The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient\'s autonomy.

UNASSIGNED: To evaluate the diagnostic accuracy of machine learning (ML) techniques applied to radiomic features extracted from OCT and OCT angiography (OCTA) images for diabetes mellitus (DM), diabetic retinopathy (DR), and referable DR (R-DR) diagnosis.
UNASSIGNED: Cross-sectional analysis of a retinal image dataset from a previous prospective OCTA study (ClinicalTrials.govNCT03422965).
UNASSIGNED: Patients with type 1 DM and controls included in the progenitor study.
UNASSIGNED: Radiomic features were extracted from fundus retinographies, OCT, and OCTA images in each study eye. Logistic regression, linear discriminant analysis, support vector classifier (SVC)-linear, SVC-radial basis function, and random forest models were created to evaluate their diagnostic accuracy for DM, DR, and R-DR diagnosis in all image types.
UNASSIGNED: Area under the receiver operating characteristic curve (AUC) mean and standard deviation for each ML model and each individual and combined image types.
UNASSIGNED: A dataset of 726 eyes (439 individuals) were included. For DM diagnosis, the greatest AUC was observed for OCT (0.82, 0.03). For DR detection, the greatest AUC was observed for OCTA (0.77, 0.03), especially in the 3 × 3 mm superficial capillary plexus OCTA scan (0.76, 0.04). For R-DR diagnosis, the greatest AUC was observed for OCTA (0.87, 0.12) and the deep capillary plexus OCTA scan (0.86, 0.08). The addition of clinical variables (age, sex, etc.) improved most models AUC for DM, DR and R-DR diagnosis. The performance of the models was similar in unilateral and bilateral eyes image datasets.
UNASSIGNED: Radiomics extracted from OCT and OCTA images allow identification of patients with DM, DR, and R-DR using standard ML classifiers. OCT was the best test for DM diagnosis, OCTA for DR and R-DR diagnosis and the addition of clinical variables improved most models. This pioneer study demonstrates that radiomics-based ML techniques applied to OCT and OCTA images may be an option for DR screening in patients with type 1 DM.
UNASSIGNED: Proprietary or commercial disclosure may be found after the references.

The aim is to explore the predictive value of salivary bacteria for the presence of esophageal squamous cell carcinoma (ESCC). Saliva samples were obtained from 178 patients with ESCC and 101 healthy controls, and allocated to screening and verification cohorts, respectively. In the screening phase, after saliva DNA was extracted, 16S rRNA V4 regions of salivary bacteria were amplified by polymerase chain reaction (PCR) with high-throughput sequencing. Highly expressed target bacteria were screened by Operational Taxonomic Units clustering, species annotation and microbial diversity assessment. In the verification phase, the expression levels of target bacteria identified in the screening phase were verified by absolute quantitative PCR (Q-PCR). Receiver operating characteristic (ROC) curves were plotted to investigate the predictive value of target salivary bacteria. LEfSe analysis revealed higher proportions of Fusobacterium, Streptococcus and Porphyromonas, and Q-PCR assay showed significantly higher numbers of Streptococcus salivarius, Fusobacterium nucleatum and Porphyromonas gingivalis in patients with ESCC, when compared with healthy controls (all P < 0.05). The areas under the ROC curves for Streptococcus salivarius, Fusobacterium nucleatum, Porphyromonas gingivalis and the combination of the three bacteria for predicting patients with ESCC were 69%, 56.5%, 61.8% and 76.4%, respectively. The sensitivities corresponding to cutoff value were 69.3%, 22.7%, 35.2% and 86.4%, respectively, and the matched specificity were 78.4%, 96.1%, 90.2% and 58.8%, respectively. These highly expressed Streptococcus salivarius, Fusobacterium nucleatum and Porphyromonas gingivalis in the saliva, alone or in combination, indicate their predictive value for ESCC.

