Exploring the biphasic dose-response effects of photobiomodulation on the viability, migration, and extracellular vesicle secretion of human adipose mesenchymal stem cells.

Photobiomodulation (PBM) is a well-established medical technology that employs diverse light sources like lasers or light-emitting diodes to generate diverse photochemical and photophysical reactions in cells, thereby producing beneficial clinical outcomes. In this study, we introduced an 830 nm near-infrared (NIR) laser irradiation system combined with a microscope objective to precisely and controllably investigate the impact of PBM on the migration and viability of human adipose mesenchymal stem cells (hADSCs). We observed a biphasic dose-response in hADSCs' viability and migration after PBM exposure (0-10 J/cm2), with the 5 J/cm2 group showing significantly higher cell viability and migration ability than other groups. Additionally, at the optimal dose of 5 J/cm2, we used nanoparticle tracking analysis (NTA) and found a 6.25-fold increase in the concentration of extracellular vesicles (EVs) derived from hADSCs (PBM/ADSC-EVs) compared to untreated cells (ADSC-EVs). Both PBM/ADSC-EVs and ADSC-EVs remained the same size, with an average diameter of 56 nm measured by the ExoView R200 system, which falls within the typical size range for exosomes. These findings demonstrate that PBM not only improves the viability and migration of hADSCs but also significantly increases the EV yield.

Tf2O-Mediated P(O)-N Bond Formation of Either P(O)-OH or P(O)-H Reagents with Multitype Amines.

We have developed a Tf2O-mediated approach for the direct amination of either P(O)-OH or P(O)-H reagents with a variety of aliphatic or aromatic amines. Without the requirement of precious metals and toxic reagents, this protocol provides an alternative route to various phosphinamides and phosphoramides. The reaction proceeds under simple and mild conditions and can be effectively scaled up with similar efficiency.

Integrating Clinical Variability into PBPK Models for Virtual Bioequivalence of Single and Multiple Doses of Tofacitinib Modified-Release Dosage Form.

Tofacitinib is a potent, selective inhibitor of the Janus kinase (JAK) family of kinases with a high degree of selectivity within the human genome's set of protein kinases. Currently approved formulations for tofacitinib citrate are immediate-release (IR) tablets, modified-release (MR) tablets, and IR solution. A once daily MR microsphere formulation was developed for use in pediatric patients. Demonstration of bioequivalence (BE) between the 10 mg once daily (q.d.) MR microsphere formulation and 5 mg twice daily (b.i.d.) IR solution is needed to enable the exposure-response analyses-based bridging to support regulatory approval. To assess BE between MR microsphere and IR solution, an innovative approach was utilized with physiologically-based pharmacokinetic (PBPK) virtual BE trials (VBE) in lieu of a clinical BE trial. A PBPK model was developed to characterize the absorption of different formulations of tofacitinib using Simcyp ADAM module. VBE trials were conducted by simulating PK profiles using the verified PBPK model and integrating the clinically observed intrasubject coefficient of variation (ICV) where BE was assessed with a predetermined sample size and prespecified criteria. The VBE trials demonstrated BE between IR solution 5 mg b.i.d. and MR microsphere 10 mg q.d. after a single dose on day 1 and after multiple doses on day 5. This research presents an innovative approach that incorporates clinically observed ICV in PBPK model-based VBE trials, which could reduce unnecessary drug exposure to healthy volunteers and streamline new formulation development strategies.

Impact of homocysteine on acute ischemic stroke severity: possible role of aminothiols redox status.

