Cervical cancer biomarker screening based on Raman spectroscopy and multivariate statistical analysis.

Cervical cancer (CC) stands as one of the most prevalent malignancies among females, and the examination of serum tumor markers(TMs) assumes paramount significance in both its diagnosis and treatment. This research delves into the potential of combining Surface-Enhanced Raman Spectroscopy (SERS) with Multivariate Statistical Analysis (MSA) to diagnose cervical cancer, coupled with the identification of prospective serum biomarkers. Serum samples were collected from 95 CC patients and 81 healthy subjects, with subsequent MSA employed to analyze the spectral data. The outcomes underscore the superior efficacy of Partial Least Squares Discriminant Analysis (PLS-DA) within the MSA framework, achieving predictive accuracy of 97.73 %, and exhibiting sensitivities and specificities of 100 % and 95.83 % respectively. Additionally, the PLS-DA model yields a Variable Importance in Projection (VIP) list, which, when coupled with the biochemical information of characteristic peaks, can be utilized for the screening of biomarkers. Here, the Random Forest (RF) model is introduced to aid in biomarker screening. The two findings demonstrate that the principal contributing features distinguishing cervical cancer Raman spectra from those of healthy individuals are located at 482, 623, 722, 956, 1093, and 1656 cm-1, primarily linked to serum components such as DNA, tyrosine, adenine, valine, D-mannose, and amide I. Predictive models are constructed for individual biomolecules, generating ROC curves. Remarkably, D-mannose of V (C-N) exhibited the highest performance, boasting an AUC value of 0.979. This suggests its potential as a serum biomarker for distinguishing cervical cancer from healthy subjects.

A Duplex PCR Assay for Rapid Detection of Klebsiella pneumoniae and Chryseobacterium in Large Yellow Croaker Fish.

Both Klebsiella pneumoniae and Chryseobacterium cause an increasing number of diseases in fish, resulting in great economic losses in aquaculture. In addition, the disease infected with Klebsiella pneumoniae or Chryseobacterium exhibited the similar clinical symptoms in aquatic animals. However, there is no effective means for the simultaneous detection of co-infection and discrimination them for these two pathogens. Here, we developed a duplex polymerase chain reaction (PCR) method based on the outer membrane protein A (ompA) gene of Klebsiella pneumoniae and Chryseobacterium. The specificity and validity of the designed primers were confirmed experimentally using simplex PCR. The expected amplicons for Klebsiella pneumoniae and Chryseobacterium had a size of 663 and 1404 bp, respectively. The optimal condition for duplex PCR were determined to encompass a primer concentration of 0.5 µM and annealing temperature of 57°C. This method was analytical specific with no amplification being observed from the genomic DNA of Escherichia coli, Vibrio harveyi, Pseudomonas plecoglossicida, Aeromonas hydrophila and Acinetobacter johnsonii. The limit of detection was estimated to be 20 fg of genomic DNA for Chryseobacterium and 200 fg for Klebsiella pneumoniae, or 100 colony-forming units (CFU) of bacterial cells in both cases. The duplex PCR was capable of simultaneously amplifying target fragments from genomic DNA extracted from the bacteria and fish liver. For practical validation of the method, 20 diseased fish were collected from farms, among which 4 samples were PCR-positive for Klebsiella pneumoniae and Chryseobacterium. The duplex PCR method developed here is time-saving, specific, convenient, and may prove to be an invaluable tool for molecular detection and epidemiological investigation of Klebsiella pneumoniae and Chryseobacterium in the field of aquaculture.

Raman spectroscopy combined with multivariate statistical algorithms for the simultaneous screening of cervical and breast cancers.

Breast and cervical cancers are becoming the leading causes of death among women worldwide, but current diagnostic methods have many drawbacks, such as being time-consuming and high cost. Raman spectroscopy, as a rapid, reliable, and non-destructive spectroscopic detection technique, has achieved many breakthrough results in the screening and prognosis of various cancer tumors. Therefore, in this study, Raman spectroscopy technology was used to diagnose breast cancer and cervical cancer. A total of 225 spectra were recorded from 87 patients with cervical cancer, 60 patients with breast cancer, and 78 healthy individuals. The obvious difference in Raman spectrum between the three groups was mainly shown at 809 cm-1 (tyrosine), 958 cm-1 (carotenoid), 1004 cm-1 (phenylalanine), 1154 cm-1 (ß-carotene), 1267 cm-1 (Amide III), 1445 cm-1 (phospholipids), 1515 cm-1 (ß-carotene), and 1585 cm-1 (C = C olefinic stretch). We used one-way analysis of variance for these peaks and demonstrated that they were significantly different. Then, we combined the detected Raman spectra with multivariate statistical calculations using the principal component analysis-linear discrimination algorithm (PCA-LDA) to discriminate between the three groups of collected serum samples. The diagnostic results showed that the model's accuracy, precision, recall, and F1 score of the model were 92.90%, 92.62%, 92.10%, and 92.36%, respectively. These results suggest that Raman spectroscopy can achieve ultra-sensitive detection of serum, and the developed diagnostic models have great potential for the prognosis and simultaneous screening of cervical and breast cancers.