UNASSIGNED: Tumor delineation is required both for radiotherapy planning and quantitative imaging biomarker purposes. It is a manual, time- and labor-intensive process prone to inter- and intraobserver variations. Semi or fully automatic segmentation could provide better efficiency and consistency. This study aimed to investigate the influence of including and combining functional with anatomical magnetic resonance imaging (MRI) sequences on the quality of automatic segmentations.
UNASSIGNED: T2-weighted (T2w), diffusion weighted, multi-echo T2*-weighted, and contrast enhanced dynamic multi-echo (DME) MR images of eighty-one patients with rectal cancer were used in the analysis. Four classical machine learning algorithms; adaptive boosting (ADA), linear and quadratic discriminant analysis and support vector machines, were trained for automatic segmentation of tumor and normal tissue using different combinations of the MR images as input, followed by semi-automatic morphological post-processing. Manual delineations from two experts served as ground truth. The Sørensen-Dice similarity coefficient (DICE) and mean symmetric surface distance (MSD) were used as performance metric in leave-one-out cross validation.
UNASSIGNED: Using T2w images alone, ADA outperformed the other algorithms, yielding a median per patient DICE of 0.67 and MSD of 3.6 mm. The performance improved when functional images were added and was highest for models based on either T2w and DME images (DICE: 0.72, MSD: 2.7 mm) or all four MRI sequences (DICE: 0.72, MSD: 2.5 mm).
UNASSIGNED: Machine learning models using functional MRI, in particular DME, have the potential to improve automatic segmentation of rectal cancer relative to models using T2w MRI alone.

UNASSIGNED: Hepatic encephalopathy (HE) is a major complication after transjugular intrahepatic portosystemic shunt (TIPS) and is primarily influenced by the gut microbiota. We aimed to evaluate alterations in the microbiota after TIPS and the association between such alterations and HE.
UNASSIGNED: We conducted a prospective longitudinal study of 106 patients with cirrhosis receiving TIPS. Faecal samples were collected before and after TIPS, and the gut microbiota was analysed by 16S ribosomal RNA sequencing.
UNASSIGNED: Among all patients, 33 developed HE (HE+ group) within 6 months after TIPS and 73 did not (HE- group), and 18 died during follow-up. After TIPS, the autochthonous taxa increased, whereas the potential pathogenic taxa decreased in the HE- group, and the autochthonous taxon Lachnospiraceae decreased in the HE+ group. Furthermore, synergism among harmful bacteria was observed in all patients, which was weakened in the HE- group (p <0.001) but enhanced in the HE+ group (p <0.01) after TIPS. Variations of 5 autochthonous taxa, namely, Coprococcus, Ruminococcus, Blautia, Ruminococcaceae_uncultured, and Roseburia, were negatively correlated with the severity of HE. Notably, increased abundances of Coprococcus and Ruminococcus were protective factors against HE, and the incidences of HE in patients with improved, stable, and deteriorated microbiota after TIPS were 13.3, 25.9, and 68.2%, respectively. Higher total bilirubin level, Child-Pugh score, model for end-stage liver disease score, Granulicatella, and Alistipes and lower Subdoligranulum before TIPS were the independent risk factors for death.
UNASSIGNED: Alterations in gut dysbiosis were negatively related to the occurrence and severity of post-TIPS HE, and the pre-TIPS microbiota were associated with death, suggesting the gut microbiota could be a promising potential biological target for screening suitable patients receiving TIPS and prevention and treatment of post-TIPS HE.
UNASSIGNED: Alterations in the gut microbiota after transjugular intrahepatic portosystemic shunt (TIPS) and the relationship between such alterations and post-TIPS hepatic encephalopathy (HE) remain unclear. We therefore performed this study and found that after TIPS, restoration of the gut microbiota, mainly characterised by expansion of autochthonous taxa, depletion of harmful taxa, and weakening of synergism among harmful bacteria, was inversely related to the occurrence and severity of post-TIPS HE.

Untreated familial hypercholesterolemia (FH) leads to atherosclerosis and early cardiovascular disease. Mutations in the low-density lipoprotein receptor (LDLr) gene constitute the major cause of FH, and the high number of mutations already described in the LDLr makes necessary cascade screening or in vitro functional characterization to provide a definitive diagnosis. Implementation of high-predicting capacity software constitutes a valuable approach for assessing pathogenicity of LDLr variants to help in the early diagnosis and management of FH disease. This work provides a reliable machine learning model to accurately predict the pathogenicity of LDLr missense variants with specificity of 92.5% and sensitivity of 91.6%.

