المؤلفون: Dubey P; Electrical and Electronics Engineering, Birla Institute of Technology, Ranchi, Jharkhand, 835215, India. pdubey0595@gmail.com., Kumar S; Electrical and Electronics Engineering, Birla Institute of Technology, Ranchi, Jharkhand, 835215, India.

المصدر: Scientific reports [Sci Rep] 2023 Aug 23; Vol. 13 (1), pp. 13745. Date of Electronic Publication: 2023 Aug 23.

نوع المنشور: Journal Article

بيانات الدورية: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE

مواضيع طبية MeSH: Prostatic Neoplasms*/diagnosis , Principal Component Analysis*/methods , Discriminant Analysis* , Supervised Machine Learning*, Datasets as Topic ; Humans ; Male ; Algorithms

مستخلص: This investigation aimed to assess the effectiveness of different classification models in diagnosing prostate cancer using a screening dataset obtained from the National Cancer Institute's Cancer Data Access System. The dataset was first reduced using the PCLDA method, which combines Principal Component Analysis and Linear Discriminant Analysis. Two classifiers, Support Vector Machine (SVM) and k-Nearest Neighbour (KNN), were then applied to compare their performance. The results showed that the PCLDA-SVM model achieved an impressive accuracy rate of 97.99%, with a precision of 0.92, sensitivity of 92.83%, specificity of 97.65%, and F1 score of 0.93. Additionally, it demonstrated a low error rate of 0.016 and a Matthews Correlation Coefficient (MCC) and Kappa coefficient of 0.946. On the other hand, the PCLDA-KNN model also performed well, achieving an accuracy of 97.8%, precision of 0.93, sensitivity of 93.39%, specificity of 97.86%, an F1 score of 0.92, a high MCC and Kappa coefficient of 0.98, and an error rate of 0.006. In conclusion, the PCLDA-SVM method exhibited improved efficacy in diagnosing prostate cancer compared to the PCLDA-KNN model. Both models, however, showed promising results, suggesting the potential of these classifiers in prostate cancer diagnosis.
(© 2023. Springer Nature Limited.)

المؤلفون: Magnani G; Department of Engineering and Architecture, University of Parma, 43124 Parma, Italy., Giliberti C; Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, 43124 Parma, Italy., Errico D; Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, 43124 Parma, Italy., Stighezza M; Department of Engineering and Architecture, University of Parma, 43124 Parma, Italy., Fortunati S; Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, 43124 Parma, Italy., Mattarozzi M; Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, 43124 Parma, Italy., Boni A; Department of Engineering and Architecture, University of Parma, 43124 Parma, Italy., Bianchi V; Department of Engineering and Architecture, University of Parma, 43124 Parma, Italy., Giannetto M; Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, 43124 Parma, Italy., De Munari I; Department of Engineering and Architecture, University of Parma, 43124 Parma, Italy., Cagnoni S; Department of Engineering and Architecture, University of Parma, 43124 Parma, Italy., Careri M; Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, 43124 Parma, Italy.

المصدر: Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Jun 02; Vol. 24 (11). Date of Electronic Publication: 2024 Jun 02.

نوع المنشور: Journal Article

بيانات الدورية: Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE

مواضيع طبية MeSH: Solanum lycopersicum*/classification , Solanum lycopersicum*/chemistry , Machine Learning* , Gold*/chemistry, Discriminant Analysis ; Electronic Nose ; Metal Nanoparticles/chemistry ; Electrodes ; Polymers/chemistry ; Copper/chemistry ; Bridged Bicyclo Compounds, Heterocyclic/chemistry