BACKGROUND: Acute ischemic stroke (AIS) is one of the most common cerebrovascular diseases which accompanied by a disruption of aminothiols homeostasis. To explore the relationship of aminothiols with neurologic impairment severity, we investigated four aminothiols, homocysteine (Hcy), cysteine (Cys), cysteinylglycine (CG) and glutathione (GSH) in plasma and its influence on ischemic stroke severity in AIS patients. METHODS: A total of 150 clinical samples from AIS patients were selected for our study. The concentrations of free reduced Hcy (Hcy), own oxidized Hcy (HHcy), free reduced Cys (Cys), own oxidized Cys (cysteine, Cyss), free reduced CG (CG) and free reduced GSH (GSH) were measured by our previously developed hollow fiber centrifugal ultrafiltration (HFCF-UF) method coupled with high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). The concentration ratio of Hcy to HHcy (Hcy/HHcy), Cys to Cyss (Cys/Cyss) were also calculated. The neurologic impairment severity of AIS was evaluated using National Institutes of Health Stroke Scale (NIHSS). The Spearman correlation coefficient and logistic regression analysis was used to estimate and perform the correlation between Hcy, HHcy, Cys, Cyss, CG, GSH, Hcy/HHcy, Cys/Cyss and total Hcy with NIHSS score. RESULTS: The reduced Hcy and Hcy/HHcy was both negatively correlated with NIHSS score in AIS patients with P = 0.008, r=-0.215 and P = 0.002, r=-0.249, respectively. There was no significant correlation of Cys, CG, GSH, HHcy, Cyss, Cys/Cyss and total Hcy with NIHSS score in AIS patients with P value > 0.05. CONCLUSIONS: The reduced Hcy and Hcy/HHcy, not total Hcy concentration should be used to evaluate neurologic impairment severity of AIS patient.

Association Between Physical Activity Amount and Cardiorespiratory Fitness, Sleep Quality, and Health-Related Quality of Life in Patients With Long COVID: A Cross-sectional Study.

OBJECTIVE: To investigate the association between physical activity (PA) amount and gender differences on cardiorespiratory fitness (CRF), sleep quality, and health-related quality of life (HRQoL) in individuals with long COVID. DESIGN: Cross-sectional study. SETTING: An integrated outpatient clinic for post-COVID-19 at a medical center. PARTICIPANTS: Convenience sample of patients (N=264) diagnosed with long COVID. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: PA amounts, sleep quality, and HRQoL were measured by the International Physical Activity Questionnaire, Pittsburgh Sleep Quality Index, and the World Health Organization Questionnaire on Quality of Life: Short Form, respectively. CRF was evaluated through graded exercise testing. RESULTS: The participants had a mean age of 42.5±13.5 years and a mean duration of post-COVID-19 symptoms of 12.7±6.8 weeks. More than half (n=149, 56.5%) were female patients. Female participants had significantly lower CRF than male participants (P<.05). Older age and higher body mass index were associated with worse CRF and HRQoL (P<.05). Less sitting behavior and greater amounts of vigorous-intensity PA (VPA) or total PA were associated with better CRF (P<.05). In addition, greater total PA and moderate-intensity PA (MPA) were associated with better sleep quality and HRQoL (P<.05), respectively. CONCLUSIONS: Gender differences were found in CRF among patients with long COVID. Greater self-reported VPA or total PA was associated with better CRF, whereas greater total PA and MPA were associated with better sleep quality and HRQoL, respectively, in patients with long COVID. Further research is needed to explore these associations in longitudinal studies.

Increased co-expression of 4-1BB with PD-1 on CD8+ tumor-infiltrating lymphocytes is associated with improved prognosis and immunotherapy response in cervical cancer.

Background: The combination of agonistic antibodies with immune checkpoint inhibitors presents a promising avenue for cancer immunotherapy. Our objective is to explore the co-expression of 4-1BB, ICOS, CD28, with PD-1 on CD8+ T cells in the peripheral blood and tumor tissue of cervical cancer(CC) patients, with a specific focus on the association between the co-expression levels of 4-1BB with PD-1 and clinical features, prognosis as well as immunotherapy response. The goal is to offer valuable insights into cervical cancer immunotherapy. Methods: In this study, 50 treatment-naive patients diagnosed with CC were enrolled. Flow cytometry was used to detect PD-1/4-1BB, PD-1/ICOS and PD-1/CD28 co-expression on CD8+ T cells. Subsequent analysis aimed to investigate the differential co-expression between peripheral blood and cancer tissue, and also the correlation between co-expression and clinical features in these patients. Gene Expression Omnibus (GEO) datasets, The Cancer Genome Atlas (TCGA) cohort, The IMvigor210 cohort, The BMS038cohort and Immunophenoscores were utilized to investigate the correlation between PD-1/4-1BB and the immune microenvironment, prognosis, immunotherapy, and drug sensitivity in cervical cancer. Results: The co-expression levels of PD-1/4-1BB, PD-1/ICOS, and PD-1/CD28 on CD8+ tumor-infiltrating lymphocytes (TILs) were significantly higher in cervical cancer patients compared to those in peripheral blood. Clinical feature analysis reveals that on CD8+ TILs, the co-expression of PD-1/4-1BB is more closely correlated with clinical characteristics compared to PD-1/ICOS, PD-1/CD28, PD-1, and 4-1BB. Pseudo-time analysis and cell communication profiling reveal close associations between the subgroups harboring 4-1BB and PD-1. The prognosis, tumor mutation burden, immune landscape, and immunotherapy response exhibit statistically significant variations between the high and low co-expression groups of PD-1/4-1BB. The high co-expression group of PD-1/4-1BB is more likely to benefit from immunotherapy. Conclusion: PD-1/4-1BB, PD-1/ICOS, and PD-1/CD28 exhibit elevated co-expression on CD8+TILs of cervical cancer, while demonstrating lower expression in circulating T cells. The co-expression patterns of PD-1/4-1BB significantly contributed to the prediction of immune cell infiltration characteristics, prognosis, and tailored immunotherapy tactics. PD-1/4-1BB exhibits potential as a target for combination immunotherapy in cervical cancer.

