Raman spectroscopy (RS) is widely used as a non-invasive technique in screening for the diagnosis of oral cancer. The potential of this optical technique for several biomedical applications has been proved. This work studies the efficacy of RS in detecting oral cancer using sub-site-wise differentiation. A total of 80 samples (44 tumor and 36 normal) were cryopreserved from three different sub-sites: The tongue, the buccal mucosa, and the gingiva of the oral mucosa during surgery. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used with principal component analysis (PCA) to classify the samples and the classifications were validated by leave-one-out-cross-validation (LOOCV) and k-fold cross-validation methods. The normal and tumor tissues were differentiated under the PCA-LDA model with an accuracy of 81.25% (sensitivity: 77.27%, specificity: 86.11%). The PCA-QDA classifier model differentiated these tissues with an accuracy of 87.5% (sensitivity: 90.90%, specificity: 83.33%). The PCA-QDA classifier model outperformed the PCA-LDA-based classifier. The model studies revealed that protein, amino acid, and beta-carotene variations are the main biomolecular difference markers for detecting oral cancer.

Background Oral cancer is one of the most common diseases globally. Conventional oral examination and histopathological examination are the two main clinical methods for diagnosing oral cancer early. VELscope is an oral cancer-screening device that exploited autofluorescence. It yields inconsistent results when used to differentiate between normal, premalignant and malignant lesions. We develop a new method to increase the accuracy of differentiation. Materials and methods Five samples (images) of each of 21 normal mucosae, as well as 31 premalignant and 16 malignant lesions of the tongue and buccal mucosa were collected under both white light and autofluorescence (VELscope, 400-460 nm wavelength). The images were developed using an iPod (Apple, Atlanta Georgia, USA). Results The normalized intensity and standard deviation of intensity were calculated to classify image pixels from the region of interest (ROI). Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) classifiers were used. The performance of both of the classifiers was evaluated with respect to accuracy, precision, and recall. These parameters were used for multiclass classification. The accuracy rate of LDA with un-normalized data was increased by 2% and 14% and that of QDA was increased by 16% and 25% for the tongue and buccal mucosa, respectively. Conclusion The QDA algorithm outperforms the LDA classifier in the analysis of autofluorescence images with respect to all of the standard evaluation parameters.

Flip chip technology has been widely adopted in modern power light-emitting diode (LED) fabrications and its output efficiency is closely related to the submount material properties. Here, we present the electrical, optical and thermal properties of flip chip light-emitting diodes mounted on transparent sapphire and borosilicate glass which have shown a higher output luminous flux when compared to the traditional non-transparent mounted LEDs. Exhibiting both better thermal conductivity and good optical transparency, flip chip LEDs with a sapphire submount showed superior performance when compared to the non-transparent silicon submount ones, and also showed better optical performance than the flip chip LEDs mounted on transparent but poor-thermal-conducting glass substrates. The correspondent analysis was carried out using ANSYS 14 to compare the experimental thermal imaging with the simulation results. TracePro software was also used to check the output luminous flux dependency on different LED mounting designs.

Maximizing the expression level of therapeutic proteins in cells is the general goal for DNA/mRNA therapies. It is particularly challenging to achieve efficient protein expression in the cellular contexts with inhibited translation machineries, such as in the presence of cellular Nonstructural protein 1 (Nsp1) of coronaviruses (CoVs) that has been reported to inhibit overall protein synthesis of host genes and exogenously delivered mRNAs/DNAs. In this study, we thoroughly examined the sequence and structure contexts of viral and non-viral 5′UTRs that determine the protein expression levels of exogenously delivered DNAs and mRNAs in cells expressing SARS-CoV-2 Nsp1. It was found that high 5′-proximal A/U content promotes an escape from Nsp1-directed inhibition of protein synthesis and results in selective protein expression. Furthermore, 5′-proximal Cs were found to significantly enhance the protein expression in an Nsp1-dependent manner, while Gs located at a specific window close to the 5′-end counteract such enhancement. The distinct protein expression levels resulted from different 5′UTRs were found correlated to Nsp1-induced mRNA degradations. These findings ultimately enabled rational designs for optimized 5′UTRs that lead to strong expression of exogenous proteins regardless of the translationally repressive Nsp1. On the other hand, we have also identified several 5′-proximal sequences derived from host genes that are capable of mediating the escapes. These results provided novel perspectives to the optimizations of 5′UTRs for DNA/mRNA therapies and/or vaccinations, as well as shedding light on the potential host escapees from Nsp1-directed translational shutoffs. Key points • The 5′-proximal SL1 and 5a/b derived from SARS-CoV-2 genomic RNA promote exogenous protein synthesis in cells expressing Nsp1 comparing with non-specific 5′UTRs. • Specific 5′-proximal sequence contexts are the key determinants of the escapes from Nsp1-directed translational repression and thereby enhance protein expressions. • Systematic mutagenesis identified optimized 5′UTRs that strongly enhance protein expression and promote resistance to Nsp1-induced translational repression and RNA degradation. Supplementary Information The online version contains supplementary material available at 10.1007/s00253-023-12442-2.

The light emitting diode (LED) is widely used in modern solid-state lighting applications, and its output efficiency is closely related to the submounts’ material properties. Most submounts used today, such as low-power printed circuit boards (PCBs) or high-power metal core printed circuit boards (MCPCBs), are not transparent and seriously decrease the output light extraction. To meet the requirements of high light output and better color mixing, a three-dimensional (3-D) stacked flip-chip (FC) LED module is proposed and demonstrated. To realize light penetration and mixing, the mentioned 3-D vertically stacking RGB LEDs use transparent glass as FC package submounts called glass circuit boards (GCB). Light emitted from each GCB stacked LEDs passes through each other and thus exhibits good output efficiency and homogeneous light-mixing characteristics. In this work, the parasitic problem of heat accumulation, which caused by the poor thermal conductivity of GCB and leads to a serious decrease in output efficiency, is solved by a proposed transparent cooling oil encapsulation (OCP) method.