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A general and simple semi-analytic theory of multilayer dielectric gratings is presented. It extends a previous work [J. Opt. Soc. Am. A 41 , 252 (2024)] that assumes symmetric grating profile and Littrow mounting to gratings of asymmetric profiles in off-Littrow mounting.

The purpose of this study is to realize the automatic identification and classification of fouls in football matches and improve the overall identification accuracy. Therefore, a Deep Learning-Based Saliency Prediction Model (DLSPM) is proposed. DLSPM combines the improved DeepPlaBV 3+architecture for salient region detection, Graph Convolutional Networks (GCN) for feature extraction and Deep Neural Network (DNN) for classification. By automatically identifying the key action areas in the image, the model reduces the dependence on traditional image processing technology and manual feature extraction, and improves the accuracy and robustness of foul behavior identification. The experimental results show that DLSPM performs significantly better than the existing methods on multiple video motion recognition data sets, especially when dealing with complex scenes and dynamic changes. The research results not only provide a new perspective and method for the field of video motion recognition, but also lay a foundation for the application in intelligent monitoring and human-computer interaction.

The work started 20 years ago [Appl. Opt. 42 , 6255 (2003)] investigating the physical mechanism of multilayer dielectric reflection gratings to achieve 100% diffraction efficiency is extended to offer much deeper insight than before. How different diffraction amplitudes of the top surface corrugation contribute to the −1st-order efficiency of such a grating is shown analytically using a minimum set of real parameters. The two diffraction amplitudes transmitted through the corrugation play a dominant role in enabling DE = 100%. The necessary and sufficient condition for 100% efficiency is derived, and a very simple sufficient condition is also given. Moreover, the role of the reflection phase of the perfect-reflector, including the contribution due to optical path between the corrugation and the reflector, is emphasized.

Circulating tumor DNA (ctDNA) provides a noninvasive approach to elucidate a patient’s genomic landscape and actionable information. Here, we design a ctDNA-based study of over 10,000 pan-cancer Chinese patients. Using parallel sequencing between plasma and white blood cells, 14% of plasma cell-free DNA samples contain clonal hematopoiesis (CH) variants, for which detectability increases with age. After eliminating CH variants, ctDNA is detected in 73.5% of plasma samples, with small cell lung cancer (91.1%) and prostate cancer (87.9%) showing the highest detectability. The landscape of putative driver genes revealed by ctDNA profiling is similar to that in a tissue-based database (R 2  = 0.87, p  < 0.001) but also shows some discrepancies, such as higher EGFR (44.8% versus 25.2%) and lower KRAS (6.8% versus 27.2%) frequencies in non-small cell lung cancer, and a higher TP53 frequency in hepatocellular carcinoma (53.1% versus 28.6%). Up to 41.2% of plasma samples harbor drug-sensitive alterations. These findings may be helpful for identifying therapeutic targets and combined treatment strategies. The detection of aberrations in circulating tumour DNA represents a non-invasive method to survey the oncogenes and tumour suppressors that are modified within a patient’s cancer. Here, the authors analysed more than 10,000 patients using a targeted sequencing panel and report on the frequencies of the mutations that they found.

Increased evidence shows that gut microbiota acts as the primary regulator of the liver; however, its role in sepsis-related liver injury (SLI) in the elderly is unclear. This study assessed whether metformin could attenuate SLI by modulating gut microbiota in septic-aged rats. Cecal ligation and puncture (CLP) was used to induce SLI in aged rats. Fecal microbiota transplantation (FMT) was used to validate the roles of gut microbiota in these pathologies. The composition of gut microbiota was analysed by 16S rRNA sequencing. Moreover, the liver and colon tissues were analysed by histopathology, immunofluorescence, immunohistochemistry, and reverse transcription polymerase chain reaction (RT-PCR). Metformin improved liver damage, colon barrier dysfunction in aged SLI rats. Moreover, metformin improved sepsis-induced liver inflammation and damage under gut microbiota. Importantly, FMT assay showed that rats gavaged with faeces from metformin-treated SLI rats displayed less severe liver damage and colon barrier dysfunctions than those gavaged with faeces from SLI rats. The gut microbiota composition among the sham-operated, CLP-operated and metformin-treated SLI rats was different. In particular, the proportion of Klebsiella and Escherichia_Shigella was higher in SLI rats than sham-operated and metformin-treated SLI rats; while metformin could increase the proportion of Bifidobacterium, Muribaculaceae, Parabacteroides_distasonis and Alloprevitella in aged SLI rats. Additionally, Klebsiella and Escherichia_Shigella correlated positively with the inflammatory factors in the liver. Our findings suggest that metformin may improve liver injury by regulating the gut microbiota and alleviating colon barrier dysfunction in septic-aged rats, which may be an effective therapy for SLI.

