A Fast-scanning Protocol of Optical Coherence Tomography Angiography Based on Structure-Aware Attention.

Oral diseases have imposed a heavy social and financial burden on many countries and regions. If left untreated, severe cases can lead to malignant tumours. Common devices can no longer meet the high-resolution and non-invasive requirement, while Optical Coherence Tomography Angiography (OCTA) provides an ideal perspective for detecting vascular microcirculation. However, acquiring high-quality OCTA images takes time and can result in unpredictable motion artefacts. Therefore, we propose a systematic workflow for rapid OCTA data acquisition. Initially, we implement a fourfold reduction in sampling points to enhance the scanning speed. Then, we apply a deep neural network for rapid image reconstruction, elevating the resolution to the level achieved through full scanning. Specifically, it is a hybrid attention model with a structure-aware loss to extract local and global information on angiography, which improves the visualisation performance and quantitative metrics of numerous classical and recent-presented models by 3.536%-9.943% in SSIM and 0.930%-2.946% in MS-SSIM. Through this approach, the time of constructing one OCTA volume can be reduced from nearly 30 s to about 3 s. The rapid-scanning protocol of high-quality imaging also presents feasibility for future real-time detection applications.

The effect of probiotics supplementation on cancer-treatment complications: a critical umbrella review of interventional meta-analyses.

Cancer-related complications pose significant challenges in the management and treatment of patients with malignancies. Several meta-analyses have indicated improving effects of probiotics on cancer complications, while some studies have reported contentious findings. The purpose of the present study was to evaluate the efficacy of probiotics in addressing cancer complications, including diarrhea, mucositis, and infections, following chemotherapy, radiotherapy, and surgery. Relevant studies were searched in the PubMed, Scopus, Embase and Web of Science databases and Google Scholar up to September 2023. All meta-analyses addressing the effects of probiotics on all cancer treatments-induced complications including infection, diarrhea and oral mucositis were included. The pooled results were calculated using a random-effects model. Analyses of subgroups, sensitivity and publication bias were also conducted. The results revealed that the probiotics supplementation was effective on reduction of total cancer complications (OR:0.53; 95% CI: 0.44, 0.62, p < 0.001; I2=79.0%, p < 0.001), total infection rate (OR:0.47; 95%CI: 0.41, 0.52, p < 0.001; I2= 48.8%, p < 0.001); diarrhea (OR:0.50; 95%CI: 0.44, 0.57, p < 0.001; I2=44.4%, p = 0.023) and severe diarrhea (OR: 0.4; 95%CI: 0.27, 0.56, p < 0.001; I2=31.3%, p = 0.178), oral mucositis (OR: 0.76; 95%CI: 0.58, 0.94, p < 0.001; I2=95.5%, p < 0.001) and severe oral mucositis (OR:0.65, 95%CI: 0.58, 0.72 p < 0.001; I2=22.1%, p = 0.274). Multi strain probiotic (OR:0.49; 95%CI: 0.32, 0.65, p < 0.001; I2=90.7%, p < 0.001) were more efficacious than single strain (OR:0.73; 95%CI: 0.66, 0.81, p < 0.001; I2=0.00%, p = 0.786). The findings of the current umbrella meta-analysis provide strong evidence that probiotic supplementation can reduce cancer complications.

Evaluation of urban-rural difference in breast cancer mortality among Chinese women during 1987-2021: A hierarchical age-period-cohort analysis.