مستخلص: The potential of a voltametric E-tongue coupled with a custom data pre-processing stage to improve the performance of machine learning techniques for rapid discrimination of tomato purées between cultivars of different economic value has been investigated. To this aim, a sensor array with screen-printed carbon electrodes modified with gold nanoparticles (GNP), copper nanoparticles (CNP) and bulk gold subsequently modified with poly(3,4-ethylenedioxythiophene) (PEDOT), was developed to acquire data to be transformed by a custom pre-processing pipeline and then processed by a set of commonly used classifiers. The GNP and CNP-modified electrodes, selected based on their sensitivity to soluble monosaccharides, demonstrated good ability in discriminating samples of different cultivars. Among the different data analysis methods tested, Linear Discriminant Analysis (LDA) proved to be particularly suitable, obtaining an average F1 score of 99.26%. The pre-processing stage was beneficial in reducing the number of input features, decreasing the computational cost, i.e., the number of computing operations to be performed, of the entire method and aiding future cost-efficient hardware implementation. These findings proved that coupling the multi-sensing platform featuring properly modified sensors with the custom pre-processing method developed and LDA provided an optimal tradeoff between analytical problem solving and reliable chemical information, as well as accuracy and computational complexity. These results can be preliminary to the design of hardware solutions that could be embedded into low-cost portable devices.

المؤلفون: Zhang J; College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, China., Yu X; Shandong Medicine Technician College, Tai'an, China., Yang R; College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, China., Zheng B; College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, China., Zhang Y; College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, China., Zhang F; College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, China.

المصدر: Phytochemical analysis : PCA [Phytochem Anal] 2024 Jun; Vol. 35 (4), pp. 647-663. Date of Electronic Publication: 2024 Jan 07.

نوع المنشور: Journal Article

بيانات الدورية: Publisher: Wiley Country of Publication: England NLM ID: 9200492 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1099-1565 (Electronic) Linking ISSN: 09580344 NLM ISO Abbreviation: Phytochem Anal Subsets: MEDLINE

مواضيع طبية MeSH: Lonicera*/chemistry , Principal Component Analysis*, Chromatography, High Pressure Liquid/methods ; Drugs, Chinese Herbal/chemistry ; Drugs, Chinese Herbal/analysis ; Cluster Analysis ; Quality Control ; Least-Squares Analysis ; Flowers/chemistry ; Discriminant Analysis ; Plant Extracts

مستخلص: Introduction: Lonicerae Japonicae Flos (LJF) is widely used in food and traditional Chinese medicine. To meet demand, Lonicera japonica Thunb. is widely cultivated in many provinces of China. However, reported studies on the quality evaluation of LJF only used a single or a few active components as indicators, which could not fully reflect the quality of LJF.
Objectives: In the present study, we aimed to develop a methodology for comprehensively evaluating the quality of LJF from different origins based on high-performance liquid chromatography (HPLC) fingerprinting and multicomponent quantitative analysis combined with chemical pattern recognition.
Materials and Methods: The HPLC method was developed for fingerprint analysis and was used to determine the contents of 19 components of LJF. To distinguish between samples and identify differential components, similarity analysis, hierarchical cluster analysis, principal component analysis, and orthogonal partial least squares discriminant analysis were performed.
Results: The HPLC fingerprint was established. Using the developed method, the contents of 19 components recognized in the fingerprint analysis were determined. Samples from different origins could be effectively distinguished.
Conclusions: HPLC fingerprinting and multicomponent quantitative analysis combined with chemical pattern recognition is an efficient method for evaluating LJF.
(© 2024 The Authors. Phytochemical Analysis published by John Wiley & Sons Ltd.)

المؤلفون: Zimmerleiter R; Research Center for Non-Destructive Testing GmbH, Altenberger Straße 69, 4040 Linz, Austria., Greibl W; Criminal Intelligence Service, Forensic Science, Josef Holaubek Platz, 1090 Wien, Austria., Meininger G; Spath Micro Electronic Design GmbH, Reininghausstraße 13, 8020 Graz, Austria., Duswald K; Research Center for Non-Destructive Testing GmbH, Altenberger Straße 69, 4040 Linz, Austria., Hannesschläger G; Research Center for Non-Destructive Testing GmbH, Altenberger Straße 69, 4040 Linz, Austria., Gattinger P; Research Center for Non-Destructive Testing GmbH, Altenberger Straße 69, 4040 Linz, Austria., Rohm M; IFHA/Christian Fuczik-Chemisches Labor GmbH, Gerhardusgasse 25/3.OG, 1200 Wien, Austria., Fuczik C; IFHA/Christian Fuczik-Chemisches Labor GmbH, Gerhardusgasse 25/3.OG, 1200 Wien, Austria., Holzer R; Research Center for Non-Destructive Testing GmbH, Altenberger Straße 69, 4040 Linz, Austria., Brandstetter M; Research Center for Non-Destructive Testing GmbH, Altenberger Straße 69, 4040 Linz, Austria.

