Application of principal component analysis and logistic regression model in lupus nephritis patients with clinical hypothyroidism

Surface enhanced Raman spectroscopy and machine learning for identification of beta-lactam antibiotics resistance gene fragment in bacterial plasmid

BACKGROUND

Antibiotic resistance stands as a critical medical concern, notably evident in commonly prescribed beta-lactam antibiotics. The imperative need for expeditious and precise early detection methods underscores their role in facilitating timely intervention, curbing the propagation of antibiotic resistance, and enhancing patient outcomes.

RESULTS

This study introduces the utilization of surface-enhanced Raman spectroscopy (SERS) in tandem with machine learning (ML) for the sensitive detection of characteristic gene fragments responsible for antibiotic resistance appearance and spreading. To make the detection procedure close to the real case, we used bacterial plasmids as starting biological objects, containing or not the characteristic gene fragment (up to 1:10 ratio), encoding beta-lactam antibiotics resistance. The plasmids were subjected to enzymatic digestion and without preliminary purification or isolation the created fragments were captured by functional SERS substrates. Based on subsequent SERS measurements, a database was created for the training and validation of ML. Method validation was performed using separately measured spectra, which did not overlap with the database used for ML training. To check the efficiency of recognising the target fragment, control experiments involved bacterial plasmids containing different resistance genes, the use of inappropriate enzymes, or the absence of plasmid.

SIGNIFICANCE

SERS-ML allowed express detection of bacterial plasmids containing a characteristic gene fragment up to the 10-7 concentration of the initial plasmid, despite the complex composition of the biological sample, including the presence of interfering plasmids. Our approach offers a promising alternative to existing methods for monitoring antibiotic-resistant bacteria, characterized by its simplicity, low detection limit, and the potential for rapid and straightforward analysis.

Relevant Research of Inflammatory Cytokines Spectrum in Peripheral Blood of Sudden Hearing Loss

OBJECTIVE

To investigate whether there is a correlation between the inflammatory state and the pathogenesis and clinical features of sudden hearing loss (SHL) by studying the expression of inflammation-related cytokines in the peripheral blood of patients with SHL.

METHODS

In this work, we analyzed the cytokine profiles of 48 analytes in 38 patients with SHL compared to 38 healthy donors using a multiplex immunoassay. This study used appropriate statistical methods to screen for inflammatory cytokines associated with the pathogenesis of SHL, to analyze their network correlation, and to analyze the relationship between clinical features of SHL and inflammatory cytokines.

RESULTS

Several cytokines, including CTACK, Eotaxin, HGF, INF-α2, IFN-β, IL-1β, IL-1ra, IL-2Rα, IL-4, IL-7, IL-8, IL-9, IL-10, IL-12(p40), IL-13, MIG, β-NGF, SCF, and TNF-α, exhibited significantly higher levels in the peripheral blood of the SHL group compared to the control group. An inflammatory network composed of multiple cytokines, including IL-1β, is a risk factor for the development of SHL.

CONCLUSION

This study identified several inflammatory cytokines with elevated expression, which may be linked with the onset of SHL. The results of this study also provide a basis for the theoretical hypothesis of inflammation in SHL.

LEVEL OF EVIDENCE

3 Laryngoscope, 134:3293-3301, 2024.

Cytoarchitectureal changes in hippocampal subregions of the NZB/W F1 mouse model of lupus

Over 50% of clinical patients affected by the systemic lupus erythematosus disease display impaired neurological cognitive functions and psychiatric disorders, a form called neuropsychiatric systemic lupus erythematosus. Hippocampus is one of the brain structures most sensitive to the cognitive deficits and psychiatric disorders related to neuropsychiatric lupus. The purpose of this study was to compare, layer by layer, neuron morphology in lupus mice model NZB/W F1 versus Wild Type mice. By a morphometric of cells identified on Nissl-stained sections, we evaluated structural alterations between NZB/W F1 and Wild Type mice in seven hippocampal subregions: Molecular dentate gyrus, Granular dentate gyrus, Polymorph dentate gyrus, Oriens layer, Pyramidal layer, Radiatum layer and Lacunosum molecular layer. By principal component analysis we distinguished healthy Wild Type from NZB/W F1 mice. In NZB/W F1 mice hippocampal cytoarchitecture, the neuronal cells resulted larger in size and more regular than those of Wild Type. In NZB/W F1, neurons were usually denser than in WT. The Pyramidal layer neurons were much denser in Wild Type than in NZB/W F1. Application of principal component analysis, allowed to distinguish NZB/W F1 lupus mice from healthy, showing as NZBW subjects presented a scattered distribution and intrasubject variability. Our results show a hypertrophy of the NZB/W F1 hippocampal neurons associated with an increase in perikaryal size within the CA1, CA2, CA3 region and the DG. These results help advance our understanding on hippocampal organization and structure in the NZB/W F1 lupus model, suggesting the hypothesis that the different subregions could be differentially affected in neuropsychiatric systemic lupus erythematosus disease. Leveraging an in-depth analysis of the morphology of neural cells in the hippocampal subregions and applying dimensionality reduction using PCA, we propose an efficient methodology to distinguish pathological NZBW mice from WT mice."

