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Optical waveguides are far more than mere connecting elements in integrated optical systems and circuits. Benefiting from their high optical confinement and miniaturized footprints, waveguide structures established based on crystalline materials, particularly, are opening exciting possibilities and opportunities in photonic chips by facilitating their on-chip integration with different functionalities and highly compact photonic circuits. Femtosecond-laser-direct writing (FsLDW), as a true three-dimensional (3D) micromachining and microfabrication technology, allows rapid prototyping of on-demand waveguide geometries inside transparent materials via localized material modification. The success of FsLDW lies not only in its unsurpassed aptitude for realizing 3D devices but also in its remarkable material-independence that enables cross-platform solutions. This review emphasizes FsLDW fabrication of waveguide structures with 3D layouts in dielectric crystals. Their functionalities as passive and active photonic devices are also demonstrated and discussed.

Immunotherapy, represented by immune checkpoint inhibitors (ICI), is transforming the treatment of cancer. However, only a small percentage of patients show response to ICI, and there is an unmet need for biomarkers that will identify patients who are more likely to respond to immunotherapy. The fundamental basis for ICI response is the immunogenicity of a tumor, which is primarily determined by tumor antigenicity and antigen presentation efficiency. Here, we propose a method to measure tumor immunogenicity score (TIGS), which combines tumor mutational burden (TMB) and an expression signature of the antigen processing and presenting machinery (APM). In both correlation with pan-cancer ICI objective response rates (ORR) and ICI clinical response prediction for individual patients, TIGS consistently showed improved performance compared to TMB and other known prediction biomarkers for ICI response. This study suggests that TIGS is an effective tumor-inherent biomarker for ICI-response prediction. In the last decade a new kind of cancer therapy, called immunotherapy, has changed how doctors treat cancer patients. These therapies mean that previously incurable cancers, including some skin and lung cancers, can now sometimes be cured. Immunotherapy does this by activating the patient’s own immune system so that it will attack the cancer cells. But for this to work, the cancer cells, much like invading bacteria or viruses, need to be recognized as foreign. Cancer cells contain many DNA mutations that cause the cell to make mutated proteins it would not normally make. These proteins betray the cancer cells as foreign to the immune system. The extent to which cancer cells make mutated proteins – also called the ‘tumor mutational burden’ – can sometimes predict whether a patient will respond to immunotherapy. In general, patients with a high mutational burden respond well to immunotherapy, but overall fewer than one in five cancer patients are cured by this treatment. An important question is whether there are better ways of predicting if a cancer patient will respond to immunotherapy. Wang et al. have addressed this problem by adding a second variable to the prediction. Not only do cancer cells have to make mutated proteins, but these proteins also have to be ‘seen’ by immune cells. Cancer cells, like normal cells, have mechanisms to present protein fragments to immune cells. Wang et al. hypothesized that patients with a high mutational burden would not respond to immunotherapy if they were lacking the machinery required for presenting protein fragments. The experiments revealed that measuring both tumor mutational burden and the levels of the machinery that presents protein fragments resulted in better predictions of patients’ responses to immunotherapy than measuring tumor mutational burden alone. Additionally, this new way of predicting responses to immunotherapy was successful across many different cancer types. The combined measurement of these two variables could be applied in clinical practice as a way to predict cancer patients’ response to immunotherapy. This should allow doctors to determine which course of treatment will work best for a specific patient. The results also suggest that inducing tumor cells to produce more of the machinery that presents protein fragments to the immune system could increase their responsiveness to immunotherapy. In the future, predicting how well a patient will respond to immunotherapy could become even more accurate by incorporating additional variables.

Non-small cell lung cancer (NSCLC) is known to carry heavy mutation load. Besides smoking, cytidine deaminase APOBEC3B plays a key role in the mutation process of NSCLC. APOBEC3B is also reported to be upregulated and predicts bad prognosis in NSCLC. However, targeting APOBEC3B high NSCLC is still a big challenge. Here we show that APOBEC3B upregulation is significantly associated with immune gene expression, and APOBEC3B expression positively correlates with known immunotherapy response biomarkers, including: PD-L1 expression and T-cell infiltration in NSCLC. Importantly, APOBEC mutational signature is specifically enriched in NSCLC patients with durable clinical benefit after immunotherapy and APOBEC mutation count can be better than total mutation in predicting immunotherapy response. In together, this work provides evidence that APOBEC3B upregulation and APOBEC mutation count can be used as novel predictive markers in guiding NSCLC checkpoint blockade immunotherapy.

