Signal readout technologies that do not require any instrument are essential for improving the convenience and availability of paper-based sensors. Thanks to the remarkable progress in material science and nanotechnology, paper-based sensors with instrument-free signal readout have been developed for multiple purposes, such as biomedical detection, environmental pollutant tracking, and food analysis. In this review, the developments in instrument-free signal readout technologies for paper-based sensors from 2020 to 2023 are summarized. The instrument-free signal readout technologies, such as distance-based signal readout technology, counting-based signal readout technology, text-based signal readout technology, as well as other transduction technologies, are briefly introduced, respectively. On the other hand, the applications of paper-based sensors with instrument-free signal readout technologies are summarized, including biomedical analysis, environmental analysis, food analysis, and other applications. Finally, the potential and difficulties associated with the advancement of paper-based sensors without instruments are discussed.

Obesity has emerged as a growing global public health concern over recent decades. Obesity prevalence exhibits substantial global variation, ranging from less than 5% in regions like China, Japan, and Africa to rates exceeding 75% in urban areas of Samoa. To examine the involvement of metabolism-related genes. Gene expression datasets GSE110729 and GSE205668 were accessed from the GEO database. DEGs between obese and lean groups were identified through DESeq2. Metabolism-related genes and pathways were detected using enrichment analysis, WGCNA, Random Forest, and XGBoost. The identified signature genes were validated by real-time quantitative PCR (qRT-PCR) in mouse models. A total of 389 genes exhibiting differential expression were discovered, showing significant enrichment in metabolic pathways, particularly in the propanoate metabolism pathway. The orangered4 module, which exhibited the highest correlation with propanoate metabolism, was identified using Weighted Correlation Network Analysis (WGCNA). By integrating the DEGs, WGCNA results, and machine learning methods, the identification of two metabolism-related genes, Storkhead Box 1 (STOX1), NACHT and WD repeat domain-containing protein 2(NWD2) was achieved. These signature genes successfully distinguished between obese and lean individuals. qRT-PCR analysis confirmed the downregulation of STOX1 and NWD2 in mouse models of obesity. This study has analyzed the available GEO dataset in order to identify novel factors associated with obesity metabolism and found that STOX1 and NWD2 may serve as diagnostic biomarkers.

Point-of-care testing (POCT) is a contemporary diagnostic approach characterized by its user-friendly nature, cost efficiency, environmental compatibility, and lack of reliance on professional experts. Therefore, it is widely used in clinical diagnosis and other analytical testing fields to meet the demand for rapid and convenient testing. The application of POCT technology not only improves testing efficiency, but also brings convenience and benefits to the healthcare industry. The personal glucose meter (PGM) is a highly successful commercial POCT tool that has been widely used not only for glucose analysis, but also for non-glucose target detection. In this review, the recent advances from 2020 to 2024 in non-glucose target analysis for PGMs as POCT devices are summarized. The signal transduction strategies for non-glucose target analysis based on PGMs, including enzymatic transduction, nanocarrier transduction (enzyme or glucose), and glucose consumption transduction are briefly introduced. Meanwhile, the applications of PGMs in non-glucose target analysis are outlined, encompassing biomedical, environmental, and food analysis, along with other diverse applications. Finally, the prospects of and obstacles to employing PGMs as POCT tools for non-glucose target analysis are discussed.