BACKGROUND: Breast cancer mortality varies in urban and rural areas in China. Studies have reported urban-rural difference across time period, however, the evaluation on urban-rural differences in age and birth cohort effects is limited. Our aim was to quantitatively assess urban-rural disparities in age, period and cohort effects in breast cancer mortality in China. METHODS: We collected age-specific breast cancer mortality rates for urban and rural females aged 20-84 years from 1987 to 2021. Hierarchical age-period-cohort (HAPC) models were used to evaluate the effect of area (urban, rural) on breast cancer mortality and investigate urban-rural differences in age, time period and birth cohort effects. RESULTS: We found a significant area (urban, rural) effect on breast cancer mortality in that rural females had a lower mortality risk than urban females [-0.25 (95 % confidence interval (CI): -0.32, -0.17)]. Age trajectories of mortality based on the HAPC model showed nonlinear trends with adjustment for area variable. The urban-rural difference in age effect appeared to be divergent with age, and urban women had higher mortality risk in the senior age group. The urban-rural difference in birth cohort effect indicated a reversal around the birth cohort group of 1962-1966, after which rural females had a higher mortality risk than urban females. CONCLUSION: The area (urban, rural) could affect breast cancer mortality among women, and the effect of urban-rural difference varies with age and birth cohort. To promote the health of urban and rural females, the gap between urban and rural areas should be shorten.

The response of sediment transport and morphological evolution to storms with different characteristics.

Strong wave-current interaction under the impact of storm events can induce a series of complex sedimentary processes of sediment resuspension and transport and morphology changes, significantly changing the topography of coastal zones. However, coastal sedimentary processes during storm events have not been fully understood. In this study, we developed a wave-current-sediment coupled model to investigate the response of dynamical processes to extreme storm events. The model was first validated against the observed data for both storm conditions during the 2007 Typhoon Wipha and fair-weather conditions in 2016 in the Haizhou Bay (HZB) of the Yellow Sea. The simulated results indicated that the longshore sediment transport was dominated originally by tidal effects which were significantly enhanced by wind-induced waves during the passage of the Typhoon Wipha. Storms with different characteristics correspond to two typical sedimentary dynamic response modes based on a series of numerical experiments. The tidal pumping effect (T3 + T4 + T5) and gravitational circulation term (T6) controlled the total storm-induced sediment flux, and T6 played a crucial and special role, typically in the opposite direction of the dominant wind of the storm. The strong wind could lead to the stratification of the water column, causing the down-slope or up-slope cross-shore sediment transport, resulting in coastal seabed erosion/deposition. In addition, the onshore wind was found to have a stronger impact on the sedimentary process. The methodology and findings of this study provide a scientific basis for understanding the response mechanism of sediment transport during storm events in coastal areas.

Radiomics-based machine learning approach for the prediction of grade and stage in upper urinary tract urothelial carcinoma: a step towards virtual biopsy.

OBJECTIVES: Upper tract urothelial carcinoma (UTUC) is a rare, aggressive lesion, with early detection a key to its management. This study aimed to utilise computed tomographic urogram data to develop machine learning models for predicting tumour grading and staging in upper urothelial tract carcinoma patients and to compare these predictions with histopathological diagnosis used as reference standards. METHODS: Protocol-based computed tomographic urogram data from 106 patients were obtained and visualised in 3D. Digital segmentation of the tumours was conducted by extracting textural radiomics features. They were further classified using 11 predictive models. The predicted grades and stages were compared to the histopathology of radical nephroureterectomy specimens. RESULTS: Classifier models worked well in mining the radiomics data and delivered satisfactory predictive machine learning models. The multilayer panel showed 84% sensitivity and 93% specificity while predicting UTUC grades. The Logistic Regression model showed a sensitivity of 83% and a specificity of 76% while staging. Similarly, other classifier algorithms [e.g. Support Vector classifier (SVC)] provided a highly accurate prediction while grading UTUC compared to clinical features alone or ureteroscopic biopsy histopathology. CONCLUSION: Data mining tools could handle medical imaging datasets from small (<2 cm) tumours for UTUC. The radiomics-based machine learning algorithms provide a potential tool to model tumour grading and staging with implications for clinical practice and the upgradation of current paradigms in cancer diagnostics. CLINICAL RELEVANCE: Machine learning based on radiomics features can predict upper tract urothelial cancer grading and staging with significant improvement over ureteroscopic histopathology. The study showcased the prowess of such emerging tools in the set objectives with implications towards virtual biopsy.