Panda-UV Unlocks Deeper Protein Characterization with Internal Fragments in Ultraviolet Photodissociation Mass Spectrometry.

Ultraviolet photodissociation (UVPD) mass spectrometry unlocks insights into the protein structure and sequence through fragmentation patterns. While N- and C-terminal fragments are traditionally relied upon, this work highlights the critical role of internal fragments in achieving near-complete sequencing of protein. Previous limitations of internal fragment utilization, owing to their abundance and potential for random matching, are addressed here with the development of Panda-UV, a novel software tool combining spectral calibration, and Pearson correlation coefficient scoring for confident fragment assignment. Panda-UV showcases its power through comprehensive benchmarks on three model proteins. The inclusion of internal fragments boosts identified fragment numbers by 26% and enhances average protein sequence coverage to a remarkable 93% for intact proteins, unlocking the hidden region of the largest protein carbonic anhydrase II in model proteins. Notably, an average of 65% of internal fragments can be identified in multiple replicates, demonstrating the high confidence of the fragments Panda-UV provided. Finally, the sequence coverages of mAb subunits can be increased up to 86% and the complementary determining regions (CDRs) are nearly completely sequenced in a single experiment. The source codes of Panda-UV are available at https://github.com/PHOENIXcenter/Panda-UV.

Realizing thermoelectric cooling and power generation in N-type PbS0.6Se0.4 via lattice plainification and interstitial doping.

Thermoelectrics have great potential for use in waste heat recovery to improve energy utilization. Moreover, serving as a solid-state heat pump, they have found practical application in cooling electronic products. Nevertheless, the scarcity of commercial Bi2Te3 raw materials has impeded the sustainable and widespread application of thermoelectric technology. In this study, we developed a low-cost and earth-abundant PbS compound with impressive thermoelectric performance. The optimized n-type PbS material achieved a record-high room temperature ZT of 0.64 in this system. Additionally, the first thermoelectric cooling device based on n-type PbS was fabricated, which exhibits a remarkable cooling temperature difference of ~36.9 K at room temperature. Meanwhile, the power generation efficiency of a single-leg device employing our n-type PbS material reaches ~8%, showing significant potential in harvesting waste heat into valuable electrical power. This study demonstrates the feasibility of sustainable n-type PbS as a viable alternative to commercial Bi2Te3, thereby extending the application of thermoelectrics.

Pain prediction model based on machine learning and SHAP values for elders with dementia in Taiwan.

INTRODUCTION: Pain conditions are common in elderly individuals, including those with dementia. However, symptoms associated with dementia may lead to poor recognition, assessment and management of pain. In this study, we incorporated the variables based on questionnaires into a machine learning algorithm to build a prediction model for the pain index of elderly individuals with dementia. MATERIALS AND METHODS: In this study, 113 cases were collected through questionnaires and used to build prediction models for the patient's pain index. Three machine learning algorithms were incorporated for comparison in this study. To interpret the prediction model, SHapley additive explanations values were used to depict the ranking importance of variables and the relationship between features and pain index. RESULTS: In the comparison of models, random forests with feature selection outperformed in terms of root mean square error and mean absolute error. A total of 11 features were selected based on embedded method. The results showed that the Karnofsky scale played a key role in predicting pain index for elderly individuals with dementia and was positively associated with pain index. Arthritis is the most important disease to predicting the pain index. CONCLUSIONS: Our findings provided the key insights to predict the pain index of elderly patients with dementia. In the future, it can be used to develop an application system or webpage, which can reduce the use of labour and improve the efficiency.

