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Although polymerase chain reaction (PCR) is the most widely used method for DNA amplification, the requirement of thermocycling limits its non-laboratory applications. Isothermal DNA amplification techniques are hence valuable for on-site diagnostic applications in place of traditional PCR. Here we describe a true isothermal approach for amplifying and detecting double-stranded DNA based on a CRISPR–Cas9-triggered nicking endonuclease-mediated Strand Displacement Amplification method (namely CRISDA). CRISDA takes advantage of the high sensitivity/specificity and unique conformational rearrangements of CRISPR effectors in recognizing the target DNA. In combination with a peptide nucleic acid (PNA) invasion-mediated endpoint measurement, the method exhibits attomolar sensitivity and single-nucleotide specificity in detection of various DNA targets under a complex sample background. Additionally, by integrating the technique with a Cas9-mediated target enrichment approach, CRISDA exhibits sub-attomolar sensitivity. In summary, CRISDA is a powerful isothermal tool for ultrasensitive and specific detection of nucleic acids in point-of-care diagnostics and field analyses. Isothermal DNA amplification techniques are useful for diagnostic applications in place of traditional PCR. Here the authors describe CRISDA, which combines CRISPR–Cas9 with strand displacement amplification and exhibits attomolar sensitivity and single-nucleotide specificity in DNA detection.

The treatment of bone defects remains a significant challenge to be solved clinically. Immunomodulatory properties of orthopedic biomaterials have significance in regulating osteoimmune microenvironment for osteogenesis. A lactic acid‐co‐glycolic acid (PLGA) scaffold incorporates black phosphorus (BP) fabricated by 3D printing technology to investigate the effect of BP on osteoimmunomodulation and osteogenesis in site. The PLGA/BP scaffold exhibits suitable biocompatibility, biodegradability, and mechanical properties as an excellent microenvironment to support new bone formation. The studies' result also demonstrate that the PLGA/BP scaffolds are able to recruit and stimulate macrophages M2 polarization, inhibit inflammation, and promote human bone marrow mesenchymal stem cells (hBMSCs) proliferation and differentiation, which in turn promotes bone regeneration in the distal femoral defect region of steroid‐associated osteonecrosis (SAON) rat model. Moreover, it is screened and demonstrated that PLGA/BP scaffolds can promote osteogenic differentiation by transcriptomic analysis, and PLGA/BP scaffolds promote osteogenic differentiation and mineralization by activating PI3K‐AKT signaling pathway in hBMSC cells. In this study, it is shown that the innovative PLGA/BP scaffolds are extremely effective in stimulating bone regeneration by regulating macrophage M2 polarization and a new strategy for the development of biomaterials that can be used to repair bone defects is offered.

Photothermal therapy (PTT) offers many advantages such as high efficiency and minimal invasiveness, but clinical adoption of PTT nanoagents have been stifled by unresolved concerns such as the biodegradability as well as long-term toxicity. Herein, poly (lactic-co-glycolic acid) (PLGA) loaded with black phosphorus quantum dots (BPQDs) is processed by an emulsion method to produce biodegradable BPQDs/PLGA nanospheres. The hydrophobic PLGA not only isolates the interior BPQDs from oxygen and water to enhance the photothermal stability, but also control the degradation rate of the BPQDs. The in vitro and in vivo experiments demonstrate that the BPQDs/PLGA nanospheres have inappreciable toxicity and good biocompatibility, and possess excellent PTT efficiency and tumour targeting ability as evidenced by highly efficient tumour ablation under near infrared (NIR) laser illumination. These BP-based nanospheres combine biodegradability and biocompatibility with high PTT efficiency, thus promising high clinical potential. Black phosphorus is a biodegradable 2D material that has attracted growing interest in biomedicine. Here, the authors carry out in vitro and in vivo experiments to demonstrate that nanospheres loaded with black-phosphorus quantum dots perform as promising phothermal cancer therapy agents.

