Electrocardiogram Parameters Associated With the Diagnosis of Pulmonary Hypertension in High-Altitude Tibetan Populations: A Retrospective Single-Centre Study.

BACKGROUND: Electrocardiogram (ECG) is a commonly used diagnostic method for pulmonary hypertension (PH) in Tibetan areas, but its sensitivity and specificity are not good enough. This study aimed to investigate the ECG parameters associated with the diagnosis of PH in Tibetan areas. METHODS: Ninety-four PH patients of Tibetan ethnicity who were treated at the hospital between March 2019 and October 2020, and 52 Tibetan individuals as controls, were included. The ECG parameters were compared between groups. Multivariate logistic analysis was performed to identify the ECG parameters that can be used for the diagnosis of PH. The univariate significances of ECG parameters were included in the multivariate analyses, whereas those exhibiting opposite trends between different PH subtypes were excluded. RESULTS: Two ECG parameters were significant in multivariate analysis. The final model included S wave amplitude in lead V3 (OR 5.81; 95% CI 2.79-12.11; p<0.001) and a negative T wave in leads V1-V3 (OR 0.05; 95% CI 0.01-0.41; p=0.005). The ROC curve analysis on the final model yielded an AUC of 0.830 (95% CI 0.766-0.894; p<0.001), indicating good diagnostic performance. A nomogram for diagnosis of PH was also established using S wave amplitude in lead V3 and a negative T wave in leads V1-V3. CONCLUSION: The ECG parameters S wave amplitude in lead V3 and a negative T wave in leads V1-V3 were independent factors associated with the diagnosis of PH in high-altitude Tibetan populations.

Diagnostic factors for recurrent pregnancy loss: an expanded workup.

PURPOSE: There is limited information on the risk factors for recurrent pregnancy loss (RPL). METHODS: In this study, a patient-based approach was used to investigate the possible involvement and relative relevance of a large number of diagnostic factors in 843 women with RPL who underwent an extensive diagnostic workup including 44 diagnostic factors divided into 7 major categories. RESULTS: The rates of abnormalities found were: (1) genital infections: 11.74%; (2) uterine anatomic defects: 23.72%; (3) endocrine disorders: 29.42%; (4) thrombophilias: 62%; (5) autoimmune abnormalities: 39.2%; (6) parental karyotype abnormalities 2.25%; (7) clinical factors: 87.78%. Six hundred and fifty-nine out of eight hundred and forty-three women (78.17%) had more than one abnormality. The mean number of pregnancy losses increased by increasing the number of the abnormalities found (r = 0.86949, P < 0.02). The factors associated with the highest mean number of pregnancy losses were cervical isthmic incompetence, anti-beta-2-glycoprotein-1 antibodies, unicornuate uterus, anti-prothrombin A antibodies, protein C deficiency, and lupus anticoagulant. The majority of the considered abnormalities had similar, non-significant prevalence between women with 2 versus ≥ 3 pregnancy losses with the exception of age ≥ 35 years and MTHFR A1298C heterozygote mutation. No difference was found between women with primary and secondary RPL stratified according to the number of abnormalities detected (Chi-square: 8.55, P = 0.07). In these women, the only factors found to be present with statistically different rates were age ≥ 35 years, cigarette smoking, and genital infection by Ureaplasma. CONCLUSION: A patient-based diagnostic approach in women with RPL could be clinically useful and could represent a basis for future research.

Role for Biochemical Assays and Kayser-Fleischer Rings in Diagnosis of Wilson's Disease.

