A nomogram and heat map based on LASSO-Cox regression for predicting the risk of early-stage severe fever with thrombocytopenia syndrome patients developing into critical illness at 7-day and 14-day.

Severe fever with thrombocytopenia syndrome (SFTS) represents an emerging infectious disease characterized by a substantial mortality risk. Early identification of patients is crucial for effective risk assessment and timely interventions. In the present study, least absolute shrinkage and selection operator (LASSO)-Cox regression analysis was conducted to identify key risk factors associated with progression to critical illness at 7-day and 14-day. A nomogram was constructed and subsequently assessed for its predictive accuracy through evaluation and validation processes. The risk stratification of patients was performed using X-tile software. The performance of this risk stratification system was assessed using the Kaplan-Meier method. Additionally, a heat map was generated to visualize the results of these analyses. A total of 262 SFTS patients were included in this study, and four predictive factors were included in the nomogram, namely viral copies, aspartate aminotransferase (AST) level, C-reactive protein (CRP), and neurological symptoms. The AUCs for 7-day and 14-day were 0.802 [95% confidence interval (CI): 0.707-0.897] and 0.859 (95% CI: 0.794-0.925), respectively. The nomogram demonstrated good discrimination among low, moderate, and high-risk groups. The heat map effectively illustrated the relationships between risk groups and predictive factors, providing valuable insights with high predictive and practical significance.

Evaluation of probiotic efficacy of indigenous yeast strain, Saccharomyces cerevisiae Y-89 isolated from a traditional fermented beverage of West Bengal, India having protective effect against DSS-induced colitis in experimental mice.

Increasing awareness regarding health promotion and disease prevention has driven inclusion of fermented foods and beverages in the daily diet. These are the enormous sources of beneficial microbes, probiotics. This study aims to isolate yeast strains having probiotic potential and effectivity against colitis. Initially, ninety-two yeast strains were isolated from Haria, an ethnic fermented beverage of West Bengal, India. Primary screening was done by their acid (pH 4) and bile salt (0.3%) tolerance ability. Four potent isolates were selected and found effective against Entamoeba histolytica, as this human pathogen is responsible to cause colitis. They were identified as Saccharomyces cerevisiae. They showed luxurious growth even at 37 oC, tolerance up to 5% of NaCl, resistance to gastric juice and high bile salt (2.0%) and oro-gastrointestinal transit tolerance. They exhibited good auto-aggregation and co-aggregation ability and strong hydrophobicity. Finally, heat map and principal component analysis revealed that strain Y-89 was the best candidate. It was further characterised and found to have significant protective effects against DSS-induced colitis in experimental mice model. It includes improvement in colon length, body weight and organ indices; reduction in disease activity index; reduction in cholesterol, LDL, SGPT, SGOT, urea and creatinine levels; improvement in HDL, ALP, total protein and albumin levels; decrease in coliform count and restoration of tissue damage. This study demonstrates that the S. cerevisiae strain Y-89 possesses remarkable probiotic traits and can be used as a potential bio-therapeutic candidate for the prevention of colitis.

Separation, identification, and fingerprinting of antioxidant components in persimmon (Diospyros kaki) leaves by offline two-dimensional liquid chromatography with electrochemical detection and tandem mass spectrometry.

In this work, the antioxidant components in persimmon (Diospyros kaki) leaves were separated by offline two-dimensional liquid chromatography-electrochemical detection (LC×LC-ECD) and identified by LC-tandem mass spectrometry (LC-MS/MS). A total of 33 antioxidants, mainly proanthocyanidins, and glycosides of kaempferol and quercetin, were identified. The antioxidant assays demonstrated that the fractions collected from the first-dimension LC (1D-LC) possessed considerable radical scavenging capabilities, with correlation coefficients of peak area versus radical scavenging capability of 1,1-diphenyl-2-picrylhydrazyl and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) being 0.9335 and 0.9116, respectively. The fingerprinting showed that 37 peaks were present in all samples. The major antioxidant components of persimmon leaves were the glycosides of kaempferol and quercetin. Finally, fourteen antioxidants were quantitatively assessed. Offline LC×LC provided high peak capacity and separation; ECD enabled specific screening and detection of antioxidant components; and MS/MS provided excellent identification capability. In this study, the combination of the three approaches was utilized to screen for antioxidant components in persimmon leaves, with satisfactory findings. In conclusion, this technique is an effective means for rapid analysis of antioxidant components and quality control of medicinal plants, achieving rapid separation of congeners and facilitating more accurate qualitative and quantitative analyses.