Patient-reported health outcomes of SARS-CoV-2-tested patients presenting to emergency departments: a propensity score-matched prospective cohort study

OBJECTIVE

This study aimed to compare the long-term physical and mental health outcomes of matched severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-positive and SARS-CoV-2-negative patients controlling for seasonal effects.

STUDY DESIGN

This was a retrospective cohort study.

METHODS

This study enrolled patients presenting to emergency departments participating in the Canadian COVID-19 Emergency Department Rapid Response Network. We enrolled consecutive eligible consenting patients who presented between March 1, 2020, and July 14, 2021, and were tested for SARS-CoV-2. Research assistants randomly selected four site and date-matched SARS-CoV-2-negative controls for every SARS-CoV-2-positive patient and interviewed them at least 30 days after discharge. We used propensity scores to match patients by baseline characteristics and used linear regression to compare Veterans RAND 12-item physical health component score (PCS) and mental health component scores (MCS), with higher scores indicating better self-reported health.

RESULTS

We included 1170 SARS-CoV-2-positive patients and 3716 test-negative controls. The adjusted mean difference for PCS was 0.50 (95% confidence interval [CI]: -0.36, 1.36) and -1.01 (95% CI: -1.91, -0.11) for MCS. Severe disease was strongly associated with worse PCS (β = -7.4; 95% CI: -9.8, -5.1), whereas prior mental health illness was strongly associated with worse MCS (β = -5.4; 95% CI: -6.3, -4.5).

CONCLUSION

Physical health, assessed by PCS, was similar between matched SARS-CoV-2-positive and SARS-CoV-2-negative patients, whereas mental health, assessed by MCS, was worse during a time when the public experienced barriers to care. These results may inform the development and prioritization of support programs for patients.

Dealing With Missing, Imbalanced, and Sparse Features During the Development of a Prediction Model for Sudden Death Using Emergency Medicine Data: Machine Learning Approach

BACKGROUND

In emergency departments (EDs), early diagnosis and timely rescue, which are supported by prediction modes using ED data, can increase patients' chances of survival. Unfortunately, ED data usually contain missing, imbalanced, and sparse features, which makes it challenging to build early identification models for diseases.

OBJECTIVE

This study aims to propose a systematic approach to deal with the problems of missing, imbalanced, and sparse features for developing sudden-death prediction models using emergency medicine (or ED) data.

METHODS

We proposed a 3-step approach to deal with data quality issues: a random forest (RF) for missing values, k-means for imbalanced data, and principal component analysis (PCA) for sparse features. For continuous and discrete variables, the decision coefficient R² and the κ coefficient were used to evaluate performance, respectively. The area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) were used to estimate the model's performance. To further evaluate the proposed approach, we carried out a case study using an ED data set obtained from the Hainan Hospital of Chinese PLA General Hospital. A logistic regression (LR) prediction model for patient condition worsening was built.

RESULTS

A total of 1085 patients with rescue records and 17,959 patients without rescue records were selected and significantly imbalanced. We extracted 275, 402, and 891 variables from laboratory tests, medications, and diagnosis, respectively. After data preprocessing, the median R² of the RF continuous variable interpolation was 0.623 (IQR 0.647), and the median of the κ coefficient for discrete variable interpolation was 0.444 (IQR 0.285). The LR model constructed using the initial diagnostic data showed poor performance and variable separation, which was reflected in the abnormally high odds ratio (OR) values of the 2 variables of cardiac arrest and respiratory arrest (201568034532 and 1211118945, respectively) and an abnormal 95% CI. Using processed data, the recall of the model reached 0.746, the F₁-score was 0.73, and the AUROC was 0.708.

CONCLUSIONS

The proposed systematic approach is valid for building a prediction model for emergency patients.

Hashimoto's thyroiditis, vitiligo, anemia, pituitary hyperplasia, and lupus nephritis-A case report of autoimmune polyglandular syndrome type III C + D and literature review

Objective

This study aims to summarize the clinical characteristics of one teenager with autoimmune polyglandular syndrome (APS) type III C + D to improve the understanding of APS III C + D and its effect of thyroid function.

Methods

This article reported the clinical manifestations, laboratory examinations, treatment methods, and outcomes of an adolescent with anemia admitted to the Pediatrics Department of Tianjin Medical University General Hospital in July 2020 and reviewed the literature.