BACKGROUND & AIMS: Wilson disease is an autosomal recessive disorder that impairs copper homeostasis and is caused by homozygous or compound heterozygous mutations in ATP7B, which encodes a copper-transporting P-type ATPase. Patients have variable clinical manifestations and laboratory test results, resulting in diagnostic dilemmas. We aimed to identify factors associated with symptoms and features of Wilson disease from a large cohort, over 15 years. METHODS: We collected data from 715 patients (529 with symptoms, 146 without symptoms, and 40 uncategorized) and a genetic confirmation of Wilson's disease (mean age of diagnosis, 18.84 years), recruited from 3 hospitals in China from 2004 through 2019. We analyzed clinical data along with serum levels of ceruloplasmin (available from 636 patients), 24-hr urinary copper excretion (collected from 131 patients), Kayser-Fleisher rings (copper accumulation in eyes, with neurologic data from 355 patients), and magnetic resonance imaging (MRI) abnormalities. Differences among the groups were analyzed using 1-way analysis of variance followed by Tukey multiple comparison test. RESULTS: Of the 529 patients with symptoms, 121 had hepatic features, 355 had neurologic features, 28 had osteomuscular features (premature osteoarthritis, skeletal deformities, and pathological bone fractures), and 25 had psychiatric symptoms. Age of onset was significantly younger in patients with hepatic (16.94 ± 1.03 years; P = .0105) or osteomuscular features (13 ± 1.33 years; P = .0001) than patients with neurological features (19.48 ± 0.46 years). Serum levels of ceruloplasmin differed among asymptomatic patients and patients with osteomuscular or neurologic symptoms of Wilson disease. Serum levels of ceruloplasmin ranged from 18.93 mg/L to approximately 120.00 mg/L (quantiles of 0.025 to approximately 0.975). Fifty-one of 131 patients (39%) had urinary copper excretion levels below 100 µg/24 hr; there was significant variation in levels of urinary copper excretion between patients older than 14 years vs 14 years or younger. Of the 355 patients with neurologic features, 244 patients (69%) had abnormal findings from MRI and Kayser-Fleisher rings; only 1 patient with abnormal findings from brain MRI was negative for Kayser-Fleisher rings. CONCLUSIONS: Serum level of ceruloplasmin, 24-hour urinary copper excretion, and Kayser-Fleisher rings can be used to identify patients who might have Wilson disease. Patients with serum levels of ceruloplasmin below 120 mg/L and children with urinary copper excretion above 40 µg should undergo genetic testing for Wilson's disease. Patients with movement disorders and brain MRI abnormalities without Kayser-Fleisher rings are not likely to have Wilson disease.

A questionnaire-based crossover study on the association of microbiological testing with prescription of systemic antibiotics following initial periodontal therapy.

AIM: (1) To assess prescription of systemic antibiotics following initial periodontal therapy with and without the availability of microbiological information; (2) To identify factors associated with prescription of systemic antibiotics following initial periodontal therapy. MATERIALS AND METHODS: Twenty-four clinicians were invited to complete a questionnaire on 20 patient records with respect to periodontal treatment planning, once with microbiological information available and once without. Randomization determined when the microbiological information was provided, and a 3-month washout period was respected between scoring sessions. Regression analysis was performed to identify factors associated with prescription of systemic antibiotics. RESULTS: Twenty-one clinicians completed both scoring sessions. Clinicians prescribed systemic antibiotics in on average 56% (95% Confidence Interval (CI) [0.51; 0.61]) of the cases having microbiological information, and in 52% (95% CI [0.47; 0.57]) of the same cases not having that information (p = 0.094). The odds for prescribing systemic antibiotics were 3.34 (95% CI [2.06; 5.42]) times higher when the clinician had at least 3 years of experience, 2.55 (95% CI [1.40; 4.66]) times higher for patients diagnosed with periodontitis stage IV when compared to stage III, 1.08 (95% CI [1.04; 1.11]) times higher for younger patients, 2.78 times (95% CI [1.37; 5.56]) times higher for non-smokers and 2.22 (95% CI [1.27; 3.85]) times higher when less than three teeth would require extraction. No significant associations with the prescription of systemic antibiotics were found for detection of A.actinomycetemcomitans (p = 0.287), grade of periodontitis (p = 0.499) and gender of the patient (p = 0.067). CONCLUSIONS: Based on a limited number of cases and clinicians, several patient and clinician related factors were associated with prescription of systemic antibiotics following initial periodontal therapy. However, microbiological testing was not.

Metalloproteinases 1 and 3 as Potential Biomarkers in Breast Cancer Development.