BarlowTwins-CXR: enhancing chest X-ray abnormality localization in heterogeneous data with cross-domain self-supervised learning.

BACKGROUND: Chest X-ray imaging based abnormality localization, essential in diagnosing various diseases, faces significant clinical challenges due to complex interpretations and the growing workload of radiologists. While recent advances in deep learning offer promising solutions, there is still a critical issue of domain inconsistency in cross-domain transfer learning, which hampers the efficiency and accuracy of diagnostic processes. This study aims to address the domain inconsistency problem and improve autonomic abnormality localization performance of heterogeneous chest X-ray image analysis, particularly in detecting abnormalities, by developing a self-supervised learning strategy called "BarlwoTwins-CXR". METHODS: We utilized two publicly available datasets: the NIH Chest X-ray Dataset and the VinDr-CXR. The BarlowTwins-CXR approach was conducted in a two-stage training process. Initially, self-supervised pre-training was performed using an adjusted Barlow Twins algorithm on the NIH dataset with a Resnet50 backbone pre-trained on ImageNet. This was followed by supervised fine-tuning on the VinDr-CXR dataset using Faster R-CNN with Feature Pyramid Network (FPN). The study employed mean Average Precision (mAP) at an Intersection over Union (IoU) of 50% and Area Under the Curve (AUC) for performance evaluation. RESULTS: Our experiments showed a significant improvement in model performance with BarlowTwins-CXR. The approach achieved a 3% increase in mAP50 accuracy compared to traditional ImageNet pre-trained models. In addition, the Ablation CAM method revealed enhanced precision in localizing chest abnormalities. The study involved 112,120 images from the NIH dataset and 18,000 images from the VinDr-CXR dataset, indicating robust training and testing samples. CONCLUSION: BarlowTwins-CXR significantly enhances the efficiency and accuracy of chest X-ray image-based abnormality localization, outperforming traditional transfer learning methods and effectively overcoming domain inconsistency in cross-domain scenarios. Our experiment results demonstrate the potential of using self-supervised learning to improve the generalizability of models in medical settings with limited amounts of heterogeneous data. This approach can be instrumental in aiding radiologists, particularly in high-workload environments, offering a promising direction for future AI-driven healthcare solutions.

Screening and characterization of Sahiwal cattle calves-origin lactic acid bacteria based on desired probiotic attributes for potential application.

Probiotics are living microorganisms that confer health benefits to host when administered in adequate amounts. To develop novel host-specific probiotic for their application as feed additive, the present study was undertaken to isolate and characterize probiotic strains of indigenous cattle-calves origin. A total of 55 colonies were isolated from 12 healthy calves, with 34 of the isolates being Gram-positive, catalase-negative and vancomycin-resistant. Furthermore, eleven isolates showed tolerance to acid (pH 2.0) and thirteen isolates tolerated bile salts (0.3%). Seven common acid and bile tolerance strains were further investigated for other probiotic attributes and displayed higher (p< 0.05) auto-aggregation and cell surface hydrophobicity values. Moreover, all seven isolates had potent antibacterial activity against pathobiont E. coli as well as significant co-aggregation capacity and enzyme activity. In vitro biosafety assessment revealed that all seven isolates were non-hemolytic, negative for mucin degradation and susceptible to most of the antibiotics. Based on the obtained findings, heatmap and principal component analysis identified four highly effective probiotic candidates confirmed by 16S rDNA sequencing as Limosilactobacillus reuteri SW23, Limosilactobacillus reuteri SW26, Limosilactobacillus reuteri SW27 and Enterococcus faecium SW28, respectively. Further studies on biosafety aspect are warranted for the application of these strains in animal as potential probiotics.HIGHLIGHTSL. reuteri SW23, L. reuteri SW26, L. reuteri SW28 and Enterococcus faecium SW28 were successfully isolated and identified from indigenous calves' feces.These microbes were characterized for potential probiotics attributes.Heatmap analysis and principal component analysis (PCA) was used along with probiotic attributes to select highly effective probiotic candidates.