Seedling root system adaptation to water availability during maize domestication and global expansion.

The maize root system has been reshaped by indirect selection during global adaptation to new agricultural environments. In this study, we characterized the root systems of more than 9,000 global maize accessions and its wild relatives, defining the geographical signature and genomic basis of variation in seminal root number. We demonstrate that seminal root number has increased during maize domestication followed by a decrease in response to limited water availability in locally adapted varieties. By combining environmental and phenotypic association analyses with linkage mapping, we identified genes linking environmental variation and seminal root number. Functional characterization of the transcription factor ZmHb77 and in silico root modeling provides evidence that reshaping root system architecture by reducing the number of seminal roots and promoting lateral root density is beneficial for the resilience of maize seedlings to drought.

CS1 Expression Pattern in NK Cells and Correlated Factors in Plasma Cell dyscrasias: Implications for Elotuzumab Therapy and CAR-T Efficacy.

Treatment with elotuzumab alone has no discernible antitumor effect and progress in chimeric antigen receptor T cells (CAR-T) therapy targeting CS1 is relatively slow. A retrospective analysis was performed on 236 patients with multiple myeloma (MM) and 30 patients with other plasma cell dyscrasias (PCDs). CS1 expression in NK cells, lymphocytes, and monoclonal plasma cells was assessed using multiparameter flow cytometry. Furthermore, new explorations were undertaken regarding the antitumor applications of elotuzumab. Patients with MM had significantly higher CS1 expression levels in plasma cells than other patients with PCDs, with no significant differences between lymphocytes and NK cells. In both patients with MM and other PCDs, CS1 expression was significantly higher in plasma cells than in NK cells and lymphocytes. Univariate and multivariate analyses revealed a significant correlation between CS1 expression in plasma (r = 0.60; P < 0.001) and NK (r = 0.79; P < 0.001) cells. Factors such as cytogenetic abnormalities, disease progression, and survival were not associated with CS1 expression in NK cells. Moreover, this study showed that elotuzumab strongly increases the cytotoxicity of NK cells against non-plasma and plasma tumor cells independent of their CS1 expression level. This underscores the potential of elotuzumab in combination with NK cells as an effective therapeutic strategy against a broad spectrum of tumor types.

Development of multi-epitope mRNA vaccine against Clostridioides difficile using reverse vaccinology and immunoinformatics approaches.

Clostridioides difficile (C. difficile), as the major pathogen of diarrhea in healthcare settings, has become increasingly prevalent within community populations, resulting in significant morbidity and mortality. However, the therapeutic options for Clostridioides difficile infection (CDI) remain limited, and as of now, no authorized vaccine is available to combat this disease. Therefore, the development of a novel vaccine against C. difficile is of paramount importance. In our study, the complete proteome sequences of 118 strains of C. difficile were downloaded and analyzed. We found four antigenic proteins that were highly conserved and can be used for epitope identification. We designed two vaccines, WLcd1 and WLcd2, that contain the ideal T-cell and B-cell epitopes, adjuvants, and the pan HLA DR-binding epitope (PADRE) sequences. The biophysical and chemical assessments of these vaccine candidates indicated that they were suitable for immunogenic applications. Molecular docking analyses revealed that WLcd1 bonded with higher affinity to Toll-like receptors (TLRs) than WLcd2. Furthermore, molecular dynamics (MD) simulations, performed using Gmx_MMPBSA v1.56, confirmed the binding stability of WLcd1 with TLR2 and TLR4. The preliminary findings suggested that this multi-epitope vaccine could be a promising candidate for protection against CDI; however, experimental studies are necessary to confirm these predictions.

Case report: Light-chain amyloidosis responsive to selinexor in combination with daratumumab and dexamethasone (SDd) therapy.