Angelica dahurica is a famous functional food and herb. To guarantee quality of A. dahurica, a strategy \"Q-markers targeted screening\" was successfully developed by sufficient extraction of compounds and the targeted screening of qualitative and quantitative markers calculated through chemometric methods based fingerprints. Accelerated solvent extraction was selected due to its prominent advantages exhibiting the maximum extraction yields and varieties of compounds and especially excellent reproducibility (RSD < 1). After extraction, the fingerprints of A. dahuricae samples were established. For the preliminary herb authenticity, the targeted screening of 23 quantitative markers were performed by similarity analysis and hierarchical cluster analysis based on the fingerprints, which were identified by liquid chromatography tandem mass spectrometry (LC-MS). Subsequently, for further quality control, the targeted screening of nine quantitative markers were done by similarity analysis & linear discriminant analysis, which were determined by LC. Lastly, the strategy was successfully applied to quality assessment of A. dahurica samples.

Parkinson\'s disease (PD) is the second most common neurodegenerative disease, but none of the current treatments for PD can halt the progress of the disease due to the limited understanding of the pathogenesis. In PD development, the communication between the brain and the gastrointestinal system influenced by gut microbiota is known as microbiota-gut-brain axis. However, the explicit mechanisms of microbiota dysbiosis in PD development have not been well elucidated yet. FLZ, a novel squamosamide derivative, has been proved to be effective in many PD models and is undergoing the phase I clinical trial to treat PD in China. Moreover, our previous pharmacokinetic study revealed that gut microbiota could regulate the absorption of FLZ in vivo. The aims of our study were to assess the protective effects of FLZ treatment on PD and to further explore the underlying microbiota-related mechanisms of PD by using FLZ as a tool. In the current study, chronic oral administration of rotenone was utilized to induce a mouse model to mimic the pathological process of PD. Here we revealed that FLZ treatment alleviated gastrointestinal dysfunctions, motor symptoms, and dopaminergic neuron death in rotenone-challenged mice. 16S rRNA sequencing found that PD-related microbiota alterations induced by rotenone were reversed by FLZ treatment. Remarkably, FLZ administration attenuated intestinal inflammation and gut barrier destruction, which subsequently inhibited systemic inflammation. Eventually, FLZ treatment restored blood-brain barrier structure and suppressed neuroinflammation by inhibiting the activation of astrocytes and microglia in the substantia nigra (SN). Further mechanistic research demonstrated that FLZ treatment suppressed the TLR4/MyD88/NF-κB pathway both in the SN and colon. Collectively, FLZ treatment ameliorates microbiota dysbiosis to protect the PD model via inhibiting TLR4 pathway, which contributes to one of the underlying mechanisms beneath its neuroprotective effects. Our research also supports the importance of microbiota-gut-brain axis in PD pathogenesis, suggesting its potential role as a novel therapeutic target for PD treatment.

Carotenoids are powerful antioxidants capable of helping to protect the skin from the damaging effects of exposure to sun by reducing the free radicals in skin produced by exposure to ultraviolet radiation, and they may also have a physical protective effect in human skin. Since carotenoids are lipophilic molecules which can be ingested with the diet, they can accumulate in significant quantities in the skin. Several studies on humans have been conducted to evaluate the protective function of carotenoids against various diseases, but there is very limited published information available to understand the mechanism of carotenoid bioavailability in animals. The current study was conducted to investigate the skin carotenoid level (SCL) in two cattle skin sets - weaners with an unknown feeding regime and New Generation Beef (NGB) cattle with monitored feed at three different ages. Rapid analytical and sensitive Raman spectroscopy has been shown to be of interest as a powerful technique for the detection of carotenoids in cattle skin due to the strong resonance enhancement with 532 nm laser excitation. The spectral difference of both types of skin were measured and quantified using univariate and linear discriminant analysis. SCL was higher in NGB cattle than weaners and there is a perfect classification accuracy between weaners and NGB cattle skin using carotenoid markers as a basis. Further work carried out on carotenoid rich NGB cattle skin of 8, 12 and 24 months of age identified an increasing trend in SCL with age. The present work validated the ability of Raman spectroscopy to determine the skin carotenoid level in cattle by comparing it with established HPLC methods. There is an excellent correlation of R2 = 0.96 between the two methods that could serve as a model for future application for larger population studies.

The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.