Siamese network for classification of Raman spectroscopy with inter-instrument variation for biological applications.

Raman spectroscopy has emerged as a highly sensitive, rapid, and label-free detection method, extensively utilized in biological research. Presently, it is frequently paired with artificial intelligence (AI) algorithms to facilitate identification and classification tasks. However, variations in the settings across different Raman spectrometers, along with the sensitive and continuous nature of biological Raman signals, can subtly alter the acquisition of these signals. This can potentially impact the classification outcomes of the spectra. Moreover, Raman spectra with disparate resolutions pose challenges for effective model training. In this study, we introduce a modularized Siamese neural network, equipped with multiple projection layers to segregate the model components. This design allows our model to support the core module spectral encoder's pluggability. The model determines the classification results by extracting the features of Raman spectra with inter-instrument variation, mapping these feature distances into spectral similarities, and finally, comparing a set of similarities. Our experimental results demonstrate the feasibility of training the model with only 10 spectra per category, using bacterial datasets we created. We compared the classification outcomes of three distinct spectral encoders, with the most effective model achieving a classification accuracy exceeding 90%. Furthermore, we successfully implemented the fusion training and prediction of Raman spectra with different resolutions. In conclusion, our model enhances the validity and comparability of Raman spectral acquisition for biological applications and diversifies the methods of Raman spectral acquisition.

TP53 and EGFR amplification are negative predictors of overall survival in patients diagnosed with non-small cell lung cancer with brain metastases.

Background: The discovery of driver genes such as EGFR, KRAS, and ALK, has dramatically shifted treatment patterns in patients harboring these oncogenes. However, dissemination into the central nervous system (CNS) is a severe complication. In addition, the particular anatomical structure of the CNS has made it difficult to obtain tissue specimens from brain metastases (BM) to generate a gene map, as such, potential predictive markers for survival in patients with non-small cell lung cancer (NSCLC) and BM (NSCLC-BM) remain unclear. Methods: Data from 28 patients diagnosed with NSCLC-BM between June 2019 and May 2021 at Guangdong Sanjiu Brain Hospital (Guangzhou, China), were reviewed. Targeted next-generation sequencing (NGS) of a 168 cancer-related gene panel was available for surgically resected brain tissues from all patients. In addition, molecular characteristics and overall survival (OS) were analyzed to determine potential predictive markers. Results: Among patients with NSCLC-BM, NGS revealed that TP53 was the most frequent mutation (61 %), with a detection rate of 39 %, closely by EGFR amplification. Additionally, CDKN2A, MYC, LRP1B, and RNF43 were frequently observed (18 %). The median OS was significantly shorter in the TP53 mutation group than in the wildtype group (14 versus undefined months, p = 0.014). Similar results were also found in the genetic alteration of EGFR amplification, suggesting that EGFR amplification was associated with worse OS (14 vs. 24 months, p = 0.039). Interestingly, NGS revealed that gene alternations such as TP53, EGFR amplification, and CDKN2A, tended to coexist and such a co-alteration panel indicated worse clinical outcomes (median OS, 5 months). In addition, the detection rate of negative survival genes, including TP53 or EGFR amplification, was much higher in tumor tissues than in plasma samples, indicating the limited predictive value of matched PLA samples. Conclusions: Gene signatures, such as TP53 or EGFR amplification, were associated with worse survival in patients diagnosed with NSCLC-BM. These valuable findings may shed light on new strategies for the prognostic assessment of specific patient groups.

Raman cell sorting for single-cell research.

Cells constitute the fundamental units of living organisms. Investigating individual differences at the single-cell level facilitates an understanding of cell differentiation, development, gene expression, and cellular characteristics, unveiling the underlying laws governing life activities in depth. In recent years, the integration of single-cell manipulation and recognition technologies into detection and sorting systems has emerged as a powerful tool for advancing single-cell research. Raman cell sorting technology has garnered attention owing to its non-labeling, non-destructive detection features and the capability to analyze samples containing water. In addition, this technology can provide live cells for subsequent genomics analysis and gene sequencing. This paper emphasizes the importance of single-cell research, describes the single-cell research methods that currently exist, including single-cell manipulation and single-cell identification techniques, and highlights the advantages of Raman spectroscopy in the field of single-cell analysis by comparing it with the fluorescence-activated cell sorting (FACS) technique. It describes various existing Raman cell sorting techniques and introduces their respective advantages and disadvantages. The above techniques were compared and analyzed, considering a variety of factors. The current bottlenecks include weak single-cell spontaneous Raman signals and the requirement for a prolonged total cell exposure time, significantly constraining Raman cell sorting technology's detection speed, efficiency, and throughput. This paper provides an overview of current methods for enhancing weak spontaneous Raman signals and their associated advantages and disadvantages. Finally, the paper outlines the detailed information related to the Raman cell sorting technology mentioned in this paper and discusses the development trends and direction of Raman cell sorting.

Different genetic profiles contribute to worse overall survival in patients with leptomeningeal metastases of non-small-cell lung cancer.