The outcome of AL amyloidosis remains poor, particularly in patients with advanced organ involvement which takes long time to recovery. We conducted an observational study of two patients with AL amyloidosis treated with SDd regimen. Both patients successfully achieved significant hematological and organ responses without severe adverse events, and the time to organ response was remarkably shorter than previously reported. Notably, an over 15% reduction in interventricular septal thickness (IVST) was observed in patient#2 within 6 months. Up to now, SDd therapy has not been previously reported in AL amyloidosis and may be a promising option for these patients.

Hydrological response of drought impacts across catchments worldwide.

Drought will inevitably affect linkages between different water components, which have previously been investigated across different spatiotemporal scales. Elucidating drought-induced precipitation (P) partition effects remain uncertain because they involve drought propagation, even inducing streamflow (Q) non-stationarity. This study collected data on 1069 catchments worldwide to investigate Q and evapotranspiration (ET) impacts from P deficit-derived reductions in drought propagation. Results show that P deficits trigger soil moisture drought, subsequently inducing negative Q and ET anomalies that vary under different climate regimes. Generally, drought-induced hydrological legacies indicate that breaks in hydrological linkages cause a relatively rapid Q response (i.e., negative Q anomaly), amplified by drought strength and duration. Compared with the Q response, the ET response to drought stress involves a more complex, associative vegetation response and an associative evaporative state controlled by water and energy, which lags behind the Q response and can also intensify with increasing drought severity and duration. This is confirmed by the ET response under different climate regimes. Namely, in drier climates, a positive ET anomaly can be detected in its early stages, this is unusual in wetter climate. Additionally, Q and ET sensitivity to drought strength can be mechanistically explained by the water and energy status. This implies that ET is mainly controlled by water and energy, resulting in higher and lower drought sensitivity within water- and energy-limited regions, respectively. Understanding the impacts of drought on Q and ET response is essential for identifying key linkages in drought propagation across different climate regimes. Our findings will also be useful for developing early warning and adaptation systems that support both human and ecosystem requirements.

A Comparative Study of Removal of Acid Red 27 by Adsorption on Four Different Chitosan Morphologies.

To investigate the relationship between structures and adsorption properties, four different morphologies of chitosan, with hydrogel (CSH), aerogel (CSA), powder (CSP), and electrospinning nanofiber (CSEN) characteristics, were employed as adsorbents for the removal of Acid Red 27. The structures and morphologies of the four chitosan adsorbents were characterized with SEM, XRD, ATR-FTIR, and BET methods. The adsorption behaviors and mechanisms of the four chitosan adsorbents were comparatively studied. All adsorption behaviors exhibited a good fit with the pseudo-second-order kinetic model (R2 > 0.99) and Langmuir isotherm model (R2 > 0.99). Comparing the adsorption rates and the maximum adsorption capacities, the order was CSH > CSA > CSP > CSEN. The maximum adsorption capacities of CSH, CSA, CSP, and CSEN were 2732.2 (4.523), 676.7 (1.119), 534.8 (0.885), and 215.5 (0.357) mg/g (mmol/g) at 20 °C, respectively. The crystallinities of CSH, CSA, CSP, and CSEN were calculated as 0.41%, 6.97%, 8.76%, and 39.77%, respectively. The crystallinity of the four chitosan adsorbents was the main factor impacting the adsorption rates and adsorption capacities, compared with the specific surface area. With the decrease in crystallinity, the adsorption rates and capacities of the four chitosan adsorbents increased gradually under the same experimental conditions. CSH with a low crystallinity and large specific surface area resulted in the highest adsorption rate and capacity.

Modeling the Potential Distribution Patterns of the Invasive Plant Species Phytolacca americana in China in Response to Climate Change.

