Cardiovascular disease is one of the most common causes of mortality in the world. Most of the diagnostic processes usually require bulky instruments and trained professionals, which cannot meet the demand for fast, early and regular bedside diagnosis. In this paper, a bespoke app on a smartphone and an autonomous capillary microfluidic chip (ACMC) are combined to realize the point-of-care testing of cardiac troponin I (cTnI). The smartphone-app-ACMC platform was based on the sandwich immunofluorescence principle and featured self-aligned on-chip focusing (SOF) lenses which can avoid the complex optical coupling process. The operator only needs to introduce 100 μl sample into the ACMC, where the priming, time-delaying, mixing and washing steps for the assay can be accomplished automatically. With the help of the bespoke app and a palm-sized optical attachment, the smartphone can capture fluorescence images, process fluorescence intensity information, output detection results and save the results for long-term monitoring. Our results showed that within 12 min, the detection limit of 78 pg ml⁻¹ and 94 pg ml⁻¹ for cTnI was attained in buffer and spiked human serum, respectively. Our proposed platform has the potential to be applied in the POCT field especially for some resource-limited settings.

Purpose To determine the association between plasma free fatty acid (FFA) levels and primary angle‐closure glaucoma (PACG). Methods Free fatty acid (FFA) levels in patients with PACG (n = 181) and people without glaucoma (n = 340) were compared. Twenty‐two FFAs and six lipid classes were measured using metabolomics analysis. Odds ratio (OR) of these metabolites and their 95% confidence intervals (95%CI) for PACG were obtained by logistic regression. Stepwise forward selection was performed to identify FFAs that influenced PACG risk. Areas under the curve (AUC) were applied to assess the predictive performance. Spearman’s rank correlation was used to assess the relationship between ocular parameters and FFAs. Results Most FFAs in the PACG group were lower than those in the non‐glaucoma group. Docosahexaenoic acid (DHA; OR for fourth quartile (Q4) vs. first quartile (Q1): 0.32 (0.16–0.66); per standard deviation (SD) increase: 0.64 (0.49–0.83); p for trend: 0.0007) and total saturated fatty acids (SFAs; OR for Q4 versus Q1: 0.27 (0.13–0.56); per SD increase: 0.65 (0.50–0.87); p for trend: 0.0004) were associated with decreased PACG risk. The AUC of the model that included DHA, total SFAs, demographic and ophthalmic factors increased from 0.8230 (0.7811–0.8649) to 0.8512 (0.8133–0.8891) (increased AUC: 0.0282 (0.0112–0.0453); p for increased AUC: 0.0012). Additionally, the cup‐disc ratio had a weak negative correlation with DHA and total SFAs (DHA: r = −0.12085, p = 0.0065; total SFAs: r = −0.13318, p = 0.0024). Conclusions Decrease in FFA levels may be related to lipid peroxidation. Docosahexaenoic acid (DHA) and total SFAs may be screening indices for PACG patients.

Graphene, owing to its exceptional electronic, optical, thermal, and mechanical properties, has emerged as a highly promising material. Currently, the synthesis of large‐area graphene films on metal substrates via chemical vapor deposition remains the predominant approach for producing high‐quality graphene. To realize the potential applications of graphene, it is essential to transfer graphene films to target substrates in a manner that is non‐destructive, clean, and efficient, as this significantly affects the performance of graphene devices. This review examines the current methods for graphene transfer from three perspectives: non‐destructive transfer, clean transfer, and high‐efficiency transfer. It analyzes and compares the advancements and limitations of various transfer techniques. Finally, the review identifies the key challenges faced by current graphene transfer methods and anticipates future developmental prospects.

To address the problem of cooperative work among right-angle coordinate robots in spacecraft structural plate mount tasks, an improved particle swarm optimization (PSO) algorithm was proposed to assign paths to three robots in a surface-mounted technology (SMT) machine. First, the optimization objective of path planning was established by analyzing the working process of the SMT machine. Then, the inertia weight update strategy was designed to overcome the early convergence of the traditional PSO algorithm, and the learning factor of each particle was calculated using fuzzy control to improve the global search capability. To deal with the concentration phenomenon of particles in the iterative process, the genetic algorithm (GA) was introduced when the particles were similar. The particles were divided into elite, high-quality, or low-quality particles according to their performance. New particles were generated through selection and crossover operations to maintain the particle diversity. The performance of the proposed algorithm was verified with the simulation results, which could shorten the planning path and quicken the convergence compared to the traditional PSO or GA. For large and complex maps, the proposed algorithm shortens the path by 7.49% and 11.49% compared to traditional PSO algorithms, and by 3.98% and 4.02% compared to GA.