Introduction: Intestinal dysbiosis, characterized by reduced diversity and enrichment of pro-inflammatory taxa, is implicated in the pathogenesis of polycystic ovary syndrome (PCOS). Ramulus mori (Sangzhi) alkaloids (SZ-A), approved in China for type 2 diabetes with broad metabolic effects, remain untested as a microbiota-targeted intervention for PCOS. Methods: In a dehydroepiandrosterone (DHEA)-induced rat model of PCOS, we evaluated the therapeutic efficacy of SZ-A and its underlying microbiota–metabolite interactions through integrated assessments of reproductive and endocrine–metabolic function, oxidative stress, inflammatory cytokines, and gut microbiota and serum metabolite profiles. Results: Relative to SD rats, PCOS rats showed approximately 10-fold higher cystic follicle burden and a one-third reduction in corpora lutea, with serum testosterone rising from 0.12 ± 0.08 to 0.27 ± 0.08 ng/mL, total bile acids falling from 34.22 ± 5.52 to 20.63 ± 4.94 μM, and HOMA-IR significantly increased (all p < 0.05). SZ-A treatment reduced cystic follicles, restored estrous cyclicity and luteal formation, and shifted testosterone, total bile acids, and HOMA-IR toward SD levels. At the molecular level, SZ-A appears to act by remodeling gut microbiota composition and serum metabolite profiles. SZ-A significantly shifted microbial β-diversity in PCOS rats while retaining a community dominated by Bacteroidetes and Firmicutes with Lactobacillus and Treponema _2 as key genera. Untargeted metabolomics identified 13 PCOS-associated serum metabolites that were significantly reduced after SZ-A treatment (p < 0.05), highlighting fenoldopam as a putative mediator of its beneficial effects on ovarian function and metabolic homeostasis. With respect to oxidative injury, serum malondialdehyde (MDA) levels in PCOS rats were approximately twice those of the SD group, while total antioxidant capacity (T-AOC) and the activities of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) were significantly reduced (p < 0.05); treatment with SZ-A markedly attenuated these alterations (p < 0.05). Besides, it suppressed systemic inflammation by reducing interleukin-6 (IL-6), interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α) levels in serum and relevant tissues (p < 0.05). Discussion: Collectively, these findings indicate that SZ-A alleviates PCOS by attenuating intestinal oxidative stress and normalizing gut microbiota–metabolite interactions, and highlight fenoldopam as a potential effector, supporting SZ-A as a promising therapeutic candidate for PCOS.

As one of the biomarkers of coagulation system-related diseases, the detection of thrombin is of practical importance. Thus, this study developed a portable biosensor based on a personal glucometer for rapid detection of thrombin activity. Fibrinogen was used for the detection of thrombin, and the assay principle was inspired by the blood coagulation process, where thrombin hydrolyzes fibrinogen to produce a fibrin hydrogel, and the amount of invertase encapsulated in the fibrin hydrogel fluctuates in accordance with the activity of thrombin in the sample solution. The quantitative assay is conducted by measuring the amount of unencapsulated invertase available to hydrolyze the substrate sucrose, and the signal readout is recorded using a personal glucometer. A linear detection range of 0–0.8 U/mL of thrombin with a limit of detection of 0.04 U/mL was obtained based on the personal glucometer sensing platform. The results of the selectivity and interference experiments showed that the developed personal glucometer sensing platform is highly selective and accurate for thrombin activity. Finally, the reliability of the portable glucometer method for rapid thrombin detection in serum samples was investigated by measuring the recovery rate, which ranged from 92.8% to 107.7%. In summary, the fibrin hydrogel sensing platform proposed in this study offers a portable and versatile means for detecting thrombin using a personal glucometer. This approach not only simplifies the detection process, but also eliminates the need for large instruments and skilled operators, and substantially reduces detection costs.

Introduction: Excess adiposity drives adipose tissue dysfunction and metabolic disease, including type 2 diabetes and cardiovascular disorders. Building on our prior GEO-based analyses that nominated Storkhead box 1 (STOX1) as a candidate biomarker of obesity-related adipose inflammation, we compared circulating STOX1 between individuals with normal weight and those with overweight/obesity and examined its metabolic correlates. Methods: In 476 volunteers, we quantified serum STOX1, adipokines, and clinical parameters; we further contrasted STOX1 and adiponectin between groups and performed an oral glucose tolerance test (OGTT) to assess glycemic effects on STOX1. Results: Serum STOX1 was significantly lower in overweight/obese (OW/OB) participants than controls (controls: 1.27 ± 1.40 µg/L; OW/OB: 0.69 ± 0.77 µg/L; p < 0.001), accompanied by reduced adiponectin in OW/OB. Partial correlations showed inverse associations of STOX1 with metabolic and inflammatory indices and a positive association with adiponectin. In multivariable linear regression, systolic blood pressure (SBP), tumor necrosis factor-alpha (TNF-α), and body fat percentage independently predicted STOX1 (Y_STOX1 = 2.569 – 0.110 × SBP – 0.272 × TNF-α – 0.106 × body fat %; R = 0.37, R ² = 0.13). Discussion: During OGTT, hyperglycemia suppressed circulating STOX1, suggesting glucose-dependent regulation of its secretion/release. Collectively, these findings indicate that decreased serum STOX1 tracks adverse metabolic and inflammatory profiles in obesity and support its potential utility as a noninvasive biomarker of adipose tissue inflammation in overweight/obese individuals.