In this work, a low-cost, portable device is developed to detect colorimetric assays for in-field and point-of-care (POC) analysis. The device can rapidly detect both pH values and nitrite concentrations of five different samples, simultaneously. After mixing samples with specific reagents, a high-resolution digital camera collects a picture of the sample, and a single-board computer processes the image in real time to identify the hue–saturation–value coordinates of the image. An internal light source reduces the effect of any ambient light so the device can accurately determine the corresponding pH values or nitrite concentrations. The device was purposefully designed to be low-cost, yet versatile, and the accuracy of the results have been compared to those from a conventional method. The results obtained for pH values have a mean standard deviation of 0.03 and a correlation coefficient R ² of 0.998. The detection of nitrites is between concentrations of 0.4–1.6 mg l ⁻¹ , with a low detection limit of 0.2 mg l ⁻¹ , and has a mean standard deviation of 0.073 and an R ² value of 0.999. The results represent great potential of the proposed portable device as an excellent analytical tool for POC colorimetric analysis and offer broad accessibility in resource-limited settings.

Cryptosporidium, an intestinal protozoan pathogen, is one of the leading causes of diarrhea in healthy adults and death in children. Detection of Cryptosporidium oocysts has become a high priority to prevent potential outbreaks. In this paper, a label-free interdigitated-based capacitive biosensor has been introduced for the detection of Cryptosporidium oocysts in water samples. Specific anti-Cryptosporidium monoclonal antibodies (IgG3) were covalently immobilized onto interdigitated gold electrodes as the capture probes, and bovine serum albumin was used to avoid non-specific adsorption. The immobilization of the antibodies was confirmed by measuring the change in the contact angle. The detection was achieved by measuring the relative change in the capacitive/dielectric properties due to the formation of Cryptosporidium-antibody complex. The biosensor has been tested for different concentrations of Cryptosporidium. The results show that the biosensor developed can accurately distinguish different numbers of captured cells and densities on the surface of the biosensor. The number of Cryptosporidium oocysts captured on the electrode surface was confirmed using a fluorescein isothiocyanate (FITC) immunofluorescence assay. The response from the developed biosensor has been mainly dependent on the concentration of Cryptosporidium under optimized conditions. The biosensor showed a linear detection range between 15 and 153 cells/mm² and a detection limit of 40 cells/mm². The label-free capacitive biosensor developed has a great potential for detecting Cryptosporidium in environmental water samples. Furthermore, under optimized conditions, this label-free biosensor can be extended for detection of other biomarkers for biomedical and environmental analyses.

Codon optimization is widely used to improve heterologous gene expression in Escherichia coli. However, many existing methods focus primarily on maximizing the codon adaptation index (CAI) and neglect broader aspects of biological context. In this study, we present ColiFormer, a transformer-based codon optimization framework fine-tuned on 3676 high-expression E. coli genes curated from the NCBI database. Built on the CodonTransformer BigBird architecture, ColiFormer employs self-attention mechanisms and a mathematical optimization method (the augmented Lagrangian approach) to balance multiple biological objectives simultaneously, including CAI, GC content, tRNA adaptation index (tAI), RNA stability, and minimization of negative cis-regulatory elements. Based on in silico evaluations on 37,053 native E. coli genes and 80 recombinant protein targets commonly used in industrial studies, ColiFormer demonstrated significant improvements in CAI and tAI values, maintained GC content within biologically optimal ranges, and reduced inhibitory cis-regulatory motifs compared with established codon optimization approaches, while maintaining competitive runtime performance. These results represent computational predictions derived from standard in silico metrics; future experimental work is anticipated to validate these computational predictions in vivo. ColiFormer has been released as an open-source tool alongside the benchmark datasets used in this study.

Recent advances in nanomaterials have profoundly transformed the landscape of biomedical imaging and analytical detection. By leveraging unique physical, chemical, and optical properties at the nanoscale, these materials enable unprecedented sensitivity, resolution, and specificity in both in vitro and in vivo applications. An analysis of the nanomaterials topic collection in Web of Science reveals that although biomedical imaging is not the most dominant domain in the nanomaterials field, it is rapidly growing across many disciplines and applications, including super-resolution nanoscopy, spatial sequencing technology, and immunology. Based on citation network topology and high-level topic modeling, we highlight recent advances in signal enhancement, targeted specificity, and mechanical innovations across multiple modalities, including optical imaging, contrast MRI, cytometry, and spatial sequencing.

Competitive lateral flow assays (cLFAs) have been widely used for decades, yet improvements in sensitivity remain largely empirical. A general physics-based model that predicts assay performance has been missing. We address this gap by developing an analytical framework derived from first principles of transport, competitive binding, and electrostatic interactions. The model provides closed-form expressions for the limit of detection (LOD), identifies the physical parameters that constrain sensitivity, and quantitatively links nanoparticle concentration, antibody loading, and electrostatic exclusion to the measured signal. To demonstrate generality and practical utility, we apply the model to fentanyl detection using gold nanoparticle reporters. Guided by the framework, we reduced antibody loading and minimized non-participating analyte effects, achieving a ∼100-fold improvement in sensitivity over commercial fentanyl tests (LOD ≈ 7 pg ml−1). This analytically tractable formulation offers a basis for predictive performance benchmarking and rational optimization of competitive LFAs and related point-of-care assays.