We present a low-cost and simple method to fabricate a novel lock-and-key mixer microfluidics using an economic stereolithography (SLA) three-dimensional (3D) printer, which costs less than USD 400 for the investment. The proposed study is promising for a high throughput fabrication module, typically limited by conventional microfluidics fabrications, such as photolithography and polymer-casting methods. We demonstrate the novel modular lock-and-key mixer for the connector and its chamber modules with optimized parameters, such as exposure condition and printing orientation. In addition, the optimization of post-processing was performed to investigate the reliability of the fabricated hollow structures, which are fundamental to creating a fluidic channel or chamber. We found out that by using an inexpensive 3D printer, the fabricated resolution can be pushed down to 850 µm and 550 µm size for squared- and circled-shapes, respectively, by the gradual hollow structure, applying vertical printing orientation. These strategies opened up the possibility of developing straightforward microfluidics platforms that could replace conventional microfluidics mold fabrication methods, such as photolithography and milling, which are costly and time consuming. Considerably cheap commercial resin and its tiny volume employed for a single printing procedure significantly cut down the estimated fabrication cost to less than 50 cents USD/module. The simulation study unravels the prominent properties of the fabricated devices for biological fluid mixers, such as PBS, urine and plasma blood. This study is eminently prospective toward microfluidics application in clinical biosensing, where disposable, low-cost, high-throughput, and reproducible chips are highly required.

One common type of vascular dementia (VaD) is poststroke dementia (PSD). Vascular dementia can occur in one-third of stroke patients. The worsening of cognitive function can occur quickly if not detected and treated early. One of the potential medical modalities for observing this disorder by considering costs and safety factors is electroencephalogram (EEG). It is thought that there are differences in the spectral dynamics of the EEG signal between the normal group and stroke patients with cognitive impairment so that it can be used in detection. Therefore, this study proposes an EEG signal characterization method using EEG spectral power complexity measurements to obtain features of poststroke patients with cognitive impairment and normal subjects. Working memory EEGs were collected and analyzed from forty-two participants, consisting of sixteen normal subjects, fifteen poststroke patients with mild cognitive impairment, and eleven poststroke patients with dementia. From the analysis results, it was found that there were differences in the dynamics of the power spectral in each group, where the spectral power of the cognitively impaired group was more regular than the normal group. Notably, (1) significant differences in spectral entropy (SpecEn) with a p value <0.05 were found for all electrodes, (2) there was a relationship between SpecEn values and the severity of dementia (SpecEnDem < SpecEnMCI < SpecEnNormal), and (3) a post hoc multiple comparison test showed significant differences between groups at the F7 electrode. This study shows that spectral complexity analysis can discriminate between normal and poststroke patients with cognitive impairment. For further studies, it is necessary to simulate performance validation so that the proposed approach can be used in the early detection of poststroke dementia and monitoring the development of dementia.

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Dementia is a term that represents a set of symptoms that affect the ability of the brain’s cognitive functions related to memory, thinking, behavior, and language. At worst, dementia is often called a major neurocognitive disorder or senile disease. One of the most common types of dementia after Alzheimer’s is vascular dementia. Vascular dementia is closely related to cerebrovascular disease, one of which is stroke. Post-stroke patients with recurrent onset have the potential to develop dementia. An accurate diagnosis is needed for proper therapy management to ensure the patient’s quality of life and prevent it from worsening. The gold standard diagnostic of vascular dementia is complex, includes psychological tests, complete memory tests, and is evidenced by medical imaging of brain lesions. However, brain imaging methods such as CT-Scan, PET-Scan, and MRI have high costs and cannot be routinely used in a short period. For more than two decades, electroencephalogram signal analysis has been an alternative in assisting the diagnosis of brain diseases associated with cognitive decline. Traditional EEG analysis performs visual observations of signals, including rhythm, power, and spikes. Of course, it requires a clinician expert, time consumption, and high costs. Therefore, a quantitative EEG method for identifying vascular dementia in post-stroke patients is discussed in this study. This study used 19 EEG channels recorded from normal elderly, post-stroke with mild cognitive impairment, and post-stroke with dementia. The QEEG method used for feature extraction includes relative power, coherence, and signal complexity; the evaluation performance of normal-mild cognitive impairment-dementia classification was conducted using Support Vector Machine and K-Nearest Neighbor. The results of the classification simulation showed the highest accuracy of 96% by Gaussian SVM with a sensitivity and specificity of 95.6% and 97.9%, respectively. This study is expected to be an additional criterion in the diagnosis of dementia, especially in post-stroke patients.

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