Breast cancer continues to be one of the main causes of morbidity and mortality globally and was the leading cause of cancer death in women in Spain in 2020. Early diagnosis is one of the most effective methods to lower the incidence and mortality rates of breast cancer. The human metalloproteinases (MMP) mainly function as proteolytic enzymes degrading the extracellular matrix and plays important roles in most steps of breast tumorigenesis. This retrospective cohort study shows the immunohistochemical expression levels of MMP-1, MMP-2, MMP-3, and MMP-9 in 154 women with breast cancer and 42 women without tumor disease. The samples of breast tissue are assessed using several tissue matrices (TMA). The percentages of staining (≤50%->50%) and intensity levels of staining (weak, moderate, or intense) are considered. The immunohistochemical expression of the MMP-1-intensity (p = 0.043) and MMP-3 percentage (p = 0.018) and intensity, (p = 0.025) present statistically significant associations with the variable group (control-case); therefore, expression in the tumor tissue samples of these MMPs may be related to the development of breast cancer. The relationships between these MMPs and some clinicopathological factors in breast cancer are also evaluated but no correlation is found. These results suggest the use of MMP-1 and MMP-3 as potential biomarkers of breast cancer diagnosis.

Advances in Lipidomics for Cancer Biomarkers Discovery.

Lipids play critical functions in cellular survival, proliferation, interaction and death, since they are involved in chemical-energy storage, cellular signaling, cell membranes, and cell-cell interactions. These cellular processes are strongly related to carcinogenesis pathways, particularly to transformation, progression, and metastasis, suggesting the bioactive lipids are mediators of a number of oncogenic processes. The current review gives a synopsis of a lipidomic approach in tumor characterization; we provide an overview on potential lipid biomarkers in the oncology field and on the principal lipidomic methodologies applied. The novel lipidomic biomarkers are reviewed in an effort to underline their role in diagnosis, in prognostic characterization and in prediction of therapeutic outcomes. A lipidomic investigation through mass spectrometry highlights new insights on molecular mechanisms underlying cancer disease. This new understanding will promote clinical applications in drug discovery and personalized therapy.

Development and validation of predictive model based on deep learning method for classification of dyslipidemia in Chinese medicine.

Backgrounds: Dyslipidemia is a prominent risk factor for cardiovascular diseases and one of the primary independent modifiable factors of diabetes and stroke. Statins can significantly improve the prognosis of dyslipidemia, but its side effects cannot be ignored. Traditional Chinese Medicine (TCM) has been used in clinical practice for more than 2000 years in China and has certain traits in treating dyslipidemia with little side effect. Previous research has shown that Mutual Obstruction of Phlegm and Stasis (MOPS) is the most common dyslipidemia type classified in TCM. However, how to compose diagnostic factors in TCM into diagnostic rules relies heavily on the doctor's experience, falling short in standardization and objectiveness. This is a limit for TCM to play its advantages of treating dyslipidemia with MOPS. Methods: In this study, the syndrome diagnosis in TCM was transformed into the prediction and classification problem in artificial intelligence The deep learning method was employed to build the classification prediction models for dyslipidemia. The models were built and trained with a large amount of multi-centered clinical data on MOPS. The optimal model was screened out by evaluating the performance of prediction models through loss, accuracy, precision, recall, confusion matrix, PR and ROC curve (including AUC). Results: A total of 20 models were constructed through the deep learning method. All of them performed well in the prediction of dyslipidemia with MOPS. The model-11 is the optimal model. The evaluation indicators of model-11 are as follows: The true positive (TP), false positive (FP), true negative (TN) and false negative (FN) are 51, 15, 129, and 9, respectively. The loss is 0.3241, accuracy is 0.8672, precision is 0.7138, recall is 0.8286, and the AUC is 0.9268. After screening through 89 diagnostic factors of TCM, we identified 36 significant diagnosis factors for dyslipidemia with MOPS. The most outstanding diagnostic factors from the importance were dark purple tongue, slippery pulse and slimy fur, etc. Conclusions: This study successfully developed a well-performing classification prediction model for dyslipidemia with MOPS, transforming the syndrome diagnosis problem in TCM into a prediction and classification problem in artificial intelligence. Patients with dyslipidemia of MOPS can be accurately recognized through limited information from patients. We also screened out significant diagnostic factors for composing diagnostic rules of dyslipidemia with MOPS. The study is an avant-garde attempt at introducing the deep-learning method into the research of TCM, which provides a useful reference for the extension of deep learning method to other diseases and the construction of disease diagnosis model in TCM, contributing to the standardization and objectiveness of TCM diagnosis.

