Lab-on-a-chip technologies for genodermatoses: Recent progress and future perspectives

Advancements in the research of finger-actuated POCT chips

Microfluidic point-of-care testing (POCT) chips are used to enable the mixing and reaction of small sample volumes, facilitating target molecule detection. Traditional methods for actuating POCT chips rely on external pumps or power supplies, which are complex and non-portable. The development of finger-actuated chips has reduced operational difficulty and improved portability, promoting the development of POCT chips. This paper reviews the significance, developments, and potential applications of finger-actuated POCT chips. Three methods for controlling the flow accuracy of finger-actuated chips are summarized: direct push, indirect control, and sample injection control method, along with their respective advantages and disadvantages. Meanwhile, a comprehensive analysis of multi-fluid driving modes is provided, categorizing them into single-push multi-driving and multi-push multi-driving modes. Furthermore, recent research breakthroughs in finger-actuated chips are thoroughly summarized, and their structures, driving, and detection methods are discussed. Finally, this paper discusses the driving performance of finger-actuated chips, the suitability of detection scenarios, and the compatibility with existing detection technologies. It also provides prospects for the future development and application of finger-actuated POCT chips.

Whole-exome sequencing enables rapid and prenatal diagnosis of inherited skin disorders

BACKGROUND

Genodermatoses are a broad group of disorders with specific or non-specific skin-based phenotypes, most of which are monogenic disorders. However, it's a great challenge to make a precise molecular diagnosis because of the clinical heterogeneity. The genetic and clinical heterogeneity brings great challenges for diagnosis in dermatology. The whole exome sequencing (WES) not only expedites the discovery of the genetic variations, but also contributes to genetic counselling and prenatal diagnosis.

MATERIALS AND METHODS

Followed by the initial clinical and pathological diagnosis, genetic variations were identified by WES. The pathogenicity of the copy number variations (CNVs) and single-nucleotide variants (SNVs) were evaluated according to ACMG guidelines. Candidate pathogenic SNVs were confirmed by Sanger sequencing in the proband and the family members.

RESULTS

Totally 25 cases were recruited. Nine novel variations, including c.5546G > C and c.1457delC in NF1, c.6110G > T in COL7A1, c.2127delG in TSC1, c.1445 C > A and c.1265G > A in TYR, Xp22.31 deletion in STS, c.908 C > T in ATP2A2, c.1371insC in IKBKG, and nine known ones were identified in 16 cases (64%). Prenatal diagnosis was applied in 6 pregnant women by amniocentesis, two of whom carried positive findings.

CONCLUSIONS

Our findings highlighted the value of WES as a first-tier genetic test in determining the molecular diagnosis. We also discovered the distribution of genodermatoses in this district, which provided a novel clinical dataset for dermatologists.

Machine Learning Model of Dimensionless Numbers to Predict Flow Patterns and Droplet Characteristics for Two-Phase Digital Flows

In the digital microfluidic experiments, the droplet characteristics and flow patterns are generally identified and predicted by the empirical methods, which are difficult to process a large amount of data mining. In addition, due to the existence of inevitable human invention, the inconsistent judgment standards make the comparison between different experiments cumbersome and almost impossible. In this paper, we tried to use machine learning to build algorithms that could automatically identify, judge, and predict flow patterns and droplet characteristics, so that the empirical judgment was transferred to be an intelligent process. The difference on the usual machine learning algorithms, a generalized variable system was introduced to describe the different geometry configurations of the digital microfluidics. Specifically, Buckingham’s theorem had been adopted to obtain multiple groups of dimensionless numbers as the input variables of machine learning algorithms. Through the verification of the algorithms, the SVM and BPNN algorithms had classified and predicted the different flow patterns and droplet characteristics (the length and frequency) successfully. By comparing with the primitive parameters system, the dimensionless numbers system was superior in the predictive capability. The traditional dimensionless numbers selected for the machine learning algorithms should have physical meanings strongly rather than mathematical meanings. The machine learning algorithms applying the dimensionless numbers had declined the dimensionality of the system and the amount of computation and not lose the information of primitive parameters.