BACKGROUND: To assess the genetic characteristics of central nervous system (CNS) metastases from non-small-cell lung cancer (NSCLC), we gathered the genetic profiles of brain metastases (BM) and leptomeningeal metastases (LM). Our objective was to identify genetic factors contributing to poorer overall survival (OS) in NSCLC patients with LM. METHODS: This study included 25 consecutive patients with BM and 52 patients with LM from Guangdong Sanjiu Brain Hospital. All participants underwent 168-target panel sequencing. RESULTS: Among the 25 patients with BM, TP53 was the most frequently mutated gene (44%), followed by driver genes such as EGFR and BRAF (40% and 20%, respectively). In patients with BM, EGFR_amp and CDK4 were also frequently mutated, with rates of 20% and 16%, respectively. The genetic landscape of patients with LM differed, with the top mutated genes being EGFR, TP53, EGFR_amp, CDKN2A, CCNE1, CDK4, PMS2, and PIK3CA, with mutation rates of 77%, 69%, 31%, 29%, 13%, 13%, 13%, and 12%, respectively. In our study, patients with LM exhibited significantly worse OS compared to those with BM (p = 0.029). The mutation rates of TP53, EGFR_amp, and CDKN2A varied between patients with LM and those with BM, at 69.23% vs. 44%, 30.77% vs. 20%, and 28.85% vs. 12%, respectively. Further exploration revealed that patients with BM with TP53 mutations had a shorter OS than patients without TP53 mutations (p = 0.014). Similarly, patients with LM and TP53 mutations presented with worse OS than those without TP53 mutations (p = 0.0067). LM patients with CDKN2A deletions had worse OS than those without CDKN2A deletions (p = 0.037). Additionally, patients with EGFR_amp had a shorter OS than those without EGFR_amp (p = 0.044). CONCLUSIONS: Patients with LM exhibited significantly worse OS than those with BM. Gene signatures, such as TP53, EGFR_amp, and CDKN2A, may account for shorter outcomes in patients with LM.

Precision isolation and cultivation of single cells by vortex and flat-top laser ejection.

Single-cell isolation stands as a critical step in single-cell studies, and single-cell ejection technology based on laser induced forward transfer technology (LIFT) is considered one of the most promising methods in this regard for its ability of visible isolating single cell from complex samples. In this study, we improve the LIFT technology and introduce optical vortex laser-induced forward transfer (OV-LIFT) and flat-top laser-induced forward transfer (FT-LIFT) by utilizing spatial light modulator (SLM), aiming to enhance the precision of single-cell sorting and the cell's viability after ejection. Experimental results demonstrate that applying vortex and flat-top beams during the sorting and collection process enables precise retrieval of single cells within diameter ranges of 50 µm and 100 µm, respectively. The recovery rates of Saccharomyces cerevisiae and Escherichia coli DH5α single cell ejected by vortex beam are 89 and 78%, by flat-top beam are 85 and 57%. When employing Gaussian beam sorting, the receiving range extends to 400 µm, with cultivation success rates of S. cerevisiae and E. coli DH5α single cell are 48 and 19%, respectively. This marks the first application of different mode beams in the ejection and cultivation of single cells, providing a novel and effective approach for the precise isolation and improving the viability of single cells.

An optimized DCO with modified binary-weighted DCTLs based hybrid tuning banks for an E-band DPLL.

An optimized millimeter-wave digital controlled oscillator (DCO) in a 40-nm CMOS process is presented in this work. The coarse-tuning modules and medium-tuning modules of the DCO utilize modified binary-weighted digitally controlled transmission lines (DCTLs) to achieve a better compromise among smaller chip size, higher resonant frequency, better tuning resolution and lower phase noise. The tuning precision and die size of the medium tuning bank are improved without changing the binary coding rules by replacing the lowest-weight bit of the DCTLs with switched capacitors. In comparison with traditional DCTLs, the control bits of the coarse and medium tuning modules have been changed from 30 to 8, resulting in a 34.4% reduction in overall length (from 122[Formula: see text]m to 80[Formula: see text]m). In addition, the DCO's fine-tuning modules are achieved using a binary-weighted switched capacitors array connected to the secondary winding of a low-coupling transformer, which enhances the DCO's fine-tuning bank for better frequency resolution with less circuit complexity. The measured tuning range of the optimized DCO is 76-81GHz with a smaller die size of 0.12mm[Formula: see text]. This results in an outstanding figure of merit ([Formula: see text]) of - 190.52dBc/Hz.

[Retrospective study of the relationship of height difference and bone density of postmenopausal women].

OBJECTIVE: To analyze the relationship of the height difference and bone density (BD) of premenopausal and postmenopausal women. METHODS: The height values of 191 premenopausal and postmenopausal women were recorded, and the BD values of lumbar vertebrae and hip were detected by double energy X-ray BD detector. RESULTS: The lower the height of the postmenopausal women, the less the BD value. The BD of lumbar vertebrae dropped 0.025 5 g/cm(2) with each 2 cm of the shortened height, and the BD of hip joint dropped 0.029 2 g/cm(2). The shortened value in postmenopausal women with osteoporosis was statistically greater than that in postmenopausal women without osteoporosis. CONCLUSION: The BD of the postmenopausal women can be estimated by the calculation of their shortened height value.