroencephalography (EEG) evaluation. Intracranial channels were categorized into the irritative zone (IZ) and normal zone (NOZ) regarding the presence of IEDs. During wakefulness, we found that pre-IED SOs within the IZ exhibited higher amplitudes for propagating IEDs than non-propagating IEDs (delta band: p = 0.001, theta band: p

Toll-like receptors (TLRs) belong to a germline-encoded protein family. These are pattern recognition receptors. They sense pathogen-associated molecular patterns (PAMPs). When this occurs, activation of the NF-ĸB pathway follows. This triggers the innate immune response of the host. The consequent inflammatory cytokine response usually contributes to the elimination of the pathogen. Activation of TLRs also induces an adaptive immune response by a cross-prime mechanism. This mechanism is employed in cancer immunotherapy. Using TLR ligands as adjuvants induces upregulation of costimulatory signals which in turn activates a cytotoxic leukocyte response against cancer cells. However, TLRs are also overexpressed in human cancer cells resulting in increased cell proliferation, migration, invasion, and angiogenesis. An intracellular adaptor, myeloid differentiation factor 88 (MyD88) probably mediates this process. MyD88 is intimately involved with all TLRs except TLR3. One consequence of the interaction between a TLR and MyD88 is activation of NF-ĸB. In this context of a variety of proinflammtory cytokines being produced, chronic inflammation may result. Inflammation is an important protective mechanism. However, chronic inflammation is also involved in carcinogenesis. Activation of NF-ĸB inhibits apoptosis and under certain circumstances, tumor cell survival. In this review, the potential therapeutic value of TLRs in immunotherapy and its role in oncogenesis are explored. The emerging use of artificial intelligence is mentioned.

ObjectivesWe aim to investigate the functional profiles of perilesional gray matter (GM) in epileptic patients with focal cortical dysplasia (FCD) and to correlate these profiles with FCD II subtypes, surgical outcomes, and different antiseizure medications (ASMs) treatment response patterns.MethodsNine patients with drug-responsive epilepsy and 30 patients with drug-resistant epilepsy (11 were histologically confirmed FCD type IIa, 19 were FCD type IIb) were included. Individual-specific perilesional GM and contralateral homotopic GM layer masks were generated. These masks underwent a two-voxel (2 mm) dilation from the FCD lesion and contralateral homotopic region, resulting in 10 GM layers (20 mm). Layer 1, the innermost, progressed to Layer 10, the outermost. Amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo) analyses were conducted to assess the functional characteristics of ipsilateral perilesional GM and contralateral homotopic GM.ResultsCompared to the contralateral homotopic GM, a significant reduction of ALFF was detected at ipsilateral perilesional GM layer 1 to 6 in FCD type IIa (after Bonferroni correction p

Cholangiocarcinoma (CCA) is an aggressive biliary epithelial tumor with limited therapeutic options and poor prognosis. Curcumin is a promising active natural compound with several anti-cancer properties, though its clinical uses remain hindered due to its poor bioavailability. We recently synthesized curcumin analogs with multifunctional pharmacological and bioactivities with enhanced bioavailability. Among these novel curcumin analogs, WZ26 is a representative molecule. However, the anti-tumor effect of WZ26 against CCA is unclear. In this study, we evaluated the anti-tumor effect of WZ26 in both CCA cells and CCA xenograft mouse model. The underlying molecular anti-cancer mechanism of WZ26 was also studied. Our results show that WZ26 significantly inhibited cell growth and induced mitochondrial apoptosis in CCA cell lines, leading to significant inhibition of tumor growth in xenograft tumor mouse model. Treatment of WZ26 increased reactive oxygen species (ROS) generation, subsequently decreased mitochondrial membrane potential and inhibited the phosphorylation of signal transducer and activator of transcription 3 (STAT3), thereby inducing G2/M cell cycle arrest and cell apoptosis. Pretreatment of N-acetyl cysteine (NAC), an antioxidant agent, could fully reverse the WZ26-induced ROS-mediated changes in CCA cells. Our findings provide experimental evidence that curcumin analog WZ26 could be a potential candidate against CCA via enhancing ROS induction and inhibition of STAT3 activation.

