Infectious diseases caused by bacterial pathogens are important public issues. In addition, due to the overuse of antibiotics, many multidrug-resistant bacterial pathogens have been widely encountered in clinical settings. Thus, the fast identification of bacteria pathogens and profiling of antibiotic resistance could greatly facilitate the precise treatment strategy of infectious diseases. So far, many conventional and molecular methods, both manual or automatized, have been developed for in vitro diagnostics, which have been proven to be accurate, reliable, and time efficient. Although Raman spectroscopy (RS) is an established technique in various fields such as geochemistry and material science, it is still considered as an emerging tool in research and diagnosis of infectious diseases. Based on current studies, it is too early to claim that RS may provide practical guidelines for microbiologists and clinicians because there is still a gap between basic research and clinical implementation. However, due to the promising prospects of label-free detection and noninvasive identification of bacterial infections and antibiotic resistance in several single steps, it is necessary to have an overview of the technique in terms of its strong points and shortcomings. Thus, in this review, we went through recent studies of RS in the field of infectious diseases, highlighting the application potentials of the technique and also current challenges that prevent its real-world applications.

With the low-cost, label-free, and nondestructive features, Raman spectroscopy is becoming an attractive technique with great potential to discriminate bacterial infections. In this pilot study, we analyzed surfaced-enhanced Raman spectroscopy (SERS) spectra via supervised machine learning algorithms, through which we confirmed the application potentials of the SERS technique in rapid and accurate discrimination of Klebsiella pneumoniae strains with different antibiotic resistance profiles.

EEG is widely applied in emotion recognition, brain disease detection, and other fields due to its high temporal resolution and non-invasiveness. However, artifact removal remains a crucial issue in EEG signal processing. Recently, with the rapid development of deep learning, there has been a significant transformation in the methods of EEG artifact removal. Nonetheless, existing research still exhibits some limitations: (1) insufficient capability to remove unknown artifacts; (2) inability to adapt to tasks where artifact removal needs to be applied to the overall input of multi-channel EEG data. Therefore, this study proposes CLEnet by integrating dual-scale CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory), and incorporating an improved EMA-1D (One-Dimensional Efficient Multi-Scale Attention Mechanism). CLEnet can extract the morphological features and temporal features of EEG, thereby separating EEG from artifacts. We conducted experiments on three datasets, and the results showed that CLEnet performed best. Specifically, in the task of removing artifacts from multi-channel EEG data containing unknown artifacts, CLEnet shows improvements of 2.45% and 2.65% in SNR(signal-to-noise ratio) and CC(average correlation coefficient). Moreover, RRMSE_t(relative root mean square error in the temporal domain) and RRMSE_f (relative root mean square error in the frequency domain) decrease by 6.94% and 3.30%.

Background: Mulberry leaf as a traditional Chinese medicine is able to treat obesity, diabetes, and dyslipidemia. It is well known that diabetes leads to intestinal microbiota dysbiosis. It is also recently discovered that liver glycogen structure is impaired in diabetic animals. Since mulberry leaves are able to improve the diabetic conditions through reducing blood glucose level, it would be interesting to investigate whether they have any positive effects on intestinal microbiota and liver glycogen structure. Methods: In this study, we first determined the bioactive components of ethanol extract of mulberry leaves via high-performance liquid chromatography (HPLC) and liquid chromatography/mass spectrometry (LC/MS). Murine animal models were divided into three groups, normal Sprague-Dawley (SD) rats, high-fat diet (HFD) and streptozotocin (STZ) induced type 2 diabetic rats, and HFD/STZ-induced rats administered with ethanol extract of mulberry leaves (200 mg/kg/day). Composition of intestinal microbiota was analyzed via metagenomics by sequencing the V3-V4 region of 16S rDNAs. Liver glycogen structure was characterized through size exclusion chromatography (SEC). Both Student’s t-test and Tukey’s test were used for statistical analysis. Results: A group of type 2 diabetic rat models were successfully established. Intestinal microbiota analysis showed that ethanol extract of mulberry leaves could partially change intestinal microbiota back to normal conditions. In addition, liver glycogen was restored from fragile state to stable state through administration of ethanol extract of mulberry leaves. Conclusions: This study confirms that the ethanol extract of mulberry leaves (MLE) ameliorates intestinal microbiota dysbiosis and strengthens liver glycogen fragility in diabetic rats. These finding can be helpful in discovering the novel therapeutic targets with the help of further investigations.

In this paper, we have conducted a thorough investigation into the shadow characteristics exhibited by the axially symmetric Finslerian extension of a Schwarzschild black hole and supplementary numerically simulated the motion behavior of photons near the black hole with respect to varying Finsler parameter $\epsilon$ and observation inclinations ${\theta }_o$. In this spacetime, the apparent shape and size of the black hole shadow remain the same as the one in the Schwarzschild black hole, regardless of changes in the Finsler parameter $\epsilon$. However, the apparent position of shadow in the observer’s sky plane appears to shift along the horizontal axis, and the distance moved is positively correlated with the value of the Finsler parameter $\epsilon$. Interestingly, with a fixed value of $\epsilon$, the displacement of the visual position of shadow is rectified as the observation angle decreases. In addition, it is demonstrated that the motion behavior of photons around the axially symmetric Finslerian extension of a Schwarzschild black hole has a greater degree of deflection than a Schwarzschild black hole, although their shadows are in some cases consistent in shape, size, and apparent position. These results can serve as a valuable tool in distinguishing the axially symmetric Finslerian extension of a Schwarzschild black hole from its counterparts in general relativity.