Development of a Strategy for L-Lactic Acid Production by Rhizopus oryzae Using Zizania latifolia Waste and Cane Molasses as Carbon Sources.

As a biodegradable and renewable material, polylactic acid is considered a major environmentally friendly alternative to petrochemical plastics. Microbial fermentation is the traditional method for lactic acid production, but it is still too expensive to compete with the petrochemical industry. Agro-industrial wastes are generated from the food and agricultural industries and agricultural practices. The utilization of agro-industrial wastes is an important way to reduce costs, save energy and achieve sustainable development. The present study aimed to develop a method for the valorization of Zizania latifolia waste and cane molasses as carbon sources for L-lactic acid fermentation using Rhizopus oryzae LA-UN-1. The results showed that xylose derived from the acid hydrolysis of Z. latifolia waste was beneficial for cell growth, while glucose from the acid hydrolysis of Z. latifolia waste and mixed sugars (glucose and fructose) from the acid hydrolysis of cane molasses were suitable for the accumulation of lactic acid. Thus, a three-stage carbon source utilization strategy was developed, which markedly improved lactic acid production and productivity, respectively reaching 129.47 g/L and 1.51 g/L·h after 86 h of fermentation. This work demonstrates that inexpensive Z. latifolia waste and cane molasses can be suitable carbon sources for lactic acid production, offering an efficient utilization strategy for agro-industrial wastes.

Antimicrobial activity of phenyllactic acid against Klebsiella pneumoniae and its effect on cell wall membrane and genomic DNA.

As Klebsiella pneumoniae (KP) has acquired high levels of resistance to multiple antibiotics, it is considered a worldwide pathogen of concern, and substitutes for traditional antibiotics are urgently needed. 3-Phenyllactic acid (PLA) has been reported to have antimicrobial activity against food-borne bacteria. However, there was no experiment evidence for the exact antibacterial effect and mechanism of PLA kills pathogenic KP. In this study, the Oxford cup method indicated that PLA is effective to KP with a minimum inhibitory concentration of 2.5 mg/mL. Furthermore, PLA inhibited the growth and biofilm formation of in a time- and concentration-dependent manner. In vivo, PLA could significantly increase the survival rate of infected mice and reduce the pathological tissue damage. The antibacterial mode of PLA against KP was further explored. Firstly, scanning electron microscopy illustrated the disruption of cellular ultrastructure caused by PLA. Secondly, measurement of leaked alkaline phosphatase demonstrated that PLA disrupted the cell wall integrity of KP and flow cytometry analysis with propidium iodide staining suggested that PLA damaged the cell membrane integrity. Finally, the results of fluorescence spectroscopy and agarose gel electrophoresis demonstrated that PLA bound to genomic DNA and initiated its degradation. The anti-KP mode of action of PLA was attributed to the destruction of the cell wall, membrane, and genomic DNA binding. These findings suggest that PLA has great potential applications as antibiotic substitutes in feed additives against KP infection in animals.

High-definition image transmission through dynamically perturbed multimode fiber by a self-attention based neural network.

We implement faithful multimode fiber (MMF) image transmission by a self-attention-based neural network. Compared with a real-valued artificial neural network (ANN) based on a convolutional neural network (CNN), our method utilizes a self-attention mechanism to achieve a higher image quality. The enhancement measure (EME) and structural similarity (SSIM) of the dataset collected in the experiment improved by 0.79 and 0.04; the total number of parameters can be reduced by up to 25%. To enhance the robustness of the neural network to MMF bending in image transmission, we use a simulation dataset to prove that the hybrid training method is helpful in MMF transmission of a high-definition image. Our findings may pave the way for simpler and more robust single-MMF image transmission schemes with hybrid training; SSIM on datasets under different disturbances improve by 0.18. This system has the potential to be applied to various high-demand image transmission tasks, such as endoscopy.

Diagnoses in multiple types of cancer based on serum Raman spectroscopy combined with a convolutional neural network: Gastric cancer, colon cancer, rectal cancer, lung cancer.

Cancer is one of the major diseases that seriously threaten human health. Timely screening is beneficial to the cure of cancer. There are some shortcomings in current diagnosis methods, so it is very important to find a low-cost, fast, and nondestructive cancer screening technology. In this study, we demonstrated that serum Raman spectroscopy combined with a convolutional neural network model can be used for the diagnosis of four types of cancer including gastric cancer, colon cancer, rectal cancer, and lung cancer. Raman spectra database containing four types of cancer and healthy controls was established and a one-dimensional convolutional neural network (1D-CNN) was constructed. The classification accuracy of the Raman spectra combined with the 1D-CNN model was 94.5%. A convolutional neural network (CNN) is regarded as a black box, and the learning mechanism of the model is not clear. Therefore, we tried to visualize the CNN features of each convolutional layer in the diagnosis of rectal cancer. Overall, Raman spectroscopy combined with the CNN model is an effective tool that can be used to distinguish different cancer from healthy controls.