Stifle joint osteoarthritis in small-breed and medium-breed dogs is more severe after cranial cruciate ligament injury than medial patellar luxation.

This retrospective observational study aimed to characterize the severity and distribution of OA in the stifle joints of small and medium dogs with CCL injury and/or MPL. Radiographs of the stifle joints from 218 dogs from 10 small and medium breeds were included; 127 joints had CCL injury, 76 joints had MPL, and 73 joints had CCL injury and MPL. OA was graded at 33 sites within the joint. The mean ± SD OA score was 20.3 ± 9.9. For all joints, OA was more severe in heavier than lighter dogs (P = 0.003). Joints with MPL (14.9 ± 8.2) had lower OA scores than joints with CCL injury (22.2 ± 10.0, P = 0.003) or CCL injury and MPL (22.6 ± 9.4, P < 0.001). OA scores were higher in joints with MPL for older dogs (r = 0.408, P < 0.001) but did not change with age in joints with CCL injury. The pattern of OA did not differ among joints with CCL injury or MPL. The retrospective nature of the study limited findings to associations but did not allow conclusions regarding factors causing OA or enhancing its progression. We concluded that, in small- and medium-breed dogs, the patterns of stifle OA joint after CCL injury and MPL are similar. Radiographic OA after CCL injury is more severe than MPL. An increase in age leads to an increase in OA at the time of presentation at a referral hospital in stifle joints with MPL and without CCL injury.

Dendrograms, heat maps and principal component analysis - the practical use of statistical methods for source apportionment of trace elements in PM10.

Dendrogram (DE), heat map (HM) and principal component analysis (PCA) methods were used in order to identify possible emission sources of As, Cd, Co, Cr, Hg, Mn, Ni, Pb, Sb and Se in ambient PM10 collected in the surroundings of working power plants. Each statistical tool resulted in slightly different clusters. The best approximation of possible emission sources was received by the use of statistical analysis of trace-element concentrations combined with characterization of the sampling sites. In the study, PCA was indicated as the most useful statistical tool for source apportionment of trace elements in PM10. Major sources identified by PCA included: (1) coal combustion, (2) soil and road-dust resuspension, (3) the use of pesticides and (4) waste incineration.

Advancing clinical genomics and precision medicine with GVViZ: FAIR bioinformatics platform for variable gene-disease annotation, visualization, and expression analysis.

