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.

Heat-Insulated Regenerated Fibers with UV Resistance: Silk Fibroin/Al2O3 Nanoparticles.

The various wastes generated by silkworm silk textiles that are no longer in use are increasing, which is causing considerable waste and contamination. This issue has attracted widespread attention in countries that use a lot of silk. Therefore, enhancing the mechanical properties of regenerated silk fibroin (RSF) and enriching the function of silk are important directions to expand the comprehensive utilization of silk products. In this paper, the preparation of RSF/Al2O3 nanoparticles (NPs) hybrid fiber with different Al2O3 NPs contents by wet spinning and its novel performance are reported. It was found that the RSF/Al2O3 NPs hybrid fiber was a multifunctional fiber material with thermal insulation and UV resistance. Natural light tests showed that the temperature rise rate of RSF/Al2O3 NPs hybrid fibers was slower than that of RSF fibers, and the average temperature rose from 29.1 °C to about 35.4 °C in 15 min, while RSF fibers could rise to about 40.1 °C. UV absorption tests showed that the hybrid fiber was resistant to UV radiation. Furthermore, the addition of Al2O3 NPs may improve the mechanical properties of the hybrid fibers. This was because the blending of Al2O3 NPs promoted the self-assembly of ß-sheets in the RSF reaction mixture in a dose-dependent manner, which was manifested as the RSF/Al2O3 NPs hybrid fibers had more ß-sheets, crystallinity, and a smaller crystal size. In addition, RSF/Al2O3 NPs hybrid fibers had good biocompatibility and durability in micro-alkaline sweat environments. The above performance makes the RSF/Al2O3 NPs hybrid fibers promising candidates for application in heat-insulating and UV-resistant fabrics as well as military clothing.

Deep Reinforcement Learning for Solving Vehicle Routing Problems With Backhauls.

The vehicle routing problem with backhauls (VRPBs) is a challenging problem commonly studied in computer science and operations research. Featured by linehaul (or delivery) and backhaul (or pickup) customers, the VRPB has broad applications in real-world logistics. In this article, we propose a neural heuristic based on deep reinforcement learning (DRL) to solve the traditional and improved VRPB variants, with an encoder-decoder structured policy network trained to sequentially construct the routes for vehicles. Specifically, we first describe the VRPB based on a graph and cast the solution construction as a Markov decision process (MDP). Then, to identify the relationship among the nodes (i.e., linehaul and backhaul customers, and the depot), we design a two-stage attention-based encoder, including a self-attention and a heterogeneous attention for each stage, which could yield more informative representations of the nodes so as to deliver high-quality solutions. The evaluation on the two VRPB variants reveals that, our neural heuristic performs favorably against both the conventional and neural heuristic baselines on randomly generated instances and benchmark instances. Moreover, the trained policy network exhibits a desirable capability of generalization to various problem sizes and distributions.

Conditional Neural Heuristic for Multiobjective Vehicle Routing Problems.

Existing neural heuristics for multiobjective vehicle routing problems (MOVRPs) are primarily conditioned on instance context, which failed to appropriately exploit preference and problem size, thus holding back the performance. To thoroughly unleash the potential, we propose a novel conditional neural heuristic (CNH) that fully leverages the instance context, preference, and size with an encoder-decoder structured policy network. Particularly, in our CNH, we design a dual-attention-based encoder to relate preferences and instance contexts, so as to better capture their joint effect on approximating the exact Pareto front (PF). We also design a size-aware decoder based on the sinusoidal encoding to explicitly incorporate the problem size into the embedding, so that a single trained model could better solve instances of various scales. Besides, we customize the REINFORCE algorithm to train the neural heuristic by leveraging stochastic preferences (SPs), which further enhances the training performance. Extensive experimental results on random and benchmark instances reveal that our CNH could achieve favorable approximation to the whole PF with higher hypervolume (HV) and lower optimality gap (Gap) than those of the existing neural and conventional heuristics. More importantly, a single trained model of our CNH can outperform other neural heuristics that are exclusively trained on each size. In addition, the effectiveness of the key designs is also verified through ablation studies.

Machine learning-assisted serum SERS strategy for rapid and non-invasive screening of early cystic echinococcosis.

Early and accurate diagnosis of cystic echinococcosis (CE) with existing technologies is still challenging. Herein, we proposed a novel strategy based on the combination of label-free serum surface-enhanced Raman scattering (SERS) spectroscopy and machine learning for rapid and non-invasive diagnosis of early-stage CE. Specifically, by establishing early- and middle-stage mouse models, the corresponding CE-infected and normal control serum samples were collected, and silver nanoparticles (AgNPs) were utilized as the substrate to obtain SERS spectra. The early- and middle-stage discriminant models were developed using a support vector machine, with diagnostic accuracies of 91.7% and 95.7%, respectively. Furthermore, by analyzing the serum SERS spectra, some biomarkers that may be related to early CE were found, including purine metabolites and protein-related amide bands, which was consistent with other biochemical studies. Thus, our findings indicate that label-free serum SERS analysis is a potential early-stage CE detection method that is promising for clinical translation.