Isolation and screening of a chitin deacetylase producing Bacillus cereus and its potential for chitosan preparation.

Chitosan is a biopolymer material extracted from marine biomass waste such as shrimp and crab shells, which has good biocompatibility and degradability with great potential for application in the field of wastewater treatment and soil remediation. The higher the degree of deacetylation (DD), the better the adsorption performance of chitosan. Chitin deacetylase (CDA) can specifically catalyze the deacetylate of chitin in a green reaction that is environmentally friendly. However, the scarcity of high yielding chitin deacetylase strains has been regarded as the technical bottleneck of chitosan green production. Here, we screened a natural chitin degrading bacterium from coastal mud and identified it as Bacillus cereus ZWT-08 by re-screening the chitin deacetylase activity and degree of deacetylation values. By optimizing the medium conditions and enzyme production process, ZWT-08 was cultured in fermentation medium with 1% (m/V) glucose and yeast extract at pH 6.0, 37°C, and a stirring speed of 180 r/min. After fermenting in 5 L fermenter for 48 h, the deacetylation activity of the supernatant reached 613.25 U/mL. Electron microscopic examination of the chitin substrate in the fermentation medium revealed a marshmallow-like fluffy texture on its structural surface. Meanwhile, 89.29% of the acetyl groups in this chitin substrate were removed by enzymatic digestion of chitin deacetylase produced by ZWT-08, resulting in the preparation of chitosan a degree of deacetylation higher than 90%. As an effective strain for chitosan production, Bacillus cereus ZWT-08 plays a positive role in the bioconversion of chitin and the upgrading of the chitosan industry.

Compared between support vector machine (SVM) and deep belief network (DBN) for multi-classification of Raman spectroscopy for cervical diseases.

In this study, a minimally invasive test method for cervical cancer in vitro was proposed by comparing Raman spectroscopy with support vector machine (SVM) model and deep belief network (DBN) model. The serum Raman spectra of cervical cancer, hysteromyoma, and healthy people were collected. After data processing, SVM classification model and DBN classification model were built respectively. The experimental results show that when the DBN network algorithm is used, the sample test set can be divided accurately and the result of cross-validation is ideal. Compared with the traditional SVM algorithm, this method firstly screened the effective feature matrix from the data, and then classified the data. With high efficiency and accuracy, based on 445 samples collected, this method improved the accuracy by 13.93%±2.47% compared with the SVM method, and provided a new direction and idea for the in vitro diagnosis of cervical diseases.

A classification model for detection of ductal carcinoma in situ by Fourier transform infrared spectroscopy based on deep structured semantic model.

At present, deep learning is widely used in spectral data processing. Deep learning requires a large amount of data for training, while the collection of biological serum spectra is limited by sample numbers and labor costs, so it is impractical to obtain a large amount of serum spectral data for disease detection. In this study, we propose a spectral classification model based on the deep structured semantic model (DSSM) and successfully apply it to Fourier Transform Infrared (FT-IR) spectroscopy for ductal carcinoma in situ (DCIS) detection. Compared with the traditional deep learning model, we match the spectral data into positive and negative pairs according to whether the spectra are from the same category. The DSSM structure is constructed by extracting features according to the spectral similarity of spectra pairs. This new construction model increases the data amount used for model training and reduces the dimension of spectral data. Firstly, the FT-IR spectra are paired. The spectra pairs are labeled as positive pairs if they come from the same category, and the spectra pairs are labeled as negative pairs if they come from different categories. Secondly, two spectra in each spectra pair are put into two deep neural networks of the DSSM structure separately. Then the spectral similarity between the output feature maps of two deep neural networks is calculated. The DSSM structure is trained by maximizing the conditional likelihood of the spectra pairs from the same category. Thirdly, after the training of DSSM is done, the training set and testing set are input into two deep neural networks separately. The output feature maps of the training set are put into the reference library. Lastly, the k-nearest neighbor (KNN) model is used for classification according to Euclidean distances between the output feature map of each unknown sample to the reference library. The category of the unknown sample is judged according to the categories of k nearest samples. We also use principal component analysis (PCA) to reduce dimension for comparison. The accuracies of the KNN model, principal component analysis-k nearest neighbor (PCA-KNN) model, and deep structured semantic model-k nearest neighbor (DSSM-KNN) model are 78.8%, 72.7%, and 97.0%, which proves that our proposed model has higher accuracy.