BACKGROUND: Genetic disposition is considered critical for identifying subjects at high risk for disease development. Investigating disease-causing and high and low expressed genes can support finding the root causes of uncertainties in patient care. However, independent and timely high-throughput next-generation sequencing data analysis is still a challenge for non-computational biologists and geneticists. RESULTS: In this manuscript, we present a findable, accessible, interactive, and reusable (FAIR) bioinformatics platform, i.e., GVViZ (visualizing genes with disease-causing variants). GVViZ is a user-friendly, cross-platform, and database application for RNA-seq-driven variable and complex gene-disease data annotation and expression analysis with a dynamic heat map visualization. GVViZ has the potential to find patterns across millions of features and extract actionable information, which can support the early detection of complex disorders and the development of new therapies for personalized patient care. The execution of GVViZ is based on a set of simple instructions that users without a computational background can follow to design and perform customized data analysis. It can assimilate patients' transcriptomics data with the public, proprietary, and our in-house developed gene-disease databases to query, easily explore, and access information on gene annotation and classified disease phenotypes with greater visibility and customization. To test its performance and understand the clinical and scientific impact of GVViZ, we present GVViZ analysis for different chronic diseases and conditions, including Alzheimer's disease, arthritis, asthma, diabetes mellitus, heart failure, hypertension, obesity, osteoporosis, and multiple cancer disorders. The results are visualized using GVViZ and can be exported as image (PNF/TIFF) and text (CSV) files that include gene names, Ensembl (ENSG) IDs, quantified abundances, expressed transcript lengths, and annotated oncology and non-oncology diseases. CONCLUSIONS: We emphasize that automated and interactive visualization should be an indispensable component of modern RNA-seq analysis, which is currently not the case. However, experts in clinics and researchers in life sciences can use GVViZ to visualize and interpret the transcriptomics data, making it a powerful tool to study the dynamics of gene expression and regulation. Furthermore, with successful deployment in clinical settings, GVViZ has the potential to enable high-throughput correlations between patient diagnoses based on clinical and transcriptomics data.

A novel machine learning-based algorithm to identify and classify lesions and anatomical landmarks in colonoscopy images.

OBJECTIVES: Computer-aided diagnosis (CAD)-based artificial intelligence (AI) has been shown to be highly accurate for detecting and characterizing colon polyps. However, the application of AI to identify normal colon landmarks and differentiate multiple colon diseases has not yet been established. We aimed to develop a convolutional neural network (CNN)-based algorithm (GUTAID) to recognize different colon lesions and anatomical landmarks. METHODS: Colonoscopic images were obtained to train and validate the AI classifiers. An independent dataset was collected for verification. The architecture of GUTAID contains two major sub-models: the Normal, Polyp, Diverticulum, Cecum and CAncer (NPDCCA) and Narrow-Band Imaging for Adenomatous/Hyperplastic polyps (NBI-AH) models. The development of GUTAID was based on the 16-layer Visual Geometry Group (VGG16) architecture and implemented on Google Cloud Platform. RESULTS: In total, 7838 colonoscopy images were used for developing and validating the AI model. An additional 1273 images were independently applied to verify the GUTAID. The accuracy for GUTAID in detecting various colon lesions/landmarks is 93.3% for polyps, 93.9% for diverticula, 91.7% for cecum, 97.5% for cancer, and 83.5% for adenomatous/hyperplastic polyps. CONCLUSIONS: A CNN-based algorithm (GUTAID) to identify colonic abnormalities and landmarks was successfully established with high accuracy. This GUTAID system can further characterize polyps for optical diagnosis. We demonstrated that AI classification methodology is feasible to identify multiple and different colon diseases.

Sequential uptake of aldoses over fructose and enhanced phosphate solubilization in Rhizobium sp. RM.

