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OBJECTIVES: TO IDENTIFY FIRST TRIMESTER MATERNAL GYNECO-OBSTETRIC VARIABLES THAT MAY BE USEFUL FOR GESTATIONAL DIABETES MELLITUS (GDM) PREDICTION IN CHILEAN PREGNANT WOMEN. METHODS: PREGNANT WOMEN WITH ? 12 GESTATIONAL WEEKS AND WITHOUT PREGESTATIONAL DIABETES WERE RECRUITED IN CONCEPCION, CHILE. DURING THE FIRST TRIMESTER OF PREGNANCY, 19 GYNECO-OBSTETRIC VARIABLES WERE REGISTERED: MATERNAL AGE, HEIGHT, WEIGHT, BODY MASS INDEX, SYSTOLIC PRESSURE, DIASTOLIC PRESSURE, PRIOR GDM, POLYCYSTIC OVARIAN SYNDROME, DIO2 GENOTYPE, FTO GENOTYPE, GLYCEMIA, TSH, TOTAL T3, TOTAL T4, FREE T4, THYROGLOBULIN, THYROGLOBULIN ANTIBODY (TGAB), THYROID PEROXIDASE ANTIBODY (ANTI-TPO) AND TSH RECEPTOR ANTIBODY (TRAB). GDM DIAGNOSIS WAS PERFORMED AT 24-28 GESTATIONAL WEEKS, WITH POSTLOAD GLYCEMIA (75G, 2H) ? 140 MG/DL. OUT OF 39 PREGNANT WOMEN, 6 WERE DIAGNOSED WITH GDM. DATA WERE ANALYZED BY UNIVARIATE AND MULTIVARIATE APPROACHES. FOR UNIVARIATE ANALYSIS, FISHERS EXACT, STUDENTS T AND MANN-WHITNEY TESTS WERE EMPLOYED; WHILE FOR MULTIVARIATE ANALYSIS, THE MACHINE LEARNING TECHNIQUE PRINCIPAL COMPONENT ANALYSIS (PCA) WAS APPLIED. RESULTS: ACCORDING TO THE UNIVARIATE ANALYSIS, THE ONLY FIRST TRIMESTER VARIABLES THAT ENABLE TO DIFFERENTIATE THE GDM GROUP FROM THE NON-GDM ONE ARE SYSTOLIC PRESSURE (P=0.0454) AND PRIOR GDM (P=0.0002). IN CONTRAST, THE MULTIVARIATE ANALYSIS SHOWS THAT GDM PREGNANT WOMEN CAN BE DISTINGUISHED FROM THE NON-GDM ONES BY THE SIXTH PRINCIPAL COMPONENT (PC6, 7% VARIANCE), WHICH IS A LINEAL COMBINATION OF VARIABLES WHERE THE MAIN CONTRIBUTORS ARE: FTO GENOTYPE, PRIOR GDM, TOTAL T3, DIASTOLIC PRESSURE, ANTI-TPO AND TRAB. CONCLUSION: THE FIRST TRIMESTER MATERNAL VARIABLES SYSTOLIC PRESSURE, PRIOR GDM, FTO GENOTYPE, TOTAL T3, DIASTOLIC PRESSURE, ANTI-TPO AND TRAB, ARE RELEVANT TO DISCRIMINATE BETWEEN GDM AND NON-GDM PREGNANT WOMEN. THEREFORE, THEY MAY BE HELPFUL FOR GDM PREDICTION.