To help doctors and patients evaluate lumbar intervertebral disc degeneration (IVDD) accurately and efficiently, we propose a segmentation network and a quantitation method for IVDD from T2MRI. A semantic segmentation network (BianqueNet) composed of three innovative modules achieves high-precision segmentation of IVDD-related regions. A quantitative method is used to calculate the signal intensity and geometric features of IVDD. Manual measurements have excellent agreement with automatic calculations, but the latter have better repeatability and efficiency. We investigate the relationship between IVDD parameters and demographic information (age, gender, position and IVDD grade) in a large population. Considering these parameters present strong correlation with IVDD grade, we establish a quantitative criterion for IVDD. This fully automated quantitation system for IVDD may provide more precise information for clinical practice, clinical trials, and mechanism investigation. It also would increase the number of patients that can be monitored. Globally, as a major public health problem, low back pain has been the leading cause of disability worldwide for the past 30 years. Here, the authors propose a segmentation network and a quantitative method lumbar intervertebral disc degeneration assessment.

Esophageal cancer (EC) is a type of aggressive cancer without clinically relevant molecular subtypes, hindering the development of effective strategies for treatment. To define molecular subtypes of EC, we perform mass spectrometry-based proteomic and phosphoproteomics profiling of EC tumors and adjacent non-tumor tissues, revealing a catalog of proteins and phosphosites that are dysregulated in ECs. The EC cohort is stratified into two molecular subtypes—S1 and S2—based on proteomic analysis, with the S2 subtype characterized by the upregulation of spliceosomal and ribosomal proteins, and being more aggressive. Moreover, we identify a subtype signature composed of ELOA and SCAF4, and construct a subtype diagnostic and prognostic model. Potential drugs are predicted for treating patients of S2 subtype, and three candidate drugs are validated to inhibit EC. Taken together, our proteomic analysis define molecular subtypes of EC, thus providing a potential therapeutic outlook for improving disease outcomes in patients with EC. Proteomics can aid in the identification of molecular subtypes in cancers. Here, the authors perform proteomic profiling of 124 paired oesophageal cancer and adjacent non-tumour tissues and identify two subtypes that are associated with patient survival for therapeutic targeting.

Due to the small defect areas and indistinct features on industrial components, detecting surface defects with high accuracy remains challenging, often leading to false detections. To address these issues, this paper proposes an improved YOLOv5n algorithm for industrial surface defect detection. The main improvements are as follows: the DSConv-CA module in the backbone network enhances the feature extraction capability, the Gold-YOLO structure replaces the original PANet structure in the neck to improve information fusion, and the SIoU loss function is adopted to replace the regression loss, further improving detection accuracy. Experimental results demonstrate that the improved YOLOv5n algorithm achieves a mean average precision of 75.3% on the NEU-DET dataset, which is 4.3% higher than the original model.

Background The non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) serves as a novel composite lipid indicator for atherosclerosis. However, the association between NHHR and mortality in patients with diabetes or prediabetes remains unclear. Consequently, the objective of this study was to investigate the relationship between NHHR and both all-cause and cardiovascular mortality in US adults with diabetes or prediabetes. Methods This study included 12,578 adult participants with diabetes or prediabetes from the US National Health and Nutrition Examination Survey (1999–2018). Mortality outcomes were ascertained by linking to the National Death Index (NDI) record up to December 31, 2019. We employed a weighted multivariate Cox proportional hazards model and restricted cubic splines to assess the associations between NHHR and all-cause and cardiovascular mortality. A segmented Cox proportional hazards model was used for evaluating threshold effects. Furthermore, a competing risks analysis was performed to explore the relationship between NHHR and cardiovascular mortality. Results During a median follow-up period of 8.08 years, 2403 participants encountered all-cause mortality, with 662 of them specifically succumbing to cardiovascular mortality. The restricted cubic splines revealed a U-shaped association between NHHR and all-cause mortality, while an L-shaped association was observed for cardiovascular mortality. The analysis of threshold effects revealed that the inflection points for NHHR and all-cause and cardiovascular mortality were 2.72 and 2.83, respectively. Specifically, when the baseline NHHR was below the inflection points, a negative correlation was observed between NHHR and both all-cause mortality (HR: 0.76, 95% CI: 0.68–0.85) and cardiovascular mortality (HR: 0.70, 95% CI: 0.57–0.85). Conversely, when the baseline NHHR exceeded the inflection points, a positive correlation was observed between NHHR and both all-cause mortality (HR: 1.11, 95% CI: 1.06–1.16) and cardiovascular mortality (HR: 1.08, 95% CI: 1.00–1.16). Conclusions Among US adults with diabetes or prediabetes, a U-shaped association was observed between NHHR and all-cause mortality, whereas an L-shaped association was identified with cardiovascular mortality. The inflection points for all-cause and cardiovascular mortality were 2.72 and 2.83, respectively.