Rhizobium sp. RM solubilized tri-calcium phosphate (TCP: 324-463 µg ml-1) and rock phosphate (RP: 36-46.58 µg ml-1) in the presence of common rhizospheric sugars-glucose, arabinose, xylose and their combinations. Fructose, though did not support RP solubilization individually, surprisingly solubilized significantly higher phosphate when combined with aldoses. The highest TCP (644 µg ml-1) and RP (75 µg ml-1) solubilization was achieved in fructose + glucose combination. Presence of gluconate, malate and oxalate in culture supernatant indicated functioning of periplasmic glucose oxidation, the non-phosphorylative arabinose dehydrogenase pathway and the tricarboxylate (TCA) cycle, respectively. Aldoses, when present together, were co-utilized (monoauxic growth) however, when added with fructose, prevented the uptake of fructose yielding a typical diauxic growth. This presented an unusual sequential utilization of aldoses over a ketose (fructose) in strain RM. The prevention of fructose uptake by aldoses was investigated through real-time expression of key genes coding fructose transport proteins and initial enzymes of sugar metabolism. Fructose was actively transported via fructose-specific ABC transporters as suggested by upregulation of frcB and frcC only in fructose and fructose growth phases of fructose + aldose combinations. The probable route of initial fructose metabolism involved either fructokinase and/or xylose isomerase, as confirmed by enzyme activities. The upregulation of hfq and hprK genes only in aldose phase of fructose + aldose combinations suggested their possible involvement in governing the preferential utilization. The novel aspects of this study are enhanced organic acid mediated P solubilization in fructose + aldose combinations and a rare hierarchy of aldoses over fructose which is possibly regulated at the level of fructose transport and fructokinase. KEY POINTS: • Sugars when provided in different dual combinations, supported enhanced P solubilization from complex phosphate sources like TCP and RP in Rhizobium sp. RM. • Transcriptional status of genes in cells of RM when grown in different individual sugars and their combinations suggested that fructose might be a less preferred carbon source and hence was utilized after aldoses with the possible regulation by Hfq and HPrK. • First study to present a unique phenomenon of sequential utilization of aldoses (glucose, arabinose and xylose) over fructose in a concentration-independent manner in Rhizobium sp. RM. and to present the effect of dual combinations of sugars on organic acid mediated P solubilization trait of rhizobia.

Ophthalmic Trauma Correlation Matrix (OTCM): a potential novel tool for evaluation of concomitant ocular tissue damage in open globe injuries.

PURPOSE: To introduce a novel tool to investigate the correlation between concomitant injuries and primary open globe injury (OGI) in the setting of ophthalmic trauma, the "Ophthalmic Trauma Correlation Matrix" (OTCM). METHODS: Retrospective cohort review, performed at a tertiary referral eye care center in Eastern Nepal, involving all eyes with OGI meeting the inclusion criteria from 2015-2018. Clinical data including details of primary injury, concurrent injuries, and clinical course were noted from hospital medical records. A correlation matrix chart was devised using matrix correlation and Pearson's correlation coefficient. This chart was then used to evaluate the association of the various injuries in the setting of OGI. RESULTS: A total of 109 eyes with OGI were included. Majority of the eyes (78, 71.6%) had zone I injuries, while most of the eyes (66, 60.6%) had penetrating injury. The most frequent concomitant injuries in all zones of OGI were traumatic lens injury (77, 70.64%), followed by hyphema (48, 44.03%), and vitreous hemorrhage (35, 32.11%). The most common concomitant injury associated with zone I was hyphema (0.873), while traumatic subluxation/cataract (0.894) and vitreous hemorrhage (0.972) were commonly associated with zone II and III, respectively. CONCLUSIONS: OTCM could be a useful tool to manage injuries related to the primary ocular injury. This additional information will aid in the prognostication, planning, and management of OGI and potentially prevent repeat surgeries and inadequate treatments.

Are the birthdates of our ancestors real? Date of birth misregistration in twentieth-century Poland.

This study aimed to explore the phenomenon of birthdate misregistration, using birth data from 45,226,875 Polish citizens, that is, all those born 1900-2000 and registered in Poland's Universal Electronic System for Registration of the Population (PESEL). I transformed the data into a daily series of births, detrended by dividing each value by the daily average for the relevant year. Next, I selected the dates with the highest deviations based on the coefficients of the linear regression model with dummy variables. Finally, I estimated the size of the phenomenon in subsequent years by comparing the numbers of births on selected dates to their expected values. This paper is the first to document the specificity, scale, duration, and probable causes of birthdate misregistration in Poland in the twentieth century.

Visualization of Dust Generation in Outdoor Workplaces Using A Wearable Particle Monitor and Global Navigation Satellite System.