The cementless taper wedge vs. fit-and-fill stem in primary total hip arthroplasty: risk of stem-related complication differs across Dorr types.

INTRODUCTION: The choice between a cementless taper wedge stem and a fit-and-fill stem in total Hip arthroplasty (THA) for various proximal femoral morphological types has not been thoroughly evaluated. This study aimed to compare the risk of stem-related complications between these two stem types in Dorr type A, B, and C femurs. MATERIALS AND METHODS: From January 2015 through April 2021, we retrospectively reviewed 1995 cementless THA procedures. We stratified all procedures into three groups: Dorr type A (N = 360, 18.0%), B (N = 1489, 74.7%) and C (N = 146, 7.3%). The primary outcome domain was stem-related complications, including stem subsidence ≥ 3 mm, intraoperative fracture, periprosthetic fracture and aseptic stem loosening. We performed multivariate regression analysis to compare the risk of stem-related complication between the two stem types. Other factors included age, sex, body mass index, diagnosis, age-adjusted Charlson comorbidity index, stem alignment and canal fill ratio. RESULTS: The incidence of stem-related complications in the taper wedge and fit-and-fill stem groups was 4.4% (N = 15) and 6.5% (N = 107), respectively. Fit-and-fill stems showed an increased risk of stem-related complications (aOR: 9.903, 95% CI: 1.567-62.597) only in Dorr type C femurs. No significant difference in risk was observed in Dorr type A and B femurs. Furthermore, the canal fill ratio at the lesser trochanter, 2 cm and 7 cm below the lesser trochanter, did not exhibit an association with stem-related complications in any Dorr type. CONCLUSIONS: Concerning the risk of stem-related complications, the taper wedge stem was a better choice in Dorr type C femurs. However, there was no difference in risk between the taper wedge stem and fit-and-fill stem in Dorr type A and B femurs.

Effect of 2-Mercapto-1-methylimidazole on the Electrodeposition of Nickel on an Ordered Au(111) Electrode.

A nickel film electroplated onto a metal substrate can be used as a catalyst for water splitting and a magnetic material for spin valves. Although the nucleation and growth of Ni on Au(111) have already been examined with in situ scanning tunneling microscopy (STM), the current study provides new insights of the structure of the first layer of Ni on an ordered Au(111) electrode in 0.1 M KSO4 + 1 mM H2SO4 + 10 mM NiSO4 (pH 3). Prolonged STM scanning of the Ni monolayer on a Au(111) electrode revealed interfacial mixing to produce a surface alloy, initially assuming segregated Ni domains and later transforming them to a homogeneous Ni/Au phase. The formation of the Ni/Au(111) surface alloy affected the structure of the subsequent bulk Ni deposition. The inclusion of 2-mercapto-1-methylimidazole (MMI) in the deposition bath incurred Ni deposition at a less negative potential and a faster rate, resulting in an overall 5.3 times more Ni deposited on the Au electrode in potentiodynamic experiments. MMI molecules were adsorbed on the Ni deposit to prevent Ni dissolution in the Au(111) electrode. MMI could catalyze the presumed rate-determining step from Ni2+ to Ni+ en route to the metallic Ni. The resultant Ni film with MMI had a 3D texture without a preferred crystal orientation on the Au electrode, as opposed to a layer type growth of Ni on Au(111) without MMI.

Integrative machine learning approaches for predicting disease risk using multi-omics data from the UK Biobank.

We train prediction and survival models using multi-omics data for disease risk identification and stratification. Existing work on disease prediction focuses on risk analysis using datasets of individual data types (metabolomic, genomics, demographic), while our study creates an integrated model for disease risk assessment. We compare machine learning models such as Lasso Regression, Multi-Layer Perceptron, XG Boost, and ADA Boost to analyze multi-omics data, incorporating ROC-AUC score comparisons for various diseases and feature combinations. Additionally, we train Cox proportional hazard models for each disease to perform survival analysis. Although the integration of multi-omics data significantly improves risk prediction for 8 diseases, we find that the contribution of metabolomic data is marginal when compared to standard demographic, genetic, and biomarker features. Nonetheless, we see that metabolomics is a useful replacement for the standard biomarker panel when it is not readily available.