Sex differences in innate and adaptive immune responses are known, and women generally mount a stronger immune response than men. Cancer immunotherapy, represented by immune checkpoint inhibitors (ICIs), has revolutionized the treatment of cancer, and sex differences in cancer immunotherapy are just starting to be revealed. Here, we summarize recent research progress concerning sex differences in cancer immunotherapy efficacy. On their own, ICIs tend to be more effective in male cancer patients compared with female patients, while ICIs combined with chemotherapy tend to be more effective in female patients than male patients. Male tumors are usually more antigenic than female tumors, and this is reflected by their increased number of tumor mutations and cancer germline antigens. The biomarker tumor mutational burden (TMB), which reflects tumor antigenicity, is more effective at predicting immunotherapy response for female lung cancer patients than for male patients. In this review, we propose different therapeutic strategies for the different sexes: For male cancer patients, the immune environment should be enhanced, whereas for female cancer patients, tumor antigenicity should be enhanced.

Inconsistent investigations of the risk factors for all-cause mortality in patients undergoing peritoneal dialysis (PD) were reported. The present meta-analysis aimed to assess the impact of some clinical characteristics on the risk of mortality in PD patients. PubMed and Embase were systematically searched for studies evaluating the risk factors for all-cause mortality in PD patients. Hazard ratio (HR) and 95% confidence interval (CI) were derived using a random-effect or fixed-effect model considering the heterogeneity across studies. A total of 26 studies were included in this meta-analysis in accordance with the inclusion and exclusion criteria. Age, primary cardiovascular diseases, diabetes mellitus, and high level of alkaline phosphatase showed significant positive associations with elevated risk of all-cause and cardiovascular mortality in PD patients, while hemoglobin acted as a benefit factor. Furthermore, early onset of peritonitis, high peritoneal transport status, elevated body mass index and high-sensitivity C-reactive protein could also considerably increase the risk of all-cause mortality. The absolute serum level of magnesium, potassium, and uric acid required to improve survival in PD patients should be verified further. Multiple factors could affect the risk of mortality in PD patients.

Multi-sensor defect detection technology is a research hotspot for monitoring the powder bed fusion (PBF) processes, of which the quality of the captured defect images and the detection capability is the vital issue. Thus, in this study, we utilize visible information as well as infrared imaging to detect the defects in PBF parts that conventional optical inspection technologies cannot easily detect. A multi-source image acquisition system was designed to simultaneously acquire brightness intensity and infrared intensity. Then, a multi-sensor image fusion method based on finite discrete shearlet transform (FDST), multi-scale sequential toggle operator (MSSTO), and an improved pulse-coupled neural networks (PCNN) framework were proposed to fuse information in the visible and infrared spectra to detect defects in challenging conditions. The image fusion performance of the proposed method was evaluated with different indices and compared with other fusion algorithms. The experimental results show that the proposed method achieves satisfactory performance in terms of the averaged information entropy, average gradient, spatial frequency, standard deviation, peak signal-to-noise ratio, and structural similarity, which are 7.979, 0.0405, 29.836, 76.454, 20.078 and 0.748, respectively. Furthermore, the comparison experiments indicate that the proposed method can effectively improve image contrast and richness, enhance the display of image edge contour and texture information, and also retain and fuse the main information in the source image. The research provides a potential solution for defect information fusion and characterization analysis in multi-sensor detection systems in the PBF process.