We manufactured a wearable particle monitor (WPM), which is a simple and low-cost dust monitor. We aimed to evaluate the usefulness of the device by using it and location information of a Global Navigation Satellite System (GNSS) to measure dust generation in outdoor workplaces. We used nine WPMs and a particle counter KC-52 to measure in parallel the dust concentration diffusing standard particles in a dust exposure apparatus to evaluate the measurability of the WPM, and visualized dust generation in outdoor workplaces to evaluate its usability. We obtained location information using a GNSS in parallel with measuring with the WPM. The measured values of the WPM followed the measured values of the KC-52, with a strong correlation of the values between the KC-52 and each WPM. The discrepancy among devices tended to increase, however, because the measured values of the WPMs increased. For outdoor measurements, we could create a heat map of the relative values of dust generation by combining two data of the WPM and the GNSS. The methods of using the WPM could overview the conditions needed to produce dust emissions in dust-generating workplaces.

Estimating the cumulative risk of postnatal depressive symptoms: the role of insomnia symptoms across pregnancy.

PURPOSE: Insomnia symptoms during late pregnancy are a known risk for postnatal depressive symptoms (PDS). However, the cumulative effect of various risk factors throughout pregnancy has not been explored. Our aim was to test how various insomnia symptoms (sleep latency, duration, quality, frequent night awakenings, early morning awakenings) and other risk factors (e.g., history of depression, symptoms of depression and anxiety, as well as sociodemographic factors) in early, mid-, and late pregnancy predict PDS. METHODS: Using data from the FinnBrain Birth Cohort Study and logistic regression analyses, we investigated the associations of distinct insomnia symptoms at gw 14, 24, and 34 with depressive symptoms (Edinburgh Postnatal Depression Scale score ≥ 11) 3 months postnatally. We also calculated separate and combined predictive models of PDS for each pregnancy time point and reported the odds ratios for each risk group. RESULTS: Of the 2224 women included in the study, 7.1% scored EPDS ≥ 11 3 months postnatally. Our predictive models indicated that sleep latency of ≥ 20 min, anxiety in early pregnancy, and insufficient sleep during late pregnancy predicted the risk of PDS. Furthermore, we found highly elevated odds ratios in early, mid-, and late pregnancy for women with multiple PDS risk factors. CONCLUSION: Screening of long sleep latency and anxiety during early pregnancy, in addition to depression screening, could be advisable. Odds ratios of risk factor combinations demonstrate the magnitude of cumulating risk of PDS when multiple risk factors are present.

Gaze Focalization System for Driving Applications Using OpenFace 2.0 Toolkit with NARMAX Algorithm in Accidental Scenarios.

Monitoring driver attention using the gaze estimation is a typical approach used on road scenes. This indicator is of great importance for safe driving, specially on Level 3 and Level 4 automation systems, where the take over request control strategy could be based on the driver's gaze estimation. Nowadays, gaze estimation techniques used in the state-of-the-art are intrusive and costly, and these two aspects are limiting the usage of these techniques on real vehicles. To test this kind of application, there are some databases focused on critical situations in simulation, but they do not show real accidents because of the complexity and the danger to record them. Within this context, this paper presents a low-cost and non-intrusive camera-based gaze mapping system integrating the open-source state-of-the-art OpenFace 2.0 Toolkit to visualize the driver focalization on a database composed of recorded real traffic scenes through a heat map using NARMAX (Nonlinear AutoRegressive Moving Average model with eXogenous inputs) to establish the correspondence between the OpenFace 2.0 parameters and the screen region the user is looking at. This proposal is an improvement of our previous work, which was based on a linear approximation using a projection matrix. The proposal has been validated using the recent and challenging public database DADA2000, which has 2000 video sequences with annotated driving scenarios based on real accidents. We compare our proposal with our previous one and with an expensive desktop-mounted eye-tracker, obtaining on par results. We proved that this method can be used to record driver attention databases.

Antifungal potential of zinc against leaf spot disease in chili pepper caused by Alternaria alternata.