Phytolacca americana, introduced to China in the 20th century for its medicinal properties, has posed a significant ecological and agricultural challenge. Its prolific fruit production, high reproductive coefficient, adaptability, and toxic roots and fruits have led to the formation of monoculture communities, reducing native species diversity and posing threats to agriculture, human and animal health, and local ecosystems. Understanding its potential distribution patterns at a regional scale and its response to climate change is essential for effective monitoring, management, and control. In this study, we utilized the Maxent model to simulate potential habitat areas of P. americana across three timeframes (current, 2050s, and 2070s) under three climate change scenarios (SSP126, SSP245, and SSP585). Leveraging data from 556 P. americana sites across China, we employed ROC curves to assess the prediction accuracy. Our findings highlight key environmental factors influencing P. americana's geographical distribution, including the driest month's precipitation, the coldest month's minimum temperature, the wettest month's precipitation, isothermality, and temperature annual range. Under current climate conditions, P. americana potentially inhabits 280.26 × 104 km2 in China, with a concentration in 27 provinces and cities within the Yangtze River basin and its southern regions. While future climate change scenarios do not drastically alter the total suitable area, the proportions of high and low-suitability areas decrease over time, shifting towards moderate suitability. Specifically, in the SSP126 scenario, the centroid of the predicted suitable area shifts northeastward and then southwestward. In contrast, in the SSP245 and SSP585 scenarios, the centroid shifts northward.

Increased serum asprosin is correlated with diabetes mellitus-induced erectile dysfunction.

OBJECTIVE: Asprosin, a newly identified adipokine, is pathologically increased in type 2 diabetes. The aim of this study is to see whether serum asprosin concentrations are linked to diabetes mellitus-induced erectile dysfunction (DMED). METHODS: 90 male patients with type 2 diabetes were included. According to the International Index of Erectile Function (IIEF-5) score, they were classified into two groups: 45 type 2 diabetes patients without erectile dysfunction (DM group) (IIEF-5 > 21),45 patients with diabetes induced erectile dysfunction (DMED group) (IIEF-5 ≤ 21)0.45 healthy male volunteers with normal blood glucose, IIEF-5 score > 21 points, and age matched with the DMED group were included as the control group. Anthropometric and biochemical variables were determined in all participants. RESULTS: When compared to the controls, T2DM ( Type 2 Diabetes Mellitus)patients had higher serum asprosin levels. The DMED group had significantly higher serum asprosin than the T2DM groups(p < 0.001). After adjusting for multiple variables considered traditional risk factors for ED(erectile dysfunction), Asprosin can still be used as an independent risk factor for ED; The ROC(Receive Operating Characteristic Curve) indicates that asprosin has good sensitivity (97.8%) and specificity (62.2%) in predicting ED, with an area under the curve of 0.843.Correlation analysis shows that asprosin is negatively correlated with SOD(superoxide dismutase ) and positively correlated with MDA (malondialdehyde). CONCLUSION: Serum asprosin concentrations are increased in patients with DMED. Also, asprosin is correlated with oxidative stress indexes (MDA, SOD).

Focal length switchable metalens based on vanadium dioxide.

A metalens is a flat lens that can control the phase of light so that dispersed light can be reconcentrated. This study devised a tunable metalens with a switchable focal length based on the phase transition properties of vanadium dioxide (V O 2). The unit structure comprises three layers from bottom to top: gold, polyimide, and two square resonant rings. The metalens can not only transform incident x-polarized waves into y-polarized waves but also achieve beam focusing simultaneously. The designed metalens achieves polarization conversion efficiency at an operating frequency of 0.8 THz. In the insulating state of V O 2, the beam focal point is at L=1914µm; in the metallic state, the wave converges at L=982µm, closely aligning with the predetermined focal length. By controlling external temperature, focal point switching can be achieved, making it highly versatile in practical applications.

In silico development of a novel anti-mutation, multi-epitope mRNA vaccine against MPXV variants of emerging lineage and sub-lineages by using immunoinformatics approaches.