Lipopolysaccharide binding protein resists hepatic oxidative stress by regulating lipid droplet homeostasis.

Oxidative stress-induced lipid accumulation is mediated by lipid droplets (LDs) homeostasis, which sequester vulnerable unsaturated triglycerides into LDs to prevent further peroxidation. Here we identify the upregulation of lipopolysaccharide-binding protein (LBP) and its trafficking through LDs as a mechanism for modulating LD homeostasis in response to oxidative stress. Our results suggest that LBP induces lipid accumulation by controlling lipid-redox homeostasis through its lipid-capture activity, sorting unsaturated triglycerides into LDs. N-acetyl-L-cysteine treatment reduces LBP-mediated triglycerides accumulation by phospholipid/triglycerides competition and Peroxiredoxin 4, a redox state sensor of LBP that regulates the shuttle of LBP from LDs. Furthermore, chronic stress upregulates LBP expression, leading to insulin resistance and obesity. Our findings contribute to the understanding of the role of LBP in regulating LD homeostasis and against cellular peroxidative injury. These insights could inform the development of redox-based therapies for alleviating oxidative stress-induced metabolic dysfunction.

High Carrier Mobility and Promising Thermoelectric Module Performance of N-Type PbSe Crystals.

The scarcity of Te hampers the widespread use of Bi2Te3-based thermoelectric modules. Here, the thermoelectric module potential of PbSe is investigated by improving its carrier mobility. Initially, large PbSe crystals are grown with the temperature gradient method to mitigate grain boundary effects on carrier transport. Subsequently, light doping with <1mole‰ halogens (Cl/Br/I) increases room-temperature carrier mobility to ~1600 cm2 V-1 s-1, achieved by reducing carrier concentration compared to traditional heavy doping. Crystal growth design and light doping enhance carrier mobility without affecting effective mass, resulting in a high power factor ~40 µW cm-1 K-2 in PbSe-Cl/Br/I crystals at 300 K. Additionally, Cl/Br/I doping reduces thermal conductivity and bipolar diffusion, leading to significantly lower thermal conductivity at high temperature. Enhanced carrier mobility and suppressed bipolar effect boost ZT values across the entire temperature range in n-type PbSe-Cl/Br/I crystals. Specifically, ZT values of PbSe-Br crystal reach ~0.6 at 300 K, ~1.2 at 773 K, and the average ZT (ZTave) reaches ~1.0 at 300-773 K. Ultimately, ~5.8% power generation efficiency in a PbSe single leg with a maximum temperature cooling difference of 40 K with 7-pair modules is achieved. These results indicate the potential for cost-effective and high-performance thermoelectric cooling modules based on PbSe.

Machine learning for differentiating lung squamous cell cancer from adenocarcinoma using Clinical-Metabolic characteristics and 18F-FDG PET/CT radiomics.

Noninvasive differentiation between the squamous cell carcinoma (SCC) and adenocarcinoma (ADC) subtypes of non-small cell lung cancer (NSCLC) could benefit patients who are unsuitable for invasive diagnostic procedures. Therefore, this study evaluates the predictive performance of a PET/CT-based radiomics model. It aims to distinguish between the histological subtypes of lung adenocarcinoma and squamous cell carcinoma, employing four different machine learning techniques. A total of 255 Non-Small Cell Lung Cancer (NSCLC) patients were retrospectively analyzed and randomly divided into the training (n = 177) and validation (n = 78) sets, respectively. Radiomics features were extracted, and the Least Absolute Shrinkage and Selection Operator (LASSO) method was employed for feature selection. Subsequently, models were constructed using four distinct machine learning techniques, with the top-performing algorithm determined by evaluating metrics such as accuracy, sensitivity, specificity, and the area under the curve (AUC). The efficacy of the various models was appraised and compared using the DeLong test. A nomogram was developed based on the model with the best predictive efficiency and clinical utility, and it was validated using calibration curves. Results indicated that the logistic regression classifier had better predictive power in the validation cohort of the radiomic model. The combined model (AUC 0.870) exhibited superior predictive power compared to the clinical model (AUC 0.848) and the radiomics model (AUC 0.774). In this study, we discovered that the combined model, refined by the logistic regression classifier, exhibited the most effective performance in classifying the histological subtypes of NSCLC.