INTRODUCTION: MACHINE LEARNING (ML) CORRESPONDS TO A WIDE VARIETY OF METHODS THAT USE MATHEMATICS, STATISTICS AND COMPUTATIONAL SCIENCE TO LEARN FROM MULTIPLE VARIABLES SIMULTANEOUSLY. BY MEANS OF PATTERN RECOGNITION, ML METHODS ARE ABLE TO FIND HIDDEN CORRELATIONS AND ACCOMPLISH ACCURATE PREDICTIONS REGARDING DIFFERENT CONDITIONS. ML HAS BEEN SUCCESSFULLY USED TO SOLVE VARIED PROBLEMS IN DIFFERENT AREAS OF SCIENCE, SUCH AS PSYCHOLOGY, ECONOMICS, BIOLOGY AND CHEMISTRY. THEREFORE, WE WONDERED HOW FAR IT HAS PENETRATED INTO THE FIELD OF OBSTETRICS AND GYNECOLOGY. AIM: TO DESCRIBE THE STATE OF ART REGARDING THE USE OF ML IN THE CONTEXT OF PREGNANCY DISEASES AND COMPLICATIONS. METHODOLOGY: PUBLICATIONS WERE SEARCHED IN PUBMED, WEB OF SCIENCE AND GOOGLE SCHOLAR. SEVEN SUBJECTS OF INTEREST WERE CONSIDERED: GESTATIONAL DIABETES MELLITUS, PREECLAMPSIA, PERINATAL DEATH, SPONTANEOUS ABORTION, PRETERM BIRTH, CESAREAN SECTION, AND FETAL MALFORMATIONS. CURRENT STATE: ML HAS BEEN WIDELY APPLIED IN ALL THE INCLUDED SUBJECTS. ITS USES ARE VARIED, THE MOST COMMON BEING THE PREDICTION OF PERINATAL DISORDERS. OTHER ML APPLICATIONS INCLUDE (BUT ARE NOT RESTRICTED TO) BIOMARKER DISCOVERY, RISK ESTIMATION, CORRELATION ASSESSMENT, PHARMACOLOGICAL TREATMENT PREDICTION, DRUG SCREENING, DATA ACQUISITION AND DATA EXTRACTION. MOST OF THE REVIEWED ARTICLES WERE PUBLISHED IN THE LAST FIVE YEARS. THE MOST EMPLOYED ML METHODS IN THE FIELD ARE NON-LINEAR. EXCEPT FOR LOGISTIC REGRESSION, LINEAR METHODS ARE RARELY USED. FUTURE CHALLENGES: TO IMPROVE DATA RECORDING, STORAGE AND UPDATE IN MEDICAL AND RESEARCH SETTINGS FROM DIFFERENT REALITIES. TO DEVELOP MORE ACCURATE AND UNDERSTANDABLE ML MODELS USING DATA FROM CUTTING-EDGE INSTRUMENTS. TO CARRY OUT VALIDATION AND IMPACT ANALYSIS STUDIES OF CURRENTLY EXISTING HIGH-ACCURACY ML MODELS. CONCLUSION: THE USE OF ML IN PREGNANCY DISEASES AND COMPLICATIONS IS QUITE RECENT, AND HAS INCREASED OVER THE LAST FEW YEARS. THE APPLICATIONS ARE VARIED AND POINT N

GESTATIONAL DIABETES MELLITUS (GDM) IS A HYPERGLYCEMIA STATE THAT IMPAIRS MATERNAL AND OFFSPRING HEALTH, SHORT AND LONG-TERM. IT IS USUALLY DIAGNOSED AT 24?28 WEEKS OF PREGNANCY (WP), BUT AT THAT TIME THE FETAL PHENOTYPE IS ALREADY ALTERED. MACHINE LEARNING (ML)-BASED MODELS HAVE EMERGED AS AN AUSPICIOUS ALTERNATIVE TO PREDICT THIS PATHOLOGY EARLIER, HOWEVER, THEY MUST BE VALIDATED IN DIFFERENT POPULATIONS BEFORE THEIR IMPLEMENTATION IN ROUTINE CLINICAL PRACTICE. THIS REVIEW AIMS TO GIVE AN OVERVIEW OF THE ML-BASED MODELS THAT HAVE BEEN PROPOSED TO PREDICT GDM BEFORE 24?28 WP, WITH SPECIAL EMPHASIS ON THEIR CURRENT VALIDATION STATE AND PREDICTIVE PER- FORMANCE. ARTICLES WERE SEARCHED IN PUBMED. MANUSCRIPTS WRITTEN IN ENGLISH AND PUBLISHED BEFORE JANUARY 1, 2022, WERE CONSIDERED. 