Explore the vast range of titles available.

Background Social alienation is prevalent among lung cancer (LC) patients undergoing chemotherapy. Although previous research has established an association between social alienation and symptom burden, the mechanisms underlying this relationship remain not fully understood. Methods This cross-sectional study used convenience sampling to select 378 LC patients undergoing chemotherapy from September 2022 to December 2023 at a tertiary care hospital in Guangzhou, China. Data collection was conducted using a proprietary Sociodemographic Information Questionnaire, MD Anderson Symptom Inventory (MDASI), Social Alienation Questionnaire, Perceptions of Social Support (PSS) Questionnaire, and Positive Psychological Capital (PPC) Questionnaire. To analyze chain-mediated effects, the PROCESS v3.3 Model 6 SPSS macro software was employed. Results The analysis revealed that the burden of symptoms significantly exacerbates social alienation, as evidenced in the mediation effects model (Bootstrap 95% CI: 0.031, 0.092). Furthermore, the burden of symptoms indirectly diminishes PSS (Bootstrap 95% CI: 0.019, 0.057) and PPC (Bootstrap 95% CI: 0.002, 0.020). It is crucial to note that both PSS and PPC significantly mediate the relationship between symptom burden and social alienation, as evidenced (Bootstrap 95% CI: 0.001, 0.011). Conclusions The impact of symptom burden on social alienation is moderated through PSS and PPC, manifesting both directly and indirectly. Moreover, the influence of PPC tends to mitigate the mediating role of PSS. Clinical interventions aimed at bolstering PSS and augmenting PPC may potentially alleviate social alienation and enhance the quality of life for patients undergoing chemotherapy for LC.

With the advancement of scientific research, understanding the physical parameters governing acoustic wave propagation in sea ice has become increasingly important. Among these parameters, shear wave velocity plays a crucial role. However, as measurements progressed, it became apparent that there was a large discrepancy between measured values of shear waves and predictions based on empirical formulas or existing models. These inconsistencies stem primarily from the complex internal structure of natural sea ice, which significantly influences its physical behavior. Research reveals that shear wave velocity is not only influenced by bulk properties such as density, temperature, and stress state but is also sensitive to microstructural features, including air bubbles, inclusions, and ice crystal orientation. Compared to longitudinal wave velocity, the characterization of shear wave velocity is far more challenging due to these inherent complexities, underscoring the need for more precise measurement and modeling techniques. To address the challenges posed by the complex internal structure of natural sea ice and improve prediction accuracy, this study introduces a novel, integrated approach combining simulation, measurement, and inversion intelligent learning model. First, a laboratory-based method for generating sea ice layers under controlled formation conditions is developed. The produced sea ice layers align closely with measured values for Poisson’s ratio, multi-year sea ice density, and uniaxial compression modulus, particularly in the high-temperature range. Second, enhancements to shear wave velocity measurement equipment have been implemented. The improved device achieves measurement accuracy exceeding 1%, offers portability, and meets the demands of high-precision experiments conducted in harsh polar environments. Finally, according to the characteristics of small sample data. The ANN neural network was improved to a deep residual neural network with the addition of Squeeze-and-Excitation Attention (SE-ResNet) to predict longitudinal and transverse wave velocities. This prediction method improves the accuracy of shear and longitudinal wave velocity prediction by 24.87% and 39.59%, respectively, compared to the ANN neural network.

Direct non-oxidative dehydrogenation of alkanes produces useful carbon feedstocks and hydrogen fuel. However, breaking the C–H bonds in alkanes typically requires high temperature, stoichiometric oxidants or high-energy ultraviolet light; processes that operate under milder conditions are attractive but tend to have poor efficiency. Here we report Pt/black TiO 2 photocatalysts in which Pt species are close to each other but not directly bonded, exhibiting high performance for alkane dehydrogenation in visible to near-infrared light at room temperature. For cyclohexane dehydrogenation, the turnover number for H 2 production exceeded 100,000 without any deactivation over 80 reaction cycles, far beyond thermal reactions. For methane, 8.2% conversion was achieved with 65% selectivity to propane, rather than the more common ethane. We propose that methane undergoes intramolecular dehydrogenation to produce a methylene intermediate. For C2+ alkanes, fast dehydrogenation (up to 1,440 µmol g −1  h −1 ) to the corresponding olefins was realized. Distinct from isolated Pt + monomers, the collections of Pt + monomers give better photocatalytic activity and selectivity. Dehydrogenation of alkanes produces hydrogen and useful carbon molecules but typically requires harsh conditions to operate effectively. Here the authors show that Pt/TiO 2 photocatalysts where Pt atoms are isolated from, yet still close to, one another are promising for visible-light-driven alkane dehydrogenation.