Large Microsphere Structure of a Co/C Composite Derived from Co-MOF with Excellent Wideband Electromagnetic Microwave Absorption Performance.

In the field of electromagnetic wave (EMW) absorption, carbon matrix materials based on metal-organic frameworks (MOFs) have drawn more interest as a result of their outstanding advantages, such as porous structure, lightweight, controlled morphology, etc. However, how to broaden the effective absorption bandwidth [EAB; reflection loss (RL) ≤ -10 dB] is still a challenge. In this paper, large microsphere structures of a Co/C composite composed of small particle clusters were successfully prepared by the solvothermal method and annealing treatment. At a filling ratio of 40 wt %, the Co/C composite shows attractive microwave absorption (MA) performance after being annealed at 600 °C in an atmosphere of argon. With an EAB of 6.32 GHz (9.92-16.24 GHz) and a thickness of just 2.57 mm, the minimum RL can be attained at -54.55 dB. Most importantly, the EAB can attain 7.12 GHz (10.88-18.0 GHz) when the thickness is 2.38 mm, which is larger than that of the majority of MOF-derived composites. The superior MA performance is strongly related to excellent impedance matching and a higher attenuation constant. This study provides a simple strategy for synthesizing a MOF-derived Co/C composite with a wide EAB.

Node-downloadable frequency transfer system based on a mode-locked laser with over 100 km of fiber.

To meet the requirements of time-frequency networks and enable frequency downloadability for nodes along the link, we demonstrated the extraction of stable frequency signals at nodes using a mode-locked laser under the condition of 100 km laboratory fiber. The node consists of a simple structure that utilizes widely used optoelectronic devices and enables plug-and-play applications. In addition, the node can recover frequency signals with multiple frequencies, which are useful for scenarios that require different frequencies. Here, we experimentally demonstrated a short-term frequency instability of 2.83 × 10-13@1 s and a long-term frequency instability of 1.18 × 10-15@10,000 s at the node, which is similar to that at the remote site of the frequency transfer system. At the same time, frequency signals with different frequencies also achieved stable extraction with the same performance at the node. Our results can support the distributed application under large-scale time-frequency networks.

The Photothermal Conversion and UV Resistance of Silk Fabrics Being Achieved through Surface Modification with C@SiO2 Nanoparticles.

With the improvement in people's living standards, the development and application of smart textiles are receiving increasing attention. In this study, a carbon nanosurface was successfully coated with a SiO2 layer to form C@SiO2 nanomaterials, which improved the dispersion of carbon nanomaterials in an aqueous solution and enhanced the absorption of light by the carbon nanoparticles. C@SiO2 nanoparticles were coupled on the surface of silk fabric with the silane coupling agent KH570 to form C@SiO2 nanosilk fabric. The silk fabric that was subjected to such surface modification was endowed with a special photothermal function. The results obtained with scanning electron microscopy (SEM), energy dispersive spectrometer (EDS), and infrared spectroscopy (FTIR) showed that C@SiO2 nanoparticles were successfully modified on the surface of the silk fabric. In addition, under the irradiation of near-infrared light with a power of 20 W and a wavelength of 808 nm, the C@SiO2 nanosilk fabric experienced rapid warming from 23 °C to 60 °C within 30 s. After subjecting the functional fabric to hundreds of photothermal experiments and multiple washes, the photothermal efficiency remained largely unchanged and proved to be durable and stable. In addition, the thermogravimetric (TG) analysis results showed that the C@SiO2 nanoparticles contributed to the thermal stability of the silk fabric. The UV transmittance results indicated that C@SiO2 nanofabric is UV-resistant. The silk modification method developed in this study is low-cost, efficient, and environmentally friendly. It has some prospects for future applications in the textile industry.

Rapid diagnosis of cervical cancer based on serum FTIR spectroscopy and support vector machines.

Cervical cancer is one of the most common malignant tumors among female gynecological diseases. This paper aims to explore the feasibility of utilizing serum Fourier Transform Infrared (FTIR) spectroscopy, combined with machine learning and deep learning algorithms, to efficiently differentiate between healthy individuals, hysteromyoma patients, and cervical cancer patients. In this study, serum samples from 30 groups of hysteromyoma, 36 groups of cervical cancer, and 30 healthy groups were collected and FTIR spectra of each group were recorded. In addition, the raw datasets were averaged according to the number of scans to obtain an average dataset, and the raw datasets were spectrally enhanced to obtain an augmentation dataset, resulting in a total of three sets of data with sizes of 258, 96, and 1806, respectively. Then, the hyperparameters in the four kernel functions of the Support Vector Machine (SVM) model were optimized by grid search and leave-one-out (LOO) cross-validation. The resulting SVM models achieved recognition accuracies ranging from 85.0% to 100.0% on the test set. Furthermore, a one-dimensional convolutional neural network (1D-CNN) demonstrated a recognition accuracy of 75.0% to 90.0% on the test set. It can be concluded that the use of serum FTIR spectroscopy combined with the SVM algorithm for the diagnosis of cervical cancer has important medical significance.