109 ORIGINAL RESEARCH STUDIES WERE SELECTED, AND CATEGORIZED ACCORDING TO THE TYPE OF VARIABLES THAT THEIR MODELS INVOLVED: MEDICAL, I.E. CLINICAL AND/OR BIOCHEMICAL PARAMETERS; ALTERNATIVE, I.E. METABOLITES, PEPTIDES OR PROTEINS, MICRO-RIBONUCLEIC ACID MOLECULES, MICROBIOTA GENERA, OR OTHER VARIABLES THAT DID NOT FIT INTO THE FIRST CATEGORY; OR MIXED, I.E. BOTH MEDICAL AND ALTERNATIVE DATA. ONLY 8.3 % OF THE REVIEWED MODELS HAVE HAD VALIDATION IN INDEPENDENT STUDIES, WITH LOW OR MODERATE PERFORMANCE FOR GDM PREDICTION. IN CONTRAST, SEVERAL MODELS THAT LACK OF INDEPENDENT VALIDATION HAVE SHOWN A VERY HIGH PREDICTIVE POWER. THE EVALUATION OF THESE PROMISING MODELS IN FUTURE INDEPENDENT VALIDATION STUDIES WOULD ALLOW TO ASSESS THEIR PERFORMANCE ON DIFFERENT POPULATIONS, AND CONTINUE THEIR WAY TOWARDS CLINICAL IMPLEMENTATION. ONCE SETTLED, ML-BASED MODELS WOULD HELP TO PREDICT GDM EARLIER, INITIATE ITS TREATMENT TIMELY AND PREVENT ITS NEGATIVE CONSEQUENCES ON MATERNAL AND OFFSPRING HEALTH.

OBJECTIVE: TO COMPARE THE PERFORMANCE OF NEAR-INFRARED (NIR) SPECTRA AND MATERNAL DATA BASED-MACHINE LEARNING (ML) MODELS FOR GESTATIONAL DIABETES MELLITUS (GDM) PREDICTION IN CHILEAN PREGNANT WOMEN. METHODOLOGY: PREGNANT WOMEN WITH ? 12 GESTATIONAL WEEKS AND WITHOUT PREGESTATIONAL DIABETES WERE RECRUITED IN CONCEPCION, CHILE. GDM DIAGNOSIS WAS PERFORMED AT 24-28 GESTATIONAL WEEKS, WITH FASTING GLYCEMIA 100-125 MG/DL OR POST-LOAD GLYCEMIA (75 G, 2 H) ? 140 MG/DL. DURING THE FIRST TRIMESTER OF PREGNANCY, SERA WERE COLLECTED, AND 63 CLINICAL AND BIOCHEMICAL MATERNAL VARIABLES WERE REGISTERED. FOR EACH SERUM SAMPLE, 5 NIR SPECTRA (RANGE 4000-10500 CM-1, RESOLUTION 4 CM-1) WERE RECORDED AND AVERAGED. FOR NIR SPECTRA, 80 DIFFERENT COMBINATIONS OF TRANSFORMATIONS (SAVITZKY-GOLAY SMOOTHING OR FIRST/SECOND DERIVATIVE WITH VARYING FILTER WIDTH, STANDARD NORMAL VARIATE SCATTERING CORRECTION, AUTOMATIC WEIGHTED LEAST SQUARES BASELINE CORRECTION, 2-NORM NORMALIZATION) WERE TESTED. NIR AND MATERNAL DATA WERE PREPROCESSED BY MEAN CENTERING AND AUTOSCALING, RESPECTIVELY. FOR GDM PREDICTION, THE CLASSIFICATION ML TECHNIQUE PARTIAL LEAST SQUARES-DISCRIMINANT ANALYSIS (PLS-DA) WAS EMPLOYED. EVERY MODEL WAS SUBJECTED TO LEAVE-ONE-OUT CROSS-VALIDATION. RESULTS: THE BEST NIR DATA-BASED MODEL WAS OBTAINED WITH SAVITZKY-GOLAY SMOOTHING (FILTER WIDTH 15, POLYNOMIAL ORDER 2) AND 2-NORM NORMALIZATION. IT ACHIEVED A CROSS-VALIDATION NON-ERROR RATE (CV-NER) OF 68% AND A CROSS-VALIDATION AREA UNDER THE RECEIVER OPERATING CHARACTERISTIC CURVE (CV-AUC) OF 0.669. THE MATERNAL DATA BASED-MODEL ACHIEVED A CV-NER OF 81% AND A CV-AUC OF 0.852. CONCLUSIONS: CLINICAL AND BIOCHEMICAL MATERNAL PARAMETERS ARE MORE USEFUL TO PREDICT GDM IN CHILEAN PREGNANT WOMEN THAN NIR SPECTRAL DATA.