المصدر: Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 May 17; Vol. 24 (10). Date of Electronic Publication: 2024 May 17.

نوع المنشور: Journal Article

بيانات الدورية: Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE

مواضيع طبية MeSH: Cannabis*/chemistry , Cannabis*/classification , Spectroscopy, Near-Infrared*/methods, Discriminant Analysis ; Least-Squares Analysis ; Humans ; Dronabinol/analysis

مستخلص: A rugged handheld sensor for rapid in-field classification of cannabis samples based on their THC content using ultra-compact near-infrared spectrometer technology is presented. The device is designed for use by the Austrian authorities to discriminate between legal and illegal cannabis samples directly at the place of intervention. Hence, the sensor allows direct measurement through commonly encountered transparent plastic packaging made from polypropylene or polyethylene without any sample preparation. The measurement time is below 20 s. Measured spectral data are evaluated using partial least squares discriminant analysis directly on the device's hardware, eliminating the need for internet connectivity for cloud computing. The classification result is visually indicated directly on the sensor via a colored LED. Validation of the sensor is performed on an independent data set acquired by non-expert users after a short introduction. Despite the challenging setting, the achieved classification accuracy is higher than 80%. Therefore, the handheld sensor has the potential to reduce the number of unnecessarily confiscated legal cannabis samples, which would lead to significant monetary savings for the authorities.

المؤلفون: Lee T; Health Hazards Prevention Branch, Pittsburgh Mining Research Division, National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Pittsburgh, PA 15236, USA. Electronic address: fwc8@cdc.gov., Mischler SE; Health Hazards Prevention Branch, Pittsburgh Mining Research Division, National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Pittsburgh, PA 15236, USA., Wolfe C; Health Hazards Prevention Branch, Pittsburgh Mining Research Division, National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Pittsburgh, PA 15236, USA.

المصدر: Journal of hazardous materials [J Hazard Mater] 2024 May 05; Vol. 469, pp. 133874. Date of Electronic Publication: 2024 Feb 24.

نوع المنشور: Journal Article

بيانات الدورية: Publisher: Elsevier Country of Publication: Netherlands NLM ID: 9422688 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-3336 (Electronic) Linking ISSN: 03043894 NLM ISO Abbreviation: J Hazard Mater Subsets: MEDLINE

مواضيع طبية MeSH: Asbestos*, Spectroscopy, Fourier Transform Infrared/methods ; Fourier Analysis ; Multivariate Analysis ; Discriminant Analysis ; Asbestos, Serpentine ; Least-Squares Analysis

مستخلص: This study presents a possible application of Fourier transform infrared (FTIR) spectrometry and multivariate data analysis, principal component analysis (PCA), and partial least squares-discriminant analysis (PLS-DA) for classifying asbestos and their nonasbestiform analogues. The objectives of the study are: 1) to classify six regulated asbestos types and 2) to classify between asbestos types and their nonasbestiform analogues. The respirable fraction of six regulated asbestos types and their nonasbestiform analogues were prepared in potassium bromide pellets and collected on polyvinyl chloride membrane filters for FTIR measurement. Both PCA and PLS-DA classified asbestos types and their nonasbestiform analogues on the score plots showed a very distinct clustering of samples between the serpentine (chrysotile) and amphibole groups. The PLS-DA model provided ∼95% correct prediction with a single asbestos type in the sample, although it did not provide all correct predictions for all the challenge samples due to their inherent complexity and the limited sample number. Further studies are necessary for a better prediction level in real samples and standardization of sampling and analysis procedures.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Published by Elsevier B.V.)