Over the past year, an unexpected surge in human monkeypox (hMPX) cases has been observed. This outbreak differs from previous ones, displaying distinct epidemiological characteristics and transmission patterns, believed to be influenced by a newly emerging monkeypox virus (MPXV) lineage. Notably, this emerging MPXV lineage has exhibited several non-synonymous mutations, some of which are linked to immunomodulatory activities and antigenic characteristics that aid in host detection. However, specific treatments or vaccines for human monkeypox are currently lacking. Hence, we aim to develop a multi-epitope mRNA vaccine by using immunoinformatics approaches against the MPXV, particularly its emerging variants. Six proteins (A29L, A35R, B6R, M1R, H3L, and E8L) were chosen for epitope and mutation site identification. Seventeen top-performing epitopes and eight epitopes containing mutation sites were selected and combined with adjuvants, the PADRE sequence, and linkers for vaccine development. The molecular and physical properties of the designed vaccine (WLmpx) were favorable. Immunological characteristics of WLmpx were assessed through molecular docking, molecular dynamics (MD) simulations, and immune simulations. Finally, the vaccine sequence was utilized to formulate an mRNA-based vaccine. The informatics-based predicted results indicated that the designed vaccine exhibits significant potential in eliciting high-level humoral and cellular immune responses, but further validation through in vivo and vitro studies is warranted.Communicated by Ramaswamy H. Sarma.

Machine learning surrogates for surface complexation model of uranium sorption to oxides.

The safety assessments of the geological storage of spent nuclear fuel require understanding the underground radionuclide mobility in case of a leakage from multi-barrier canisters. Uranium, the most common radionuclide in non-reprocessed spent nuclear fuels, is immobile in reduced form (U(IV) and highly mobile in an oxidized state (U(VI)). The latter form is considered one of the most dangerous environmental threats in the safety assessments of spent nuclear fuel repositories. The sorption of uranium to mineral surfaces surrounding the repository limits their mobility. We quantify uranium sorption using surface complexation models (SCMs). Unfortunately, numerical SCM solvers often encounter convergence problems due to the complex nature of convoluted equations and correlations between model parameters. This study explored two machine learning surrogates for the 2-pK Triple Layer Model of uranium retention by oxide surfaces if released as U(IV) in the oxidizing conditions: random forest regressor and deep neural networks. Our surrogate models, particularly DNN, accurately reproduce SCM model predictions at a fraction of the computational cost without any convergence issues. The safety assessment of spent fuel repositories, specifically the migration of leaked radioactive waste, will benefit from having ultrafast AI/ML surrogates for the computationally expensive sorption models that can be easily incorporated into larger-scale contaminant migration models. One such model is presented here.

The role of early cerebral edema and hematoma assessment in aneurysmal subarachnoid hemorrhage (a-SAH) in predicting early brain injury (EBI) and cognitive impairment: a case controlled study.

BACKGROUND: Early assessment and management of cerebral edema and hematoma following aneurysmal subarachnoid hemorrhage (a-SAH) can significantly impact clinical cognitive outcomes. However, current clinical practices lack predictive models to identify early structural brain abnormalities affecting cognition. To address this gap, the authors propose the development of a predictive model termed the a-SAH Early Brain Edema/Hematoma Compression Neural (Structural Brain) Networks Score System (SEBE-HCNNSS). METHODS: In this study, 202 consecutive patients with spontaneous a-SAH underwent initial computed tomography (CT) or MRI scans within 24 h of ictus with follow-up 2 months after discharge. Using logistic regression analysis (univariate and multivariate), the authors evaluated the association of clinically relevant factors and various traditional scale ratings with cognitive impairment (CI). Risk factors with the highest area under the curve (AUC) values were included in the multivariate analysis and least absolute shrinkage and selection operator (LASSO) analysis or Cox regression analysis. RESULTS: A total of 177 patients were enrolled in the study, and 43 patients were classified with a high SEBE-HCNNSS grade (3-5). After a mean follow-up of 2 months, 121 individuals (68.36%) with a-SAH and three control subjects developed incident CI. The CT interobserver reliability of the SEBE-HCNNSS scale was high, with a Kappa value of 1. Furthermore, ROC analysis identified the SEBE-HCNNSS scale (OR 3.322, 95% CI: 2.312-7.237, P =0.00025) as an independent predictor of edema, CI, and unfavorable prognosis. These results were also replicated in a validation cohort. CONCLUSION: Overall, the SEBE-HCNNSS scale represents a simple assessment tool with promising predictive value for CI and clinical outcomes post-a-SAH. Our findings indicate its practical utility as a prognostic instrument for risk evaluation after a-SAH, potentially facilitating early intervention and treatment.