Genome alteration signatures reflect recurring patterns caused by distinct endogenous or exogenous mutational events during the evolution of cancer. Signatures of single base substitution (SBS) have been extensively studied in different types of cancer. Copy number alterations are important drivers for the progression of multiple cancer. However, practical tools for studying the signatures of copy number alterations are still lacking. Here, a user-friendly open source bioinformatics tool “sigminer” has been constructed for copy number signature extraction, analysis and visualization. This tool has been applied in prostate cancer (PC), which is particularly driven by complex genome alterations. Five copy number signatures are identified from human PC genome with this tool. The underlying mutational processes for each copy number signature have been illustrated. Sample clustering based on copy number signature exposure reveals considerable heterogeneity of PC, and copy number signatures show improved PC clinical outcome association when compared with SBS signatures. This copy number signature analysis in PC provides distinct insight into the etiology of PC, and potential biomarkers for PC stratification and prognosis.

The effects of geographic distance and technological distance on knowledge spillovers have been well acknowledged in the literature, but the two distances were mostly discussed in parallel. Taking advantage of the context of multilocation firms in technology clusters, we consider the joint role of geographic and technological distances by focusing on a salient feature of clusters: firms’ concern about knowledge appropriation. Specifically, we analyze how a firm’s choice of technological distance from local entities is affected by the competitive environment in the cluster as well as its intra-firm technological structure across clusters. Empirical findings based on the global semiconductors industry support our theory that, with stronger local competition, an innovating entity will increase its technological distance from neighbors to reduce unintended knowledge spillovers. However, the technological distance is smaller when the entity is part of a multilocation firm, has a larger technological distance from other locations within the firm and has stronger intra-firm integration across locations. We argue that these features limit knowledge spillovers and, hence, reduce firms’ reliance on technological distance as a strategy for knowledge appropriation.

The applications of silica-based glass have evolved alongside human civilization for thousands of years. High-precision manufacturing of three-dimensional (3D) fused silica glass objects is required in various industries, ranging from everyday life to cutting-edge fields. Advanced 3D printing technologies have emerged as a potent tool for fabricating arbitrary glass objects with ultimate freedom and precision. Stereolithography and femtosecond laser direct writing respectively achieved their resolutions of ~50 μm and ~100 nm. However, fabricating glass structures with centimeter dimensions and sub-micron features remains challenging. Presented here, our study effectively bridges the gap through engineering suitable materials and utilizing one-photon micro-stereolithography (OμSL)-based 3D printing, which flexibly creates transparent and high-performance fused silica glass components with complex, 3D sub-micron architectures. Comprehensive characterizations confirm that the final material is stoichiometrically pure silica with high quality, defect-free morphology, and excellent optical properties. Homogeneous volumetric shrinkage further facilitates the smallest voxel, reducing the size from 2.0 × 2.0 × 1.0 μm 3 to 0.8 × 0.8 × 0.5 μm 3 . This approach can be used to produce fused silica glass components with various 3D geometries featuring sub-micron details and millimetric dimensions. This showcases promising prospects in diverse fields, including micro-optics, microfluidics, mechanical metamaterials, and engineered surfaces. 3D-printed glass holds great potential. However, it is challenging to control both the dimension and the resolution of the printed material. Here, authors present a one-photon 3D printing approach to produce high-performance fused silica glass with sub-micron resolution and millimetric dimensions.

Hydrogen sulfide (H 2 S), an endogenous gaseous signaling transmitter, has gained recognition for its physiological effects. In this review, we aim to summarize and discuss existing studies about the roles of H 2 S in renal functions and renal disease as well as the underlying mechanisms. H 2 S is mainly produced by four pathways, and the kidneys are major H 2 S-producing organs. Previous studies have shown that H 2 S can impact multiple signaling pathways via sulfhydration. In renal physiology, H 2 S promotes kidney excretion, regulates renin release and increases ATP production as a sensor for oxygen. H 2 S is also involved in the development of kidney disease. H 2 S has been implicated in renal ischemia/reperfusion and cisplatin-and sepsis-induced kidney disease. In chronic kidney diseases, especially diabetic nephropathy, hypertensive nephropathy and obstructive kidney disease, H 2 S attenuates disease progression by regulating oxidative stress, inflammation and the renin-angiotensin-aldosterone system. Despite accumulating evidence from experimental studies suggesting the potential roles of H 2 S donors in the treatment of kidney disease, these results need further clinical translation. Therefore, expanding the understanding of H 2 S can not only promote our further understanding of renal physiology but also lay a foundation for transforming H 2 S into a target for specific kidney diseases.