Numerous spectral indices have been developed to assess plant diversity. However, since they are developed in different areas and vegetation type, it is difficult to make a comprehensive comparison among these indices. The primary objective of this study was to explore the optimum spectral indices that can predict plant species richness across different communities in sandy grassland. We use 7339 spectral indices (7217 we developed and 122 that were extracted from literature) to predict plant richness using a two-year dataset of plant species and spectra information at 270 plots. For this analysis, we employed cluster analysis, correlation analysis, and stepwise linear regression. The spectral variability within the 420–480 nm and 760–900 nm ranges, the first derivative value at the sensitive bands, and the normalized difference at narrow spectral ranges correlated well with plant species richness. Within the 7339 indices that were investigated, the first-order derivative values at 606 and 583 nm, the reflectance combinations on red bands: (R802 − R465)/(R802 + R681) and (R750 − R550)/(R750 + R550) showed a stable performance in both the independent calibration and validation datasets (R2 > 0.27, p < 0.001, RMSE < 1.7). They can be regarded as the best spectral indices to estimate plant species richness in sandy grasslands. In addition to these spectral variation indices, the first derivative values or the normalized difference of the sensitive bands also reflect plant diversity. These results can help to improve the estimation of plant diversity using satellite-based airborne and hand-held hyperspectral sensors.

Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) approaches and computational modeling. In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS assays. The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.

In taxonomy, qualitative methods are commonly used to analyze morphological characters, which can lead to dramatic changes in higher taxa. Geometric morphometrics (GM) has proven to be useful for discriminating species in various taxonomy groups. However, the application of geometric morphometrics in supraspecies classification is relatively scarce. In this study, we tested the controversial two subgenera classification of Chaetocnema with geometric morphometrics; a total of 203 Chaetocnema species representing 50% of all known species from around the world were selected for the analysis. We analyzed the shape of the pronotum, elytron, head, aedeagus, and spermatheca. The results showed that the two traditional subgenera distinctly differed from each other; therefore, we propose using two subgenera arrangements to facilitate the understanding and taxonomy of the Chaetocnema species (especially in the Oriental, Palearctic, and Nearctic regions). Additionally, the morphological diversity of the abovementioned structures of Chaetocnema was analyzed, and it was found that the highest morphological diversity was in the spermatheca, which was greater than that in the aedeagus. Our research demonstrated that GM could be useful for detecting morphological delimitation of the supraspecies taxa. It also showed that GM methods are applicable to insects as small as 2 mm in body size.

BackgroundAndrogen sensitivity, which was established as the leading etiology of androgenetic alopecia (AGA) and benign prostatic hyperplasia (BPH), plays an important role in SARS-CoV-2 infection. Vaccination is essential for AGA and BPH patients in view of the high risk from SARS-CoV-2 infection.PurposeWe aimed to investigate the associated factors for SARS-CoV-2 vaccination and its side effects in populations with AGA and BPH.MethodWe collected the data on SARS-CoV-2 vaccination and adverse reactions of male AGA and BPH patients visited the outpatient of Xiangya hospital by telephone and web-based questionnaires. Vaccination rate and adverse reactions were compared by different vaccine types and use of anti-androgen therapy.ResultA total of 457 AGA patients and 397 BPH patients were recruited in this study. Among which, 92.8% AGA patients and 61.0% BPH patients had at least the first dose of SARS-CoV-2 vaccination (p < 0.001). Having comorbidities and use of anti-androgen therapy increased the risk of un-vaccination among AGA by 2.875 and 3.729 times, respectively (p < 0.001). Around 31.1% AGA patients and 9.5% BPH patients presented adverse reactions, which were mostly mild. Anti-androgen therapy increased the inclination of injection site pain after vaccination (18.7% vs 11.9%; OR: 1.708, 95% CI: 1.088-2.683, p = 0.019).ConclusionCo-existence of other systemic diseases and anti-androgen therapy were the limiting factors for SARS-CoV-2 unvaccination, especially in AGA patients. The importance of SARS-CoV-2 vaccines should be strengthened and popularized in androgen sensitive phenotypes.