Flexible and Energy-Efficient Synaptic Transistor with Quasi-Linear Weight Update Protocol by Inkjet Printing of Orientated Polar-Electret/High-k Oxide Composite Dielectric.

Inkjet printing artificial synapse is cost-effective but challenging in emulating synaptic dynamics with a sufficient number of effective weight states under ultralow voltage spiking operation. A synaptic transistor gated by inkjet-printed composite dielectric of polar-electret polyvinylpyrrolidone (PVP) and high-k zirconia oxide (ZrOx) is proposed and thus synthesized to solve this issue. Quasi-linear weight update with a large variation margin is obtained through the coupling effect and the facilitation of dipole orientation, which can be attributed to the orderly arranged molecule chains induced by the carefully designed microfluidic flows. Crucial features of biological synapses including long-term plasticity, spike-timing-dependence-plasticity (STDP), "Learning-Experience" behavior, and ultralow energy consumption (<10 fJ/pulse) are successfully implemented on the device. Simulation results exhibit an excellent image recognition accuracy (97.1%) after 15 training epochs, which is the highest for printed synaptic transistors. Moreover, the device sustained excellent endurance against bending tests with radius down to 8 mm. This work presents a very viable solution for constructing the futuristic flexible and low-cost neural systems.

Telomerase-related advances in hepatocellular carcinoma: A bibliometric and visual analysis.

BACKGROUND: As a critical early event in hepatocellular carcinogenesis, telomerase activation might be a promising and critical biomarker for hepatocellular carcinoma (HCC) patients, and its function in the genesis and treatment of HCC has gained much attention over the past two decades. AIM: To perform a bibliometric analysis to systematically assess the current state of research on HCC-related telomerase. METHODS: The Web of Science Core Collection and PubMed were systematically searched to retrieve publications pertaining to HCC/telomerase limited to "articles" and "reviews" published in English. A total of 873 relevant publications related to HCC and telomerase were identified. We employed the Bibliometrix package in R to extract and analyze the fundamental information of the publications, such as the trends in the publications, citation counts, most prolific or influential writers, and most popular journals; to screen for keywords occurring at high frequency; and to draw collaboration and cluster analysis charts on the basis of coauthorship and co-occurrences. VOSviewer was utilized to compile and visualize the bibliometric data. RESULTS: A surge of 51 publications on HCC/telomerase research occurred in 2016, the most productive year from 1996 to 2023, accompanied by the peak citation count recorded in 2016. Up to December 2023, 35226 citations were made to all publications, an average of 46.6 citations to each paper. The United States received the most citations (n = 13531), followed by China (n = 7427) and Japan (n = 5754). In terms of national cooperation, China presented the highest centrality, its strongest bonds being to the United States and Japan. Among the 20 academic institutions with the most publications, ten came from China and the rest of Asia, though the University of Paris Cité, Public Assistance-Hospitals of Paris, and the National Institute of Health and Medical Research (INSERM) were the most prolific. As for individual contributions, Hisatomi H, Kaneko S, and Ide T were the three most prolific authors. Kaneko S ranked first by H-index, G-index, and overall publication count, while Zucman-Rossi J ranked first in citation count. The five most popular journals were the World Journal of Gastroenterology, Hepatology, Journal of Hepatology, Oncotarget, and Oncogene, while Nature Genetics, Hepatology, and Nature Reviews Disease Primers had the most citations. We extracted 2293 keywords from the publications, 120 of which appeared more than ten times. The most frequent were HCC, telomerase and human telomerase reverse transcriptase (hTERT). Keywords such as mutational landscape, TERT promoter mutations, landscape, risk, and prognosis were among the most common issues in this field in the last three years and may be topics for research in the coming years. CONCLUSION: Our bibliometric analysis provides a comprehensive overview of HCC/telomerase research and insights into promising upcoming research.

Exploring the evolution of T cell function and diversity across different stages of non-small cell lung cancer.