المؤلفون: Li L; Department of Ultrasound, The First Affiliated Hospital, Nanjing Medical University, Nanjing, PR China., Deng H; Department of Ultrasound, The First Affiliated Hospital, Nanjing Medical University, Nanjing, PR China., Chen W; Department of Ultrasound, The First Affiliated Hospital, Nanjing Medical University, Nanjing, PR China., Wu L; Department of Ultrasound, The First Affiliated Hospital, Nanjing Medical University, Nanjing, PR China., Li Y; Institute of Food Safety and Nutrition, Jiangsu Academy of Agricultural Sciences, Nanjing, PR China., Wang J; Department of Radiology, The First Affiliated Hospital, Nanjing Medical University, Nanjing, PR China., Ye X; Department of Ultrasound, The First Affiliated Hospital, Nanjing Medical University, Nanjing, PR China.

المصدر: Acta radiologica (Stockholm, Sweden : 1987) [Acta Radiol] 2024 May; Vol. 65 (5), pp. 441-448. Date of Electronic Publication: 2024 Jan 17.

نوع المنشور: Journal Article; Comparative Study

بيانات الدورية: Publisher: Sage Country of Publication: England NLM ID: 8706123 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1600-0455 (Electronic) Linking ISSN: 02841851 NLM ISO Abbreviation: Acta Radiol Subsets: MEDLINE

مواضيع طبية MeSH: Thyroid Nodule*/diagnostic imaging , Ultrasonography*/methods, Humans ; Middle Aged ; Male ; Female ; Diagnosis, Differential ; Adult ; Aged ; Support Vector Machine ; Reproducibility of Results ; Models, Theoretical ; Sensitivity and Specificity ; Thyroid Gland/diagnostic imaging ; Young Adult ; Adolescent ; Least-Squares Analysis ; Retrospective Studies ; Discriminant Analysis ; Logistic Models

مستخلص: Background: The overlapping nature of thyroid lesions visualized on ultrasound (US) images could result in misdiagnosis and missed diagnoses in clinical practice.
Purpose: To compare the diagnostic effectiveness of US coupled with three mathematical models, namely logistic regression (Logistics), partial least-squares discriminant analysis (PLS-DA), and support vector machine (SVM), in discriminating between malignant and benign thyroid nodules.
Material and Methods: A total of 588 thyroid nodules (287 benign and 301 malignant) were collected, among which 80% were utilized for constructing the mathematical models and the remaining 20% were used for internal validation. In addition, an external validation cohort comprising 160 nodules (80 benign and 80 malignant) was employed to validate the accuracy of these mathematical models.
Results: Our study demonstrated that all three models exhibited effective predictive capabilities for distinguishing between benign and malignant nodules, whose diagnostic effectiveness surpassed that of the TI-RADS classification, particularly in terms of true negative diagnoses. SVM achieved a higher diagnostic rate for malignant thyroid nodules (93.8%) compared to Logistics (91.5%) and PLS-DA (91.6%). PLS-DA exhibited higher diagnostic rates for benign thyroid nodules (91.9%) compared to Logistics (86.7%) and SVM (88.7%). Both the area under the receiver operating characteristic curve (AUC) values of PLS-DA (0.917) and SVM (0.913) were higher than that of Logistics (0.891).
Conclusion: Our findings indicate that SVM had significantly higher rates of true positive diagnoses and PLS-DA exhibited significantly higher rates of true negative diagnoses. All three models outperformed the TI-RADS classification in discriminating between malignant and benign thyroid nodules.
Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