Audible Acoustic Wave Promotes EV Formation and Secretion from Adherent Cancer Cells via Mechanical Stimulation.

Cancer-derived extracellular vesicles (EVs) have shown great potential in the field of cancer metastasis research. However, inefficient EV biofabrication has become a barrier to large-scale research on cancer-derived EVs. Here, we presented a novel method to enhance the biofabrication of cancer-derived EVs via audible acoustic wave (AAW), which yielded mechanical stimuli, including surface acoustic pressure and surface stress. Compared to EV yield in conventional static culture, AAW increased the number of cancer-derived EVs by up to 2.5-folds within 3 days. Furthermore, cancer-derived EVs under AAW stimulation exhibited morphology, size, and zeta potential comparable to EVs generated in conventional static culture, and more importantly, they showed the capability to promote cancer cell migration and invasion under both 2D and 3D culture conditions. Additionally, the elevation in EV biofabrication correlated with the activation of the ESCRT pathway and upregulation of membrane fusion-associated proteins (RAB family, SNARE family, RHO family) in response to AAW stimulation. We believe that AAW represents an attractive approach to achieving high-quantity and high-quality production of EVs and that it has the potential to enhance EV biofabrication from other cell types, thereby facilitating EV-based scientific and translational research.

A biomimetic renal fibrosis progression model on-chip evaluates anti-fibrotic effects longitudinally in a dynamic fibrogenic niche.

Although renal fibrosis can advance chronic kidney disease and progressively lead to end-stage renal failure, no effective anti-fibrotic drugs have been clinically approved. To aid drug development, we developed a biomimetic renal fibrosis progression model on-chip to evaluate anti-fibrotic effects of natural killer cell-derived extracellular vesicles and pirfenidone (PFD) across different fibrotic stages. First, the dynamic interplay between fibroblasts and kidney-derived extracellular matrix (ECM) resembling the fibrogenic niche on-chip demonstrated that myofibroblasts induced by stiff ECM in 3 days were reversed to fibroblasts by switching to soft ECM, which was within 2, but not 7 days. Second, PFD significantly down-regulated the expression of α-SMA in NRK-49F in medium ECM, as opposed to stiff ECM. Third, a study in rats showed that early administration of PFD significantly inhibited renal fibrosis in terms of the expression levels of α-SMA and YAP. Taken together, both on-chip and animal models indicate the importance of early anti-fibrotic intervention for checking the progression of renal fibrosis. Therefore, this renal fibrosis progression on-chip with a feature of recapitulating dynamic biochemical and biophysical cues can be readily used to assess anti-fibrotic candidates and to explore the tipping point when the fibrotic fate can be rescued for better medical intervention.

Preparation, Chromatic Properties Analysis and Proportioning Optimization of Co-Cr-Fe-Based Black Pigment.

The utilization of Co-Cr-Fe-based black pigments bears considerable significance within the realm of commercial ceramic pigments, owing to their distinctive spinel structure, remarkable high-temperature stability, and exceptional chromatic attributes. This study delves into the synthesis of diverse black pigment configurations by employing the co-precipitation method, leveraging the interplay of these three metallic oxides. This investigation encompasses a comprehensive scrutiny of ion valences, crystal structures and parameters, colorimetric properties, and their interrelationships. The methodology integrates the response surface methodology (RSM) framework, using theoretical formulations to navigate the material ratios and elucidating the associations between the resultant compositions and color coordinate values, aligned with the CIE-Lab* colorimetric methodology. The derived predictive models yielded an optimized black pigment composition, characterized by heightened black intensity and a refined formulation.

The Preparation, Structural Design, and Application of Electroactive Poly(vinylidene fluoride)-Based Materials for Wearable Sensors and Human Energy Harvesters.

Owing to their biocompatibility, chemical stability, film-forming ability, cost-effectiveness, and excellent electroactive properties, poly(vinylidene fluoride) (PVDF) and PVDF-based polymers are widely used in sensors, actuators, energy harvesters, etc. In this review, the recent research progress on the PVDF phase structures and identification of different phases is outlined. Several approaches for obtaining the electroactive phase of PVDF and preparing PVDF-based nanocomposites are described. Furthermore, the potential applications of these materials in wearable sensors and human energy harvesters are discussed. Finally, some challenges and perspectives for improving the properties and boosting the applications of these materials are presented.