The fungal pathogen, Alternaria alternata is responsible for causing leaf spot disease in many plants, including chili pepper. Zinc (Zn) an essential micronutrient for plant growth, also increases resistance in plants against diseases, and also acts as an antifungal agent. Here, in vitro effects of ZnSO4 on the propagation of A. alternata were investigated, and also in vivo, the effect of foliar application of ZnSO4 was investigated in chili pepper plants under disease stress. In vitro, ZnSO4 inhibited fungal growth in a dose-dependent manner, with complete inhibition being observed at the concentration of 8.50 mM. Hyphae and conidial damage were observed along with abnormal activity of antioxidant enzymes, Fourier-transform infrared spectroscopy confirmed the major changes in the protein structure of the fungal biomass after Zn accumulation. In vivo, pathogen infection caused the highest leaf spot disease incidence, and cumulative disease index, which resulted in a significant reduction in the plant's growth (length and biomass), and physiochemical traits (photosynthetic pigment, activity of catalase, peroxidase, polyphenol oxidase, and phenylalanine ammonia lyase). The heat map and principal component analysis based on disease, growth and, physico-chemical variables generated useful information regarding the best treatment useful for disease management. Foliar Zn (0.036 mM) acted as a resistance inducer in chili pepper plants that improved activities of antioxidants (CAT and POX), and defense compounds (PPO and PAL), while managing 77% of disease. The study indicated foliar ZnSO4 as an effective and sustainable agriculture practice to manage Alternaria leaf spot disease in chili pepper plants.

[Analysis and evaluation of mineral elements of Chrysanthemum morifolium for medicinal and tea use of different germplasm resources].

In this study, 23 germplasm resources of Chrysanthemum morifolium used in medicine and tea were collected from Dabie Mountains and its surrounding producing areas, and the contents of 13 mineral elements were determined and compared. The thermal maps of correlation analysis, principal component analysis and cluster analysis were used for comprehensive evaluation. The results showed that the average content of each element in Ch. morifolium of different germplasm resources was: K>N>P>Mg>Ca>Fe>Mn>Zn>Cu>Ni>Cr>Pb>Cd, and the leaves were: K>N>Ca>Mg>P>Fe>Mn>Zn>Cr>Cu>Ni>Pb>Cd. There are rich contents of N, P, K, Ca, Mg and Fe in Ch. morifolium flowers and their leaves, among them, K element has the largest change range, while N, Ca, Fe, Mg and Zn elements have a larger change range. The absorption and accumulation of each element in the leaves of different germplasm resources varied greatly. The correlation analysis shows that there is a strong positive correlation between Ca element, Mg, Mn and Cd element.Principal component analysis in Ch. morifolium flowers characteristic elements for Mn, Cr, Cu, P, K, can be used as a Ch. morifolium resources to identify the characteristics of the elements, choose top five principal component(F1-F5) comprehensive evalua-tion of medicinal Ch. morifolium, scored in the top five varieties for Hangiu-Fuhuangju, Hangju-Xiaoyangju, Hangju-Sheyangju, Hangju-Dayanghua, Hangju-Subeiju,indicates that in terms of mineral elements, the five medicinal Ch. morifolium resources quality is better. The PCA score chart can divide 23 Ch. morifolium resources into 4 groups, and the cluster analysis heat map divides 23 Ch. morifolium resources into 5 groups. All the Ch. morifolium resources of the same type can be well clustered together, indicating that the difference in mineral element content of Ch. morifolium germplasm resources is closely related to genetic factors.

Heat map visualization for electrocardiogram data analysis.