Abstract High strength aluminum alloys are widely used but their strength is reduced as nano-precipitates coarsen rapidly in medium and high temperatures, which greatly limits their application. Single solute segregation layers at precipitate/matrix interfaces are not satisfactory in stabilizing precipitates. Here we obtain multiple interface structures in an Al-Cu-Mg-Ag-Si-Sc alloy including Sc segregation layers, C and L phases as well as a newly discovered χ-AgMg phase, which partially cover the θ′ precipitates. By atomic resolution characterizations and ab initio calculations, such interface structures have been confirmed to synergistically retard coarsening of precipitates. Therefore, the designed alloy shows the good combination of heat resistance and strength among all series of Al alloys, with 97% yield strength retained after thermal exposure, which is as high as 400 MPa. This concept of covering precipitates with multiple interface phases and segregation layers provides an effective strategy for designing other heat resistant materials.

Abstract Objectives This research aimed to explore the relationship between pre‐treatment inflammatory markers and other clinical characteristics and the survival of small‐cell lung cancer (SCLC) patients who received first‐line platinum‐based treatment and to construct nomograms for predicting overall survival (OS) and progression‐free survival (PFS). Methods A total of 612 patients diagnosed with SCLC between March 2008 and August 2021 were randomly divided into two cohorts: a training cohort (n = 459) and a validation cohort (n = 153). Inflammatory markers, clinicopathological factors, and follow‐up information of patients were collected for each case. Cox regression was used to conduct univariate and multivariate analyses and the independent prognostic factors were adopted to develop the nomograms. Harrell's concordance index (C‐index) and time‐dependent receiver operating characteristic curve were used to verify model differentiation, calibration curve was used to verify consistency, and decision curve analysis was used to verify the clinical application value. Results Our results showed that baseline C‐reactive protein/albumin ratio, neutrophil/lymphocyte ratio, NSE level, hyponatremia, the efficacy of first‐line chemotherapy, and stage were independent prognostic factors for both OS and PFS in SCLC. In the training cohort, the C‐index of PFS and OS was 0.698 and 0.666, respectively. In the validation cohort, the C‐index of PFS and OS was 0.727 and 0.747, respectively. The nomograms showed good predictability and high clinical value. Also, our new clinical models were superior to the US Veterans Administration Lung Study Group (VALG) staging for predicting the prognosis of SCLC. Conclusions The two prognostic nomograms of SCLC including inflammatory markers, VALG stage, and other clinicopathological factors had good predictive value and could individually assess the survival of patients.

Deconvolution of potential drug targets of the central nervous system (CNS) is particularly challenging because of the complicated structure and function of the brain. Here, a spatiotemporally resolved metabolomics and isotope tracing strategy was proposed and demonstrated to be powerful for deconvoluting and localizing potential targets of CNS drugs by using ambient mass spectrometry imaging. This strategy can map various substances including exogenous drugs, isotopically labeled metabolites, and various types of endogenous metabolites in the brain tissue sections to illustrate their microregional distribution pattern in the brain and locate drug action-related metabolic nodes and pathways. The strategy revealed that the sedative-hypnotic drug candidate YZG-331 was prominently distributed in the pineal gland and entered the thalamus and hypothalamus in relatively small amounts, and can increase glutamate decarboxylase activity to elevate γ-aminobutyric acid (GABA) levels in the hypothalamus, agonize organic cation transporter 3 to release extracellular histamine into peripheral circulation. These findings emphasize the promising capability of spatiotemporally resolved metabolomics and isotope tracing to help elucidate the multiple targets and the mechanisms of action of CNS drugs.

Abstract To evaluate the characteristics of violent injury cases in Sichuan Province, China. Overall, 4866 violent injury cases in Sichuan province, China from 2014 to 2017 were included. The injury evaluation report was used to classify and summarize the injury information, case and injury characteristics, and to describe the characteristics for each risk factor. The majority of cases were males (n = 3851, 79.14%), aged 20–60 (n = 3867, 79.47%), and living in rural areas (n = 3094, 65.55%). Many cases occurred in public areas (n = 3351, 74.19%) and in the evening (n = 1005, 29.49%). Passion was the main motive for the violent injuries (n = 2098, 82.11%) and the main types of injuries were those to the brain, face, and auricula (n = 3075, 63.21%). Blunt instruments (n = 2951, 64.86%) were most commonly used to inflict injury, and the injury evaluation determined that the majority of injuries were simple (n = 2669, 54.85%) and slight (n = 1685, 34.63%). For cases resulting from passion and money, blunt instruments were more commonly used, while sharp instruments were more commonly used for injuries resulting from emotion and revenge (p