المؤلفون: Nunes PP; Department of Veterinary Clinic and Surgery, Veterinary School, Federal University of Minas Gerais, Belo Horizonte, MG 31270-901, Brazil., Almeida MR; Department of Chemistry, Institute of Exact Science, Federal University of Minas Gerais, Belo Horizonte, MG 31270-901, Brazil., Pacheco FG; Laboratory of Carbon Nanostructure Chemistry, Nuclear Technology Development Center, Belo Horizonte, MG 31270-901, Brazil., Fantini C; Department of Physics, Institute of Exact Science, Federal University of Minas Gerais, Belo Horizonte, MG 31270-901, Brazil., Furtado CA; Laboratory of Carbon Nanostructure Chemistry, Nuclear Technology Development Center, Belo Horizonte, MG 31270-901, Brazil., Ladeira LO; Department of Physics, Institute of Exact Science, Federal University of Minas Gerais, Belo Horizonte, MG 31270-901, Brazil., Jorio A; Department of Physics, Institute of Exact Science, Federal University of Minas Gerais, Belo Horizonte, MG 31270-901, Brazil., Júnior APM; Department of Veterinary Clinic and Surgery, Veterinary School, Federal University of Minas Gerais, Belo Horizonte, MG 31270-901, Brazil., Santos RL; Department of Veterinary Clinic and Surgery, Veterinary School, Federal University of Minas Gerais, Belo Horizonte, MG 31270-901, Brazil., Borges ÁM; Department of Veterinary Clinic and Surgery, Veterinary School, Federal University of Minas Gerais, Belo Horizonte, MG 31270-901, Brazil. Electronic address: alanmborges@hotmail.com.

المصدر: Journal of dairy science [J Dairy Sci] 2024 May; Vol. 107 (5), pp. 2681-2689. Date of Electronic Publication: 2023 Nov 02.

نوع المنشور: Journal Article

بيانات الدورية: Publisher: American Dairy Science Association Country of Publication: United States NLM ID: 2985126R Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1525-3198 (Electronic) Linking ISSN: 00220302 NLM ISO Abbreviation: J Dairy Sci Subsets: MEDLINE

مواضيع طبية MeSH: Milk*/chemistry , Nanotubes, Carbon*/analysis, Female ; Cattle ; Animals ; Fourier Analysis ; Spectrum Analysis, Raman/methods ; Discriminant Analysis ; Least-Squares Analysis

مستخلص: The potential use of carbon-based methodologies for drug delivery and reproductive biology in cows raises concerns about residues in milk and food safety. This study aimed to assess the potential of Fourier transform Raman spectroscopy and discriminant analysis using partial least squares (PLS-DA) to detect functionalized multiwalled carbon nanotubes (MWCNT) in bovine raw milk. Oxidized MWCNT were diluted in milk at different concentrations from 25.00 to 0.01 µg/mL. Raman spectroscopy measurements and PLS-DA were performed to identify low concentrations of MWCNT in milk samples. The PLS-DA model was characterized by the analysis of the variable importance in projection (VIP) scores. All the training samples were correctly classified by the model, resulting in no false-positive or false-negative classifications. For test samples, only one false-negative result was observed, for 0.01 µg/mL MWCNT dilution. The association between Raman spectroscopy and PLS-DA was able to identify MWCNT diluted in milk samples up to 0.1 µg/mL. The PLS-DA model was built and validated using a set of test samples and spectrally interpreted based on the highest VIP scores. This allowed the identification of the vibrational modes associated with the D and G bands of MWCNT, as well as the milk bands, which were the most important variables in this analysis.
(The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

المؤلفون: Cilla S; Medical Physics Unit, Responsible Research Hospital, Campobasso, Italy. Electronic address: savino.cilla@gemellimolise.it., Deodato F; Radiation Oncology Unit, Responsible Research Hospital, Campobasso, Italy., Romano C; Medical Physics Unit, Responsible Research Hospital, Campobasso, Italy., Macchia G; Radiation Oncology Unit, Responsible Research Hospital, Campobasso, Italy., Buwenge M; Radiation Oncology Department, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy., Morganti AG; Radiation Oncology Department, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy; DIMEC, Alma Mater Studiorum, Bologna University, Bologna, Italy.

المصدر: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) [Phys Med] 2024 May; Vol. 121, pp. 103340. Date of Electronic Publication: 2024 Apr 09.