Heterotaxy polysplenia syndrome is a rare condition characterized by multiple abnormal spleens and irregular placement of various organs. Some patients have been documented as developing various types of cancers, although the association with heterotaxy polysplenia syndrome remains uncertain. Most cases of heterotaxy polysplenia syndrome are isolated anomalies, but there are rare instances where it appears in closely related individuals, suggesting possible genetic inheritance. We report the case of a 36-year-old woman with heterotaxy polysplenia syndrome, who presented with choledocholithiasis, thickening and stenosis of the hepatic portal bile duct wall, duodenal atresia, and congenital pancreatic insufficiency. Following recovery from a COVID-19 infection, the patient incidentally identified a mass in the upper abdomen. Pathological examination revealed the mass to be a poorly differentiated adenocarcinoma of pancreaticobiliary origin. Additionally, a familial pattern of situs inversus and breast cancer was observed. In this report, we also provide a comprehensive literature review to enhance the understanding of heterotaxy polysplenia syndrome and to explore the potential association between this condition and tumor development. Our report contributes to the current knowledge about heterotaxy polysplenia syndrome and its potential association with tumors. Effective integration of clinical diagnostic information, exploration of precise means of diagnosing and treating rare diseases, and incorporation of the patient's familial background are promising for future research and clinical interventions.

ObjectiveRecently, a new subtype of granzyme B (GrB)-producing Breg cells has been identified, which was proven to be involved in autoimmune disease. Our recent report demonstrated that GrB-producing Breg cells were correlated with clinical and immunological features of SLE. However, the effect of GrB-producing Breg cells in lupus mice is unclear.MethodsGrB expression in naïve and lupus mouse B cells was analysed using flow cytometry, PCR, ELISA and ELISpot assays. To study the role of GrB-producing B cells in a lupus model, GrB knockout (KO) and wild-type (WT) mice were intraperitoneally injected with monoclonal cells from the mutant mouse strain B6.C-H-2bm12 (bm12) for 2 weeks. In addition, the function of GrB-producing Breg cells in naïve and lupus mice was further explored using in vitro B cells-CD4+CD25− T cell co-culture assays with GrB blockade/KO of B cells.ResultsB cells from the spleens of WT C57BL/6 (B6) mice could express and secret GrB (p<0.001). GrB-producing Breg cells from WT mice showed their regulatory functions on CD4+CD25− T cell. While the frequency of GrB-producing Breg cells was significantly decreased (p=0.001) in lupus mice (p<0.001). Moreover, GrB-producing Breg cells in lupus mice failed to suppress T cell-mediated proinflammatory responses, partially due to the impaired capacity of downregulating the T cell receptor-zeta chain and inducing CD4+CD25− T cell apoptosis.ConclusionThis study further revealed the function and mechanism of GrB-producing Breg cells in regulating T cell homeostasis in lupus mice and highlighted GrB-producing Breg cells as a therapeutic target in SLE.

Dual-wavelength output devices have a wide range of applications in mid-infrared band difference frequency generation, anti-interference lidar, dual-wavelength holographic interferometry, and other applications. Vertical external cavity surface-emitting lasers (VECSELs) are a type of semiconductor laser that can achieve single-chip dual-wavelength output by designing the chip structure. In this paper, we present a single-chip VECSEL that can switch between dual-wavelength and single-wavelength output modes. The VECSEL can simultaneously emit coaxial laser beams at 967 nm and 1013 nm, with a wavelength spacing of about 45 nm. The degree of mismatch between the gain peaks of the two quantum wells in the gain chip and the corresponding cavity modes is different. By adjusting the pump power, the temperature of the active region can be changed, which alters the matching relationship between the gain peaks and the cavity modes and controls the output mode of the VECSEL. The dual-wavelength output mode maintains a stable wavelength spacing at different operating temperatures. The laser output mode can be switched between single-wavelength and dual-wavelength, and the beam divergence angle is less than 8°. The dual-wavelength output power can exceed 400 mW, and the long-wavelength output power can reach up to 700 mW.