Results

A girl, aged 13 years and 1 month, was admitted to the hospital due to anemia for more than 4 years and episodic abdominal pain for 1 week. Four years ago, the girl went to a local hospital for "vitiligo", and a routine blood test revealed anemia. The lowest hemoglobin (HGB) was 61 g/L, and the blood test revealed iron deficiency anemia. She had no menstrual cramps for 2 months. Urine routine showed protein 3+∼4+ and 258 red blood cells (RBCs)/high-power field. Urine protein was 3,380 mg/24 h. Free thyroxine was low, thyroid-stimulating hormone was >100 uIU/ml, thyroid peroxidase antibody was >1,000 IU/ml, and thyroglobulin antibody and thyrotropin receptor antibody were negative. Pituitary magnetic resonance imaging showed a mass in the sellar region with a uniform signal and a maximum height of about 15.8 mm. The result of the antinuclear antibody was 1:80 homogeneous type, and anti-dsDNA and anticardiolipin antibodies IgA and IgM were slightly higher. Thyroxine and iron were given for 1 month, menstruation resumed, and urine protein and RBC count decreased. After 5 months of treatment, free thyroid function, HGB, RBCs in urine, and pituitary returned to normal. Later, a renal biopsy showed changes in focal proliferative glomerulonephritis, and the girl was diagnosed with lupus glomerulonephritis type III. After 3 days of shock therapy with methylprednisolone, prednisone, mycophenolate mofetil, and other treatments were administrated for 1 year. At the time of writing, urine protein was 280 mg/24 h.

Conclusion

Co-occurrence of Hashimoto's thyroiditis, vitiligo, anemia, pituitary hyperplasia, and lupus nephritis is rare. It is very important to pay attention to the screening of thyroid function.

Evaluation of Risk Factors for Chronic Obstructive Pulmonary Disease in the Middle-Aged and Elderly Rural Population of Northeast China Using Logistic Regression and Principal Component Analysis

Purpose

To investigate the environmental, immune, and inflammatory factors associated with chronic obstructive pulmonary disease (COPD) in middle-aged and older Chinese individuals.

Patients and Methods

A community-based case–control study was conducted among 471 patients with COPD and 485 controls. The information on COPD of the participants was collected through face-to-face interviews, and serum samples were measured at the laboratory. The main risk factors for COPD were analyzed using principal component analysis (PCA) and logistic regression.

Results

Nine hundred and fifty-six respondents were included in the analysis. The results of the PCA-logistic regression analysis showed significant differences in the environmental factors, medical history, and serum C-reactive protein (CRP) levels between patients and controls. COPD was markedly more usual in those with smoking index >200 (OR, 1.42; 95% CI, 1.28–1.57); exposure to outdoor straw burning (OR, 1.64; 95% CI, 1.47–1.83); use of coal, wood, and straw indoors (OR, 2.31; 95% CI, 1.92–2.78); history of respiratory disease and coronary heart disease (OR, 3.58; 95% CI, 3.12–4.10), congestive heart failure (OR, 1.23; 95% CI, 1.09–1.38), and cerebrovascular disease (OR, 1.15; 95% CI,1.02–1.31); and higher serum level of CRP (OR, 1.20; 95% CI, 1.11–1.30). Compared to the logistic regression analysis, PCA logistic regression analysis identified more important risk factors for COPD.

Conclusion

PCA-logistic regression analysis was first utilized to explore the influencing factors among rural residents in Northeast China Environmental aged 40 years and above, it was found that environmental factors, medical history, and serum CRP levels mainly affected the prevalence of COPD.

Heart Failure and PAHs, OHPAHs, and Trace Elements Levels in Human Serum: Results from a Preliminary Pilot Study in Greek Population and the Possible Impact of Air Pollution

Cardiovascular diseases (CVDs) have been associated with environmental pollutants. The scope of this study is to assess any potential relation of polycyclic aromatic hydrocarbons (PAHs), their hydroxylated derivatives, and trace elements with heart failure via their direct determination in human serum of Greek citizens residing in different areas. Therefore, we analyzed 131 samples including cases (heart failure patients) and controls (healthy donors), and the respective demographic data were collected. Significantly higher concentrations (p < 0.05) were observed in cases' serum regarding most of the examined PAHs and their derivatives with phenanthrene, fluorene, and fluoranthene being the most abundant (median of >50 μg L-1). Among the examined trace elements, As, Cd, Cu, Hg, Ni, and Pb were measured at statistically higher concentrations (p < 0.05) in cases' samples, with only Cr being significantly higher in controls. The potential impact of environmental factors such as smoking and area of residence has been evaluated. Specific PAHs and trace elements could be possibly related with heart failure development. Atmospheric degradation and smoking habit appeared to have a significant impact on the analytes' serum concentrations. PCA-logistic regression analysis could possibly reveal common mechanisms among the analytes enhancing the hypothesis that they may pose a significant risk for CVD development.