The immune system plays a key role in detecting and fighting cancerous tumors. T cells are a crucial component in both natural and therapeutic cancer immunoediting responses, but it is unclear if they are the primary agents of these processes. In this study, patients with lung lesions detected by CT scan were selected, and their peripheral blood samples were analyzed for T cell population and serum cytokines/chemokines. T cell subtypes (CD3, CD4, CD8, CD27, CD28, CD45, CD45RA, CD57, CCR7, and PD1) and serum cytokines/chemokines (IL-2, IL-6, IL-10, IFN-γ, TGF-ß, TNFα, CXCL1, CXCL9, and CXCL12) were measured by flow cytometry and analysis before surgical resection or other cancer treatments. The frequency of T cell subpopulations in patients with lung cancer (n = 111) corresponded to those seen in patients with T cell exhaustion. As lung cancer progressed, the proportion of effector memory T cells decreased, while the proportion of naive T cells, PD-1, CD57+, CD28+CD27+, CD45RA+, and CD3+CD4+CCR7 increased. Circulating CD8+PD1+ T cells were positively correlated with intra-tumoral PD-L1 expression. Concurrently, serum levels of IL-2, TGF-ß, and CXCL9 decreased, while IL-6, IL-10, IFN-γ, and CXCL12 increased during the progression of lung cancer. In conclusion, T cell dysfunction is associated with cancer progression, particularly in advanced-stage lung cancer, and cancer immunoediting will provide early-stage cancer detection and further therapeutic strategies.

Carbon Emission Optimization of Ultra-High-Performance Concrete Using Machine Learning Methods.

Due to its exceptional qualities, ultra-high-performance concrete (UHPC) has recently become one of the hottest research areas, although the material's significant carbon emissions go against the current development trend. In order to lower the carbon emissions of UHPC, this study suggests a machine learning-based strategy for optimizing the mix proportion of UHPC. To accomplish this, an artificial neural network (ANN) is initially applied to develop a prediction model for the compressive strength and slump flow of UHPC. Then, a genetic algorithm (GA) is employed to reduce the carbon emissions of UHPC while taking into account the strength, slump flow, component content, component proportion, and absolute volume of UHPC as constraint conditions. The outcome is then supported by the results of the experiments. In comparison to the experimental results, the research findings show that the ANN model has excellent prediction accuracy with an error of less than 10%. The carbon emissions of UHPC are decreased to 688 kg/m3 after GA optimization, and the effect of optimization is substantial. The machine learning (ML) model can provide theoretical support for the optimization of various aspects of UHPC.

In silico analysis of the wheat BBX gene family and identification of candidate genes for seed dormancy and germination.

BACKGROUND: B-box (BBX) proteins are a type of zinc finger proteins containing one or two B-box domains. They play important roles in development and diverse stress responses of plants, yet their roles in wheat remain unclear. RESULTS: In this study, 96 BBX genes were identified in the wheat genome and classified into five subfamilies. Subcellular localization prediction results showed that 68 TaBBXs were localized in the nucleus. Protein interaction prediction analysis indicated that interaction was one way that these proteins exerted their functions. Promoter analysis indicated that TaBBXs may play important roles in light signal, hormone, and stress responses. qRT-PCR analysis revealed that 14 TaBBXs were highly expressed in seeds compared with other tissues. These were probably involved in seed dormancy and germination, and their expression patterns were investigated during dormancy acquisition and release in the seeds of wheat varieties Jing 411 and Hongmangchun 21, showing significant differences in seed dormancy and germination phenotypes. Subcellular localization analysis confirmed that the three candidates TaBBX2-2 A, TaBBX4-2 A, and TaBBX11-2D were nuclear proteins. Transcriptional self-activation experiments further demonstrated that TaBBX4-2A was transcriptionally active, but TaBBX2-2A and TaBBX11-2D were not. Protein interaction analysis revealed that TaBBX2-2A, TaBBX4-2A, and TaBBX11-2D had no interaction with each other, while TaBBX2-2A and TaBBX11-2D interacted with each other, indicating that TaBBX4-2A may regulate seed dormancy and germination by transcriptional regulation, and TaBBX2-2A and TaBBX11-2D may regulate seed dormancy and germination by forming a homologous complex. CONCLUSIONS: In this study, the wheat BBX gene family was identified and characterized at the genomic level by bioinformatics analysis. These observations provide a theoretical basis for future studies on the functions of BBXs in wheat and other species.