Patients with Parkinson's Disease (PD) have a distinctive body odor, which was first described by a patient's wife as musky and strong. Later analysis of sebum of patients with PD revealed four volatile organic compounds (VOC) (perillic aldehyde, hippuric acid, eicosane, octadecanal), that differed from healthy subjects, and the patient's wife confirmed that three of them smelled like patients with PD. However, it is unclear whether other people can also perceive this PD body odor and whether it can be artificially recreated. Hence, we aimed to systematically assess whether young women can perceive the PD body odor and whether they can discriminate between the PD body odor and the "artificial PD odor" composed of the four VOCs mentioned above. T-shirts were collected from 19 people with idiopathic PD and 15 age- and gender-matched healthy participants to represent the PD body odor and the healthy body odor, respectively. The four VOCs were diluted in 1,2-propanediol to prepare the artificial PD body odor. Body odors were rated by 26 young women. PD body odor was perceived as more musty, strong, smelly, and unpleasant compared to healthy and artificial PD body odor. Furthermore, around 80 % of women were able to discriminate PD body odor from artificial PD body odor. Overall, this study confirmed a distinctive body odor quality of patients with PD, which can be perceived by young women. However, the four VOCs, composing the artificial PD body odor, were insufficient to reproduce the body odor from PD patients.

Digital twin (DT) technology enables virtual replicas that support monitoring, simulation, and prediction for personalized care. A machine learning (ML)‐driven DT framework for a field‐effect transistor (FET)‐based hormone biosensor targeting 17 β ‐estradiol is presented, intended for future integration into a Smart Health Monitoring System (SHMS) for infertility treatment. Real‐time biosensor recordings acquired under sequential estradiol exposures (0, 20, 150, 200, and 400 pg/mL) were processed using a protocol‐aware pipeline. A row‐level learning strategy was adopted, in which each timepoint was treated as an individual training sample and the normalized biosensor signal was enhanced with local temporal descriptors. Hormone concentration was formulated as a supervised regression target transformed as log ₁₀ ( ro + 1) and evaluated using leave‐one‐biosensor‐out (LOBO) validation with out‐of‐fold (OOF) predictions. Among the evaluated models, CatBoost achieved the best LOBO performance (RMSE _log = 0.240; = 0.935), corresponding to an root mean squared error of 85.28 pg/mL in the original concentration domain. SHAP analysis indicated that prediction relied primarily on temporal and signal‐morphology features, suggesting that physically meaningful response dynamics were learned rather than sensor‐specific offsets. Finally, a one‐step‐ahead forward model was applied autoregressively to generate protocol‐consistent synthetic I _sd ( t ) signal, demonstrating the feasibility of DT‐based signal simulation from real biosensor data. While the results are promising, validation on larger independent datasets and under physiologically complex conditions remains necessary to establish robustness and clinical readiness.

Accurate monitoring of reproductive hormones is essential for the diagnosis and for improving treatment outcomes in infertility, polycystic ovary syndrome (PCOS), and other endocrine disorders. Conventional methods such as enzyme‐linked immunosorbent assay (ELISA) are reliable but often invasive, time‐consuming, and hence unsuitable for real‐time or point‐of‐care applications. We present a silicon nanonet‐based field‐effect transistor (BioFET) functionalized with DNA aptamers as a nano‐biosensing platform for discrete and real‐time detection of 17β estradiol (E2)—a key biomarker in reproductive health. The biosensor exhibits concentration‐dependent changes in drain–source current (I _SD ) across E2 levels of 20–400 pg/mL in phosphate‐buffered saline, reflecting physiologically relevant conditions. Minimal responses to progesterone and testosterone (200 pg/mL) confirm high selectivity. Analysis of the transfer characteristics shows a consistent increase in I _DS at fixed gate voltage with rising E2 concentration, while fluorescence microscopy verifies spatially controlled aptamer immobilization. The latter is achieved through microcontact printing, which enables patterned deposition of aptamers onto the 3‐triethoxysilylpropylsuccinic anhydride (TESPSA)‐functionalized silicon surface with high spatial precision. Real‐time monitoring at the gate voltage (V _G ) of 1.5 V enables dynamic tracking of hormone levels and partial signal reversibility after NaCl‐induced dissociation. Machine learning (ML) models applied to the time‐series biosensor data enable accurate prediction of E2 concentrations. These results emphasize the high potential of aptamer‐based BioFETs, particularly when integrated with data‐driven analysis, for non‐invasive and real‐time hormone monitoring in fertility care.

Thyroxine (T4) is a key hormone regulating metabolic, cardiovascular, and neurodevelopmental processes, yet its clinical quantification still relies on centralized immunoassays that limit rapid or point-of-care monitoring. Here, we present a label-free biosensing platform based on silicon nanowire field-effect transistors (SiNW-FETs) functionalized with a T4-selective DNA aptamer via a 3-Triethoxysilyl propylsuccinic Anhydride (TESPSA)-mediated silanization approach, enabling a streamlined two-step modification for oriented immobilization. The biosensor achieves robust real-time detection of T4 across the physiological concentration range (5–30 pM), with a limit of detection of ~5 pM and a strong linear correlation between drain current and analyte concentration (R² = 0.9931). Specificity is confirmed using non-functionalized devices and estradiol as a non-target control. All measurements were performed in undiluted phosphate-buffered saline, representing a physiologically relevant ionic environment and demonstrating stable sensor performance under realistic buffer conditions. The dose–response behavior follows a Hill model, allowing extraction of binding parameters and confirming that the electrical signal originates from specific aptamer–target interactions. These results demonstrate that aptamer-functionalized SiNW-FETs provide a highly sensitive, selective, and miniaturizable platform for quantitative thyroid hormone monitoring, with strong potential for future point-of-care applications.

The SARS-CoV-2 pandemic has triggered many studies worldwide in the area of biosensors, leading to innovative approaches for the quantitative assessment of COVID-19. A nanostructured field-effect transistor (FET) is one type of device shown to be ultrasensitive for virus determination. FETs can be used as transducers to analyze changes in electrical current caused by the bonding of viral molecules to the surface of the semiconducting nanomaterial layer of the FETs. Although nano-transistors require simple setups amenable to be miniaturized for point-of-care diagnostic of COVID-19, this type of sensor usually has limited sensitivity in biological fluids. The reason behind this is the shortened screening length in the presence of high ionic strength solutions. In the frame of this study, we propose a methodology consisting of the FET surface modification with a hydrogel based on the star-shaped polyethylene glycol (starPEG), which hosts specific antibodies against SARS-CoV-2 spike protein in its porous structure. The deposition of the hydrogel increases the effective Debye length, preserving the biosensor’s sensitivity. We demonstrate the capability of silicon nanonet-based FETs to detect viral antigens and cultured viral particles in phosphate-buffered saline (PBS) as well as in human-purified saliva. Finally, we discriminated between positive and negative patients’ nasopharyngeal swab samples.