In this study we seek to assess recent trends in complementary and alternative medicine (CAM) use based on a comparative analysis of data from the 2002 and 2007 National Health Interview Survey (NHIS). The findings suggest that CAM use, in particular the use of provider-based CAM therapies such as chiropractic care, massage, and acupuncture, have grown significantly in the U.S. This growth was more pronounced among non-Hispanic Whites than among racial and ethnic minorities, increasing an already existing White-minority gap in CAM use. Findings from this study also reveal that CAM use becomes more likely when access to conventional care has been restricted. In both 2002 and 2007, having unmet needs in medical care or having delayed care due to cost were associated with a higher chance of CAM use.

The incidence and mortality of cancer are the major health issue worldwide. Apart from the treatments developed to date, the unsatisfactory therapeutic effects of cancers have not been addressed by broadening the toolbox. The advent of immunotherapy has ushered in a new era in the treatments of solid tumors, but remains limited and requires breaking adverse effects. Meanwhile, the development of advanced technologies can be further boosted by gene analysis and manipulation at the molecular level. The advent of cutting-edge genome editing technology, especially clustered regularly interspaced short palindromic repeats (CRISPR-Cas9), has demonstrated its potential to break the limits of immunotherapy in cancers. In this review, the mechanism of CRISPR-Cas9-mediated genome editing and a powerful CRISPR toolbox are introduced. Furthermore, we focus on reviewing the impact of CRISPR-induced double-strand breaks (DSBs) on cancer immunotherapy (knockout or knockin). Finally, we discuss the CRISPR-Cas9-based genome-wide screening for target identification, emphasis the potential of spatial CRISPR genomics, and present the comprehensive application and challenges in basic research, translational medicine and clinics of CRISPR-Cas9.

In recent years, immunotherapy has become a hot spot in the treatment of tumors. As an emerging treatment, it solves many problems in traditional cancer treatment and has now become the main method for cancer treatment. Although immunotherapy is promising, most patients do not respond to treatment or develop resistance. Therefore, in order to achieve a better therapeutic effect, combination therapy has emerged. The combination of immune checkpoint inhibition and epigenetic therapy is one such strategy. In this review, we summarize the current understanding of the key mechanisms of how epigenetic mechanisms affect cancer immune responses and reveal the key role of epigenetic processes in regulating immune cell function and mediating anti-tumor immunity. In addition, we highlight the outlook of combined epigenetic and immune regimens, particularly the combination of immune checkpoint blockade with epigenetic agents, to address the limitations of immunotherapy alone.

Lung cancer (LC) is one of the top ten malignant tumors and the first leading cause of cancer-related death among both men and women worldwide. It is imperative to identify immune-related biomarkers for early LC diagnosis and treatment. Three Gene Expression Omnibus (GEO) datasets were selected to acquire the differentially expressed genes(DEGs) between LC and normal lung samples through GEO2R tools of NCBI. To identify hub genes, the DEGs were performed functional enrichment analysis, the protein-protein interaction (PPI) network construction, and Lasso regression. Then, a nomogram was constructed to predict the prognosis of patients with carcinoma based on hub genes. We further evaluated the influence of COL1A1 on clinical prognosis using GSE3141, GSE31210, and TCGA database. Also, the correlations between COL1A1 and cancer immune infiltrates and the B7-CD28 family was investigated via TIMER and GEPIA. Further analysis of immunohistochemistry shown that the COL1A1 expression level is positively correlated with CD276 expression level. By difference analysis, there were 340 DEGs between LC and normal lung samples. Then, we picked out seven hub genes, which were identified as components of the risk signature to divide LC into low and high-risk groups. Among them, the expression of COL1A1 is highly correlated with overall survival(OS) and progression-free survival (PFS) (  < 0.05). Importantly, there is a moderate to strong positive relationships between COL1A1 expression level and infiltration level of CD4+ T cells, Macrophage, Neutrophil, and Dendritic cell, as well as CD276 expression level. These findings suggest that COL1A1 is correlated with prognosis and immune infiltrating levels, including CD4+ T cells, Macrophage, Neutrophil, and Dendritic cell, as well as CD276 expression level, indicating COL1A1 can be a potential immunity-related biomarker and therapeutic target in LC.

Background Recent studies have reported that preadmission metformin users had lower mortality than non-metformin users in patients with sepsis and diabetes mellitus; however, these results are still controversial. Therefore, we conducted a systematic review and meta-analysis of published observational cohort data to determine the association between preadmission metformin use and mortality in septic adult patients with diabetes mellitus. Methods The MEDLINE, EMBASE, and Cochrane CENTRAL databases were searched from their inception to September 30, 2018. Cohort studies that evaluated the use of metformin in septic adult patients with diabetes mellitus were included. The quality of outcomes was evaluated using the Newcastle-Ottawa Scale (NOS). The inverse variance method with random effects modelling was used to calculate the pooled odds ratios (ORs) and 95% CIs. Results Five observational cohort studies (1282 patients) that were all judged as having a low risk of bias were included. In this meta-analysis, metformin use was associated with a significantly lower mortality rate (OR, 0.59; 95% CI, 0.43–0.79, P  = 0.001). Conclusions This meta-analysis indicated an association between metformin use prior to admission and lower mortality in septic adult patients with diabetes mellitus. This finding suggested that the possible effect of metformin should be evaluated in future clinical trials.