Development and Validation of Predictive Model for a Diagnosis of First Episode Psychosis Using the Multinational EU-GEI Case-control Study and Modern Statistical Learning Methods.

Background and Hypothesis: It is argued that availability of diagnostic models will facilitate a more rapid identification of individuals who are at a higher risk of first episode psychosis (FEP). Therefore, we developed, evaluated, and validated a diagnostic risk estimation model to classify individual with FEP and controls across six countries. Study Design: We used data from a large multi-center study encompassing 2627 phenotypically well-defined participants (aged 18-64 years) recruited from six countries spanning 17 research sites, as part of the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions study. To build the diagnostic model and identify which of important factors for estimating an individual risk of FEP, we applied a binary logistic model with regularization by the least absolute shrinkage and selection operator. The model was validated employing the internal-external cross-validation approach. The model performance was assessed with the area under the receiver operating characteristic curve (AUROC), calibration, sensitivity, and specificity. Study Results: Having included preselected 22 predictor variables, the model was able to discriminate adults with FEP and controls with high accuracy across all six countries (rangesAUROC = 0.84-0.86). Specificity (range = 73.9-78.0%) and sensitivity (range = 75.6-79.3%) were equally good, cumulatively indicating an excellent model accuracy; though, calibration slope for the diagnostic model showed a presence of some overfitting when applied specifically to participants from France, the UK, and The Netherlands. Conclusions: The new FEP model achieved a good discrimination and good calibration across six countries with different ethnic contributions supporting its robustness and good generalizability.

Risk and diagnostic factors and therapy outcome of neonatal early onset sepsis in ICU patients of Saudi Arabia: a systematic review and meta analysis.

Background: Neonatal early onset sepsis (NEOS) is a serious and potentially life-threatening condition affecting newborns within the first few days of life. While the diagnosis of NEOS was based on clinical signs and symptoms in the past, recent years have seen growing interest in identifying specific diagnostic factors and optimizing therapy outcomes. This study aims to investigate the diagnostic and risk factors and therapy outcomes of neonatal EOS in ICU patients in Saudi Arabia, with the goal of improving the management of neonatal EOS in the country. Methods: This method outlines the protocol development, search strategy, study selection, and data collection process for a systematic review on neonatal early onset sepsis in Saudi Arabian ICU patients, following the PRISMA 2020 guidelines. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is a well-established guideline that provides a framework for conducting systematic reviews and meta-analyses in a transparent and standardized manner. It aims to improve the quality and reporting of such research by ensuring clear and comprehensive reporting of study methods, results, and interpretations. The search strategy included electronic databases (PubMed, Embase, Google Scholar, Science Direct, and the Cochrane Library) and manual search of relevant studies, and data were extracted using a standardized form. Results: The systematic review included 21 studies on neonatal sepsis in Saudi Arabia, with varying study designs, sample sizes, and prevalence rates of sepsis. Group B streptococcus and E. coli were the most commonly isolated pathogens. Various diagnostic factors and risk factors were reported, including hematological parameters, biomarkers, and blood cultures. The quality of the included studies was assessed using the Newcastle-Ottawa Scale and Joanna Briggs Institute critical checklist. Conclusions: The review identified a number of risk and diagnostic factors and therapy outcomes for neonatal sepsis. However, most of the studies were having small scale cohort groups. Further research with controlled study designs is needed to develop effective prevention and management strategies for neonatal sepsis in Saudi Arabia.