We present a detailed report of the connection between long-duration gamma-ray bursts (GRBs) and their accompanying supernovae (SNe). The discussion presented here places emphasis on how observations, and the modelling of observations, have constrained what we know about GRB-SNe. We discuss their photometric and spectroscopic properties, their role as cosmological probes, including their measured luminosity–decline relationships, and how they can be used to measure the Hubble constant. We present a statistical summary of their bolometric properties and use this to determine the properties of the “average” GRB-SN. We discuss their geometry and consider the various physical processes that are thought to power the luminosity of GRB-SNe and whether differences exist between GRB-SNe and the SNe associated with ultra-long-duration GRBs. We discuss how observations of their environments further constrain the physical properties of their progenitor stars and give a brief overview of the current theoretical paradigms of their central engines. We then present an overview of the radioactively powered transients that have been photometrically associated with short-duration GRBs, and we conclude by discussing what additional research is needed to further our understanding of GRB-SNe, in particular the role of binary-formation channels and the connection of GRB-SNe with superluminous SNe.

Analyzing scattered wave to recognize object is of fundamental significance in wave physics. Recently-emerged deep learning technique achieved great success in interpreting wave field such as in ultrasound non-destructive testing and disease diagnosis, but conventionally need time-consuming computer postprocessing or bulky-sized diffractive elements. Here we theoretically propose and experimentally demonstrate a purely-passive and small-footprint meta-neural-network for real-time recognizing complicated objects by analyzing acoustic scattering. We prove meta-neural-network mimics a standard neural network despite its compactness, thanks to unique capability of its metamaterial unit-cells (dubbed meta-neurons) to produce deep-subwavelength phase shift as training parameters. The resulting device exhibits the “intelligence” to perform desired tasks with potential to overcome the current limitations, showcased by two distinctive examples of handwritten digit recognition and discerning misaligned orbital-angular-momentum vortices. Our mechanism opens the route to new metamaterial-based deep-learning paradigms and enable conceptual devices automatically analyzing signals, with far-reaching implications for acoustics and related fields. The authors present a passive meta-neural-network for real-time recognition of objects by analysis of acoustic scattering. It consists of unit cells termed meta-neurons, mimicking an analogous neural network for classical waves, and is shown to recognise handwritten digits and misaligned orbital-angular-momentum vortices.

Photothermal therapy (PTT) is a fledgling therapeutic strategy for cancer treatment with minimal invasiveness but clinical adoption has been stifled by concerns such as insufficient biodegradability of the PTT agents and lack of an efficient delivery system. Here, black phosphorus (BP) nanosheets are incorporated with a thermosensitive hydrogel [poly(d,l‐lactide)‐poly(ethylene glycol)‐poly(d,l‐lactide) (PDLLA‐PEG‐PDLLA: PLEL)] to produce a new PTT system for postoperative treatment of cancer. The BP@PLEL hydrogel exhibits excellent near infrared (NIR) photothermal performance and a rapid NIR‐induced sol–gel transition as well as good biodegradability and biocompatibility in vitro and in vivo. Based on these merits, an in vivo PTT postoperative treatment strategy is established. Under NIR irradiation, the sprayed BP@PLEL hydrogel enables rapid gelation forming a gelled membrane on wounds and offers high PTT efficacy to eliminate residual tumor tissues after tumor removal surgery. Furthermore, the good photothermal antibacterial performance prevents infection and this efficient and biodegradable PTT system is very promising in postoperative treatment of cancer. A sprayable and biodegradable photothermal therapy (PTT) system composed of a thermosensitive hydrogel incorporated with black phosphorus (BP) nanosheets is presented for post‐surgical treatment of cancer. The obtained hydrogel enables rapid gelation and offers high PTT efficacy to eliminate residual tumor after surgery. This efficient and biodegradable PTT system is very promising in the postoperative treatment of cancer.