OBJECTIVES: ANALYZE WHETHER UMBILICAL CORD PLASMA FROM PREECLAMPTIC PREGNANCIES (U-PE) REDUCES BRAIN ENDOTHELIAL CELL MIGRATION AS AN UNDERLYING MECHANISM OF IMPAIRED BRAIN ANGIOGENESIS. METHODS: HUMAN CEREBRAL MICROVASCULAR ENDOTHELIAL CELLS (HCMEC/D3) WERE INCUBATED (12 H, 1% V/V) WITH UMBILICAL CORD PLASMAS FROM NORMOTENSIVE (N=20, U-NP) OR WOMEN WITH PREECLAMPSIA (N=20, U-PE). CELL VIABILITY (MTT ASSAY), CELL PROLIFERATION (DETECTION OF BROMOURIDINE), AND CELL MIGRATION (WOUND HEALING IN VITRO ASSAY) WERE MEASURED. SYNTHESIS AND RELEASE OF VASCULAR ENDOTHELIAL GROWTH FACTOR (VEGF) AND THE PRESENCE OF ITS RECEPTOR TYPE 2 IN THE INACTIVE (VEGFR2) OR ACTIVE PRO-MIGRATORY PHOSPHORYLATION (Y951-VEGFR2) WERE ANALYZED BY WESTERN BLOT. THE SOLUBLE VEGF RECEPTOR TYPE 1 (OR SFLT-1) WAS MEASURED USING ELISA. ALSO, F-ACTIN POLYMERIZATION/DEPOLYMERIZATION PROTEINS, INCLUDING COFILIN, PHOSPHO-COFILIN, AND ARP2/ARP3 COMPLEX, WERE ANALYZED BY WESTERN BLOT. THE LENGTH AND WIDTH OF F-ACTIN FIBERS (F-ACTIN) WERE EVALUATED USING PHALLOIDIN FLUORESCENCE RESULTS: COMPARED TO PLASMA FROM U-NP, U-PE SIGNIFICANTLY REDUCED CELL PROLIFERATION (P=0.04) AND CELL MIGRATION (P=0.02) WITHOUT AFFECTING CELL VIABILITY. A REDUCTION IN THE VEGFR2 PROTEIN AMOUNT IN BOTH THE INACTIVE AND Y951 PHOSPHORYLATED ISOFORM (PARTICULARLY IN THE CYTOPLASM) WAS FOUND IN CELLS EXPOSED TO U-PE. THIS EFFECT OF U-PE WAS ASSOCIATED WITH REDUCED COFILIN EXPRESSION AND IN THE WIDTH OF F-ACTIN FIBERS, PARTICULARLY IN THE MIDDLE AND TIP SECTIONS OF THE CELLS. IN CONTRAST, NO SIGNIFICANT CHANGES WERE OBSERVED IN THE SYNTHESIS/RELEASE OF VEGF, NEITHER IN THE PLASMA LEVELS NOR THE RELEASE OF SFLT-1. ALSO, NO CHANGES IN PHOSPHO-COFILIN AND ARP2/ARP3 COMPLEX WERE FOUND IN CELLS EXPOSED TO U-PE. CONCLUSION: U-PE LEADS TO REDUCED BRAIN ENDOTHELIAL CELL MIGRATION ASSOCIATED WITH DOWNREGULATION OF THE PRO-MIGRATORY PHOSPHORYLATION OF VEGFR2 AND COFILIN LEVELS THAT MAY LEAD TO HARMED POLYMERIZATION OF F-ACTIN. THESE FINDINGS