نوع المنشور: Journal Article

بيانات الدورية: Publisher: Istituti Editoriali e Poligrafici Internazionali Country of Publication: Italy NLM ID: 9302888 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1724-191X (Electronic) Linking ISSN: 11201797 NLM ISO Abbreviation: Phys Med Subsets: MEDLINE

مواضيع طبية MeSH: Lung Neoplasms*/radiotherapy , Lung Neoplasms*/diagnostic imaging , Radiosurgery*/methods , Principal Component Analysis*, Humans ; Discriminant Analysis ; Treatment Outcome ; Male ; Female ; Tomography, X-Ray Computed ; Aged ; Middle Aged ; Image Processing, Computer-Assisted/methods ; Aged, 80 and over ; Radiomics

مستخلص: Purpose: Discriminant analysis of principal components (DAPC) was introduced to describe the clusters of genetically related individuals focusing on the variation between the groups of individuals. Borrowing this approach, we evaluated the potential of DAPC for the evaluation of clusters in terms of treatment response to SBRT of lung lesions using radiomics analysis on pre-treatment CT images.
Materials and Methods: 80 pulmonary metastases from 56 patients treated with SBRT were analyzed. Treatment response was stratified as complete, incomplete and null responses. For each lesion, 107 radiomics features were extracted using the PyRadiomics software. The concordance correlation coefficients (CCC) between the radiomics features obtained by two segmentations were calculated. DAPC analysis was performed to infer the structure of "radiomically" related lesions for treatment response assessment. The DAPC was performed using the "adegenet" package for the R software.
Results: The overall mean CCC was 0.97 ± 0.14. The analysis yields 14 dimensions in order to explain 95 % of the variance. DAPC was able to group the 80 lesions into the 3 different clusters based on treatment response depending on the radiomics features characteristics. The first Linear Discriminant achieved the best discrimination of individuals into the three pre-defined groups. The greater radiomics loadings who contributed the most to the treatment response differentiation were associated with the "sphericity", "correlation" and "maximal correlation coefficient" features.
Conclusion: This study demonstrates that a DAPC analysis based on radiomics features obtained from pretreatment CT is able to provide a reliable stratification of complete, incomplete or null response of lung metastases following SBRT.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

المؤلفون: Ma EM; Department of Respiratory and Critical Care Medicine, Shanshan Country People's Hospital, Shanshan 838200, China., Lu K; Department of Respiratory and Critical Care Medicine, Shanshan Country People's Hospital, Shanshan 838200, China., Wei YB; Department of Respiratory and Critical Care Medicine, Shanshan Country People's Hospital, Shanshan 838200, China.

المصدر: Zhonghua lao dong wei sheng zhi ye bing za zhi = Zhonghua laodong weisheng zhiyebing zazhi = Chinese journal of industrial hygiene and occupational diseases [Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi] 2024 Apr 20; Vol. 42 (4), pp. 282-285.

نوع المنشور: English Abstract; Journal Article

بيانات الدورية: Publisher: Tianjin shi lao dong wei sheng yan jiu suo ; Tianjin shi lao dong wei sheng huan jing yi xue hui Country of Publication: China NLM ID: 8410840 Publication Model: Print Cited Medium: Print ISSN: 1001-9391 (Print) Linking ISSN: 10019391 NLM ISO Abbreviation: Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi Subsets: MEDLINE

مواضيع طبية MeSH: Heat Stroke*/mortality, Humans ; Discriminant Analysis ; Male ; Female ; ROC Curve ; Middle Aged ; Intensive Care Units ; Risk Assessment/methods ; Risk Factors ; Prognosis