Forest fire risk zoning based on fuzzy logic and analytical network process.

Forest fires have a significant impact on human life, property safety, and ecological environment. Deve-loping high-quality forest fire risk maps is beneficial for preventing forest fires, guiding resource allocation for firefighting, assisting in fire suppression efforts, and supporting decision-making. With a multi-criteria decision analysis (MCDA) method based on geographic information systems (GIS) and literature review, we assessed the main factors influencing the occurrences of forest fires in Youxi County, Fujian Province. We analyzed the importance of each fire risk factor using the analytic network process (ANP) and assigned weights, and evaluated the sub-standard weights using fuzzy logic assessment. Using ArcGIS aggregation functions, we generated a forest fire risk map and validated it with satellite fire points. The results showed that the areas classified as level 4 or higher fire risk accounted for a considerable proportion in Youxi County, and that the central and northern regions were at higher risk. The overall fire risk situation in the county was severe. The fuzzy ANP model demonstrated a high accuracy of 85.8%. The introduction of this novel MCDA method could effectively improve the accuracy of forest fire risk mapping at a small scale, providing a basis for early fire warning and the planning and allocation of firefighting resources.

A signature based on circadian rhythm-associated genes for the evaluation of prognosis and the tumour microenvironment in HNSCC.

The morbidity and mortality rates of head and neck squamous cell carcinoma (HNSCC) remain high worldwide. Therefore, there is an urgent need to identify a new prognostic biomarker to guide the personalized treatment of HNSCC patients. Increasing evidence suggests that circadian rhythm genes play an important role in the development and progression of cancer. We aimed to explore the value of circadian rhythm genes in predicting prognosis and guiding the treatment of HNSCC. We first obtained a list of circadian rhythm genes from previous research. The sequencing data were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Finally, univariate Cox proportional hazard analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox proportional hazard analysis were performed to develop a prognostic signature (Circadian Rhythm-Related Gene Prognostic Index, CRRGPI) consisting of nine circadian rhythm genes. The signature exhibited good performance in predicting overall survival. Patients with low CRRGPI scores had lower metabolic activities and an active antitumour immunity ability. Additionally, a clinical cohort was used to further evaluate the ability of the CRRGPI to predict the efficacy of immune checkpoint inhibitors. In conclusion, the novel circadian rhythm-related gene signature can provide a precise prognostic evaluation with the potential capacity to guide individualized treatment regimens for HNSCC patients.

First-principles calculation of self-interstitial atom-impurity atom interactions in ferritic steel.

In this study, the interactions between self-interstitial atoms (SIA) and impurity atoms (Cu and P) in the body-centered cubic (bcc)-Fe matrix have been investigated using the first principles approach. The results show that Cu and P atoms are more prone to segregation on perpendicular and parallel surfaces containing dumbbell atoms, respectively. Next, by combining the charge density difference and considering the electronic structure and lattice distortion, the origin of the binding energy of complexes formed between SIA and impurity atoms was discussed. The results show that as the number of impurity atoms increases, the atomic bonds formed by the interactions between the impurity atoms decrease the binding energy between single impurity atoms and the matrix and reduce the strain field around them, resulting in an increase in the stability of the complexes. Comparison with previous experimental results revealed the reasons for the changes in atomic occupancy during the segregation of Cu and P atoms. The results provide insights into the behavior of impurity atoms in irradiated materials and provide a deeper understanding of the electron level of impurity atomization.