BACKGROUND: Most electrocardiogram (ECG) studies still take advantage of traditional statistical functions, and the results are mostly presented in tables, histograms, and curves. Few papers display ECG data by visual means. The aim of this study was to analyze and show data for electrocardiographic left ventricular hypertrophy (LVH) with ST-segment elevation (STE) by a heat map in order to explore the feasibility and clinical value of heat mapping for ECG data visualization. METHODS: We sequentially collected the electrocardiograms of inpatients in the First Affiliated Hospital of Shantou University Medical College from July 2015 to December 2015 in order to screen cases of LVH with STE. HemI 1.0 software was used to draw heat maps to display the STE of each lead of each collected ECG. Cluster analysis was carried out based on the heat map and the results were drawn as tree maps (pedigree maps) in the heat map. RESULTS: In total, 60 cases of electrocardiographic LVH with STE were screened and analyzed. STE leads were mainly in the V1, V2 and V3 leads. The ST-segment shifts of each lead of each collected ECG could be conveniently visualized in the heat map. According to cluster analysis in the heat map, STE leads were clustered into two categories, comprising of the right precordial leads (V1, V2, V3) and others (V4, V5, V6, I, II, III, aVF, aVL, aVR). Moreover, the STE amplitude in 40% (24 out of 60) of cases reached the threshold specified in the STEMI guideline. These cases also could be fully displayed and visualized in the heat map. Cluster analysis in the heat map showed that the III, aVF and aVR leads could be clustered together, the V1, V2, V3 and V4 leads could be clustered together, and the V5, V6, I and aVL leads could be clustered together. CONCLUSION: Heat maps and cluster analysis can be used to fully display every lead of each electrocardiogram and provide relatively comprehensive information.

Chemical Composition and Antibacterial Activity of Lippia multiflora Moldenke Essential Oil from Different Regions of Angola.

The purpose of the study was to determine the chemical composition and antibacterial activity of Lippia multiflora Moldenke essential oils (EOs) collected in different regions of Angola. Antibacterial activity was evaluated using the agar wells technique and vapour phase test. Analysis of the oils by GC/MS identified thirty-five components representing 67.5 to 100% of the total oils. Monoterpene hydrocarbons were the most prevalent compounds, followed by oxygenated monoterpenes. The content of the compounds varied according to the samples. The main components were Limonene, Piperitenone, Neral, Citral, Elemol, p-cymene, Transtagetone, and Artemisia ketone. Only one of the eleven samples contained Verbenone as the majority compound. In the vapour phase test, a single oil was the most effective against all the pathogens studied. The principal component analysis (PCA) and hierarchical cluster analysis (HCA) of components of the selected EOs and inhibition zone diameter values of agar wells technique allowed us to identify a variability between the plants from the two provinces, but also intraspecific variability between sub-groups within a population. Each group of essential oils constituted a chemotype responsible for their bacterial inhibition capacity. The results presented here suggest that Angolan Lippia multiflora Moldenke has antibacterial properties and could be a potential source of antimicrobial agents for the pharmaceutical and food industry.

Metabolomics study of polysaccharide extracts from Polygonatum sibiricum in mice based on 1 H NMR technology.

BACKGROUND: Polygonatum sibiricum Liliaceae perennial herb, as a commonly used medicine and food homologous plant, has been widely used in clinical practice of Chinese medicine since ancient times, with a history of 2000 years. As the main active ingredient, P. sibiricum polysaccharides have important pharmacological effects in blood sugar reduction and antitumor, antioxidant and liver protection. RESULTS: Mouse models of P. sibiricum polysaccharides were used in combination with 1 H NMR to investigate the metabolic regulation mechanism in mouse tissue and blood. The metabolite maps of the control group and the drug group in the liver had significant changes. The main differential metabolites were glucose 6-phosphate, inositol, lactose, glutamylglycine, galactose, rhamnose, cis-aconitic acid and histidine, indicating that there was definite correlation between the metabolic detection based on 1 H NMR and the metabolic characteristics of P. sibiricum. The common differential metabolites obtained by overall metabolism analysis were 3-hydroxybutyric acid, d-ribose, adenosine phosphate, inositol, fructose 6-phosphate, histidine, aspartic acid and cis-aconitic acid. CONCLUSIONS: This work forms the basis for identification of metabolic states combined with metabolic pathways, which could be used as diagnostic and prognostic indicators, providing therapeutic targets for new diseases. © 2020 Society of Chemical Industry.