مستخلص: Objective: To establish an early warning model to assess the mortality risk of patients with heat stroke disease. Methods: The case data of patients diagnosed with heat stroke disease admitted to the comprehensive ICU of Shanshan County from January 2016 to December 2020 were selected. According to the short-term outcome (28 days) of patients, they were divided into death group (20 cases) and survival group (53 cases) . The relevant indicators with statistically significant differences between groups within 24 hours after admission were selected. By drawing the subject work curve (ROC) and calculating the area under the curve, the relevant indicators with the area under the curve greater than 0.7 were selected, Fisher discriminant analysis was used to establish an assessment model for the death risk of heat stroke disease. The data of heat stroke patients from January 1, 2021 to December 2022 in the comprehensive ICU of Shanshan County were collected for external verification. Results There were significant differences in age, cystatin C, procalcitonin, platelet count, CKMB, CK, CREA, PT, TT, APTT, heart rate, respiratory rate and GLS score among the groups. Cystatin C, CKMB, CREA, PT, TT, heart rate AUC area at admission was greater than 0.7. Fisher analysis method is used to build a functional model. Results: The diagnostic sensitivity, specificity and AUC area of the functional model were 95%, 83% and 0.937 respectively. The external validation results showed that the accuracy of predicting survival group was 85.71%, the accuracy of predicting death group was 88.89%. Conclusion: The early warning model of heat stroke death constructed by ROC curve analysis and Fisher discriminant analysis can provide objective reference for early intervention of heat stroke.

المؤلفون: Farooq S; Center for Lasers and Applications, Instituto de Pesquisas Energeticas e Nucleares, IPEN-CNEN, Address One, Sao Paulo, 05508-000, Sao Paulo, Brazil., Del-Valle M; Center for Lasers and Applications, Instituto de Pesquisas Energeticas e Nucleares, IPEN-CNEN, Address One, Sao Paulo, 05508-000, Sao Paulo, Brazil., Dos Santos SN; Center for Radiopharmaceutics, Instituto de Pesquisas Energeticas e Nucleares, IPEN-CNEN, Address One, Sao Paulo, 05508-000, Sao Paulo, Brazil., Bernardes ES; Center for Radiopharmaceutics, Instituto de Pesquisas Energeticas e Nucleares, IPEN-CNEN, Address One, Sao Paulo, 05508-000, Sao Paulo, Brazil., Zezell DM; Center for Lasers and Applications, Instituto de Pesquisas Energeticas e Nucleares, IPEN-CNEN, Address One, Sao Paulo, 05508-000, Sao Paulo, Brazil. Electronic address: zezell@usp.br.

المصدر: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy [Spectrochim Acta A Mol Biomol Spectrosc] 2024 Apr 05; Vol. 310, pp. 123941. Date of Electronic Publication: 2024 Jan 24.

نوع المنشور: Journal Article

بيانات الدورية: Publisher: Elsevier Country of Publication: England NLM ID: 9602533 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-3557 (Electronic) Linking ISSN: 13861425 NLM ISO Abbreviation: Spectrochim Acta A Mol Biomol Spectrosc Subsets: MEDLINE

مواضيع طبية MeSH: Breast Neoplasms*/diagnosis, Humans ; Female ; Spectroscopy, Fourier Transform Infrared/methods ; Discriminant Analysis ; Principal Component Analysis ; Machine Learning ; Tumor Microenvironment

مستخلص: Fourier-transform infrared spectroscopy (FTIR) is a powerful, non-destructive, highly sensitive and a promising analytical technique to provide spectrochemical signatures of biological samples, where markers like carbohydrates, proteins, and phosphate groups of DNA can be recognized in biological micro-environment. However, method of measurements of large cells need an excessive time to achieve high quality images, making its clinical use difficult due to speed of data-acquisition and lack of optimized computational procedures. To address such challenges, Machine Learning (ML) based technologies can assist to assess an accurate prognostication of breast cancer (BC) subtypes with high performance. Here, we applied FTIR spectroscopy to identify breast cancer subtypes in order to differentiate between luminal (BT474) and non-luminal (SKBR3) molecular subtypes. For this reason, we tested multivariate classification technique to extract feature information employing three-dimension (3D)-discriminant analysis approach based on 3D-principle component analysis-linear discriminant analysis (3D-PCA-LDA) and 3D-principal component analysis-quadratic discriminant analysis (3D-PCA-QDA), showing an improvement in sensitivity (98%), specificity (94%) and accuracy (98%) parameters compared to conventional unfolded methods. Our results evidence that 3D-PCA-LDA and 3D-PCA-QDA are potential tools for discriminant analysis of hyperspectral dataset to obtain superior classification assessment.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier B.V. All rights reserved.)