Ketamine exerts dual effects on the apoptosis of primary cultured hippocampal neurons from fetal rats in vitro.

Ketamine, a noncompetitive N-methyl D-aspartate (NMDA) receptor antagonist, is widely used in pediatric clinical practice. The neuroprotective and neurotoxic effects of ketamine on brain neurons during development remain controversial. The reason may be related to the different concentrations of ketamine used in practice and the small range of concentrations used in previous studies. In this study, cultured hippocampal neurons were treated with ketamine in a wide range of concentrations to comprehensively observe the effects of different concentrations of ketamine on neurons. We demonstrated that low concentrations of ketamine (10 µM, 100 µM and 1000 µM) promoted neuronal survival (p < 0.05) and reduced neuronal apoptosis (p < 0.05) compared with those of the control group. High concentrations of ketamine (2000 µM, 2500 µM and 3000 µM) reduced neuronal survival (p < 0.05) and promoted neuronal apoptosis (p < 0.05). The p38 MAPK inhibitor SB203580 reduced neuronal apoptosis induced by high concentrations of ketamine (2500 µM) (p < 0.05). Our findings indicate that ketamine exerts a dual effect on the apoptosis of primary cultured fetal rat hippocampal neurons in vitro and that the neurotoxic effects of ketamine are related to activation of the p38 MAPK signaling pathway.

The Study of Alanine Transaminase Activity in Tissues of Silkworm (Bombyx mori) via Direct Analysis in Real-Time (DART) Mass Spectrometry.

Alanine transaminase (ALT) is an important amino acid-metabolizing enzyme in silkworm Bombyx mori L., and is mainly involved in transferring glutamate to alanine (serving as an essential precursor in silk protein synthesis) through transamination. Therefore, it is generally believed that silk protein synthesis in the silk gland and the cocoon quantity increase with the increase in ALT activity to a certain extent. Here, a novel analytical method was developed to determine the ALT activity in several key tissues of Bombyx mori L. including the posterior silk gland, midgut, fat body, middle silk gland, trachea and hemolymph, by combining the direct-analysis-in-real-time (DART) ion source with a triple-quadrupole mass spectrometer. In addition, a traditional ALT activity assay, the Reitman-Frankel method, was also used to measure ALT activity for comparison. The ALT activity results obtained via the DART-MS method are in good agreement with those obtained via the Reitman-Frankel method. However, the present DART-MS method provides a more convenient, rapid and environmentally friendly quantitative method for ALT measurement. Especially, this method can also monitor ALT activity in different tissues of Bombyx mori L. in real time.

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.

Auto- and paracrine rewiring of NIX-mediated mitophagy by insulin-like growth factor-binding protein 7 in septic AKI escalates inflammation-coupling tubular damage.

AIMS: Inflammation-coupling tubular damage (ICTD) contributes to pathogenesis of septic acute kidney injury (AKI), in which insulin-like growth factor-binding protein 7 (IGFBP-7) serves as a biomarker for risk stratification. The current study aims to discern how IGFBP-7 signalling influences ICTD, the mechanisms that underlie this process and whether blockade of the IGFBP-7-dependent ICTD might have therapeutic value for septic AKI. MATERIALS AND METHODS: In vivo characterization was carried out in B6/JGpt-Igfbp7em1Cd1165/Gpt mice subjected to cecal ligation and puncture (CLP). Transmission electron microscopy, immunofluorescence, flow cytometry, immunoblotting, ELISA, RT-qPCR and dual-luciferase reporter assays were used to determine mitochondrial functions, cell apoptosis, cytokine secretion and gene transcription. KEY FINDINGS: ICTD augments the transcriptional activity and protein secretion of tubular IGFBP-7, which enables an auto- and paracrine signalling via deactivation of IGF-1 receptor (IGF-1R). Genetic knockout (KO) of IGFBP-7 provides renal protection, improves survival and resolves inflammation in murine models of cecal ligation and puncture (CLP), while administering recombinant IGFBP-7 aggravates ICTD and inflammatory invasion. IGFBP-7 perpetuates ICTD in a NIX/BNIP3-indispensable fashion through dampening mitophagy that restricts redox robustness and preserves mitochondrial clearance programs. Adeno-associated viral vector 9 (AAV9)-NIX short hairpin RNA (shRNA) delivery ameliorates the anti-septic AKI phenotypes of IGFBP-7 KO. Activation of BNIP3-mediated mitophagy by mitochonic acid-5 (MA-5) effectively attenuates the IGFBP-7-dependent ICTD and septic AKI in CLP mice. SIGNIFICANCE: Our findings identify IGFBP-7 is an auto- and paracrine manipulator of NIX-mediated mitophagy for ICTD escalation and propose that targeting the IGFBP-7-dependent ICTD represents a novel therapeutic strategy against septic AKI.

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.

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.