Nowadays, the diagnosis of Alzheimer’s disease is a complex process that involves several clinical tests. Cerebrospinal fluid contains common Alzheimer-related biomarkers that include amyloid beta 1-42 (Aβ1-42) and tau proteins. In this work, we propose vibrational spectroscopy techniques supported by machine learning for the detection of biomarkers in cerebrospinal fluid that are related with Alzheimer’s by prediction models. Vibrational spectroscopy provides the entire biochemical composition of the body fluid, and thus, small but typical physiological changes related with the pathology can be ascertained. Within a machine learning framework, Raman and FTIR spectra were analyzed, which were taken from samples of healthy volunteers in comparison with samples from patients clinically diagnosed with Alzheimer’s. We find that a logistic regression model can discriminate between healthy control and Alzheimer’s patients with a precision of 98%, when the input for the model combines data from both vibrational spectroscopy methods. Our approach shows high discriminative capabilities and constitutes a proof of concept for an alternative and accurate tool for the diagnosis of Alzheimer’s disease.

Cerebrospinal fluid contains specific biomarkers of Alzheimer’s disease that include amyloid beta peptides and tau proteins. In this work, we present for the first time possible evidence that the formation of the constituents of cerebrospinal fluid during drying is related with Alzheimer’s. We use machine learning to examine optical microscope images of dried cerebrospinal fluid patterns from patients with Alzheimer’s and healthy controls to create a diagnostic model. To analyze the images, the histogram of oriented gradients is used as a feature descriptor. Each image is mapped into the corresponding feature space, and principal component analysis is applied for dimensionality reduction. A machine-learning prediction model with a sensitivity of 82% was built. These promising preliminary results show great potential for new rapid and low-cost diagnostic pathways in the detection of Alzheimer’s disease.

Rising global demand for biodegradable materials and green sources of energy has brought attention to lignin. Herein, we report a method for manufacturing standalone lignin membranes without additives for the first time to date. We demonstrate a scalable method for macroporous (∼100 to 200 nm pores) lignin membrane production using four different organosolv lignin materials under a humid environment (>50% relative humidity) at ambient temperatures (∼20 °C). A range of different thicknesses is reported with densely porous films observed to form if the membrane thickness is below 100 nm. The fabricated membranes were readily used as a template for Ni 2+ incorporation to produce a nickel oxide membrane after UV/ozone treatment. The resultant mask was etched via an inductively coupled plasma reactive ion etch process, forming a silicon membrane and as a result yielding black silicon (BSi) with a pore depth of >1 μm after 3 min with reflectance <3% in the visible light region. We anticipate that our lignin membrane methodology can be readily applied to various processes ranging from catalysis to sensing and adapted to large-scale manufacturing.

Vibrational spectroscopy techniques are widely used in analytical chemistry, physics and biology. The most prominent techniques are Raman and Fourier-transform infrared spectroscopy (FTIR). Combining both techniques delivers complementary information of the test sample. We present the design, construction, and calibration of a novel bimodal spectroscopy system featuring both Raman and infrared measurements simultaneously on the same sample without mutual interference. The optomechanical design provides a modular flexible system for solid and liquid samples and different configurations for Raman. As a novel feature, the Raman module can be operated off-axis for optical sectioning. The calibrated system demonstrates high sensitivity, precision, and resolution for simultaneous operation of both techniques and shows excellent calibration curves with coefficients of determination greater than 0.96. We demonstrate the ability to simultaneously measure Raman and infrared spectra of complex biological material using bovine serum albumin. The performance competes with commercial systems; moreover, it presents the additional advantage of simultaneously operating Raman and infrared techniques. To the best of our knowledge, it is the first demonstration of a combined Raman-infrared system that can analyze the same sample volume and obtain optically sectioned Raman signals. Additionally, quantitative comparison of confocality of backscattering micro-Raman and off-axis Raman was performed for the first time.