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Genetic Predictors for Bacterial Vaginosis in Women Living With and at Risk for HIV Infection
No full textPROBLEM: Bacterial vaginosis (BV) disproportionally impacts Black and Hispanic women, placing them at risk for HIV, sexually transmitted infections and preterm birth. It is unknown whether there are differences by genetic ancestry in BV risk or whether polymorphisms associated with BV risk differ by ancestry. METHODS: Women\u27s Interagency HIV Study (WIHS) participants with longitudinal Nugent scores were dichotomized as having (n = 319, Nugent 7-10) or not having BV (n = 367, Nugent 0-3). Genetic ancestry was defined by clustering of principal components from ancestry informative markers and further stratified by BV status. 627 single nucleotide polymorphisms (SNPs) across 41 genes important in mucosal defense were identified in the WIHS GWAS. A logistic regression analysis was adjusted for nongenetic predictors of BV and self-reported race/ethnicity to assess associations between genetic ancestry and genotype. RESULTS: Self-reported race and genetic ancestry were associated with BV risk after adjustment for behavioral factors. Polymorphisms in mucosal defense genes including syndecans, cytokines and toll-like receptors (TLRs) were associated with BV in all ancestral groups. CONCLUSIONS: The common association of syndecan, cytokine and TLR genes and the importance of immune function and inflammatory pathways in BV, suggests these should be targeted for further research on BV pathogenesis and therapeutics
Guideline-Based Management of Metabolic Dysfunction-Associated Steatotic Liver Disease in the Primary Care Setting
No full textPharmacotherapies in Heart Failure With Preserved Ejection Fraction: A Systematic Review and Network Meta-Analysis
No full textVarious pharmacotherapies exist for heart failure with preserved ejection fraction (HFpEF), but with unclear comparative efficacy. We searched EMBASE, Medline, and Cochrane Library from inception through August 2021 for all randomized clinical trials in HFpEF (EF \u3e40%) that evaluated beta-blockers, mineralocorticoid receptor antagonist (MRA), angiotensin-converting enzyme inhibitors (ACE), angiotensin receptor blockers (ARB), angiotensin receptor-neprilysin inhibitor (ARNI), and sodium-glucose cotransporter-2 inhibitors (SGLT2i). Outcomes assessed were cardiovascular mortality, all-cause mortality, and HF hospitalization. A frequentist network meta-analysis was performed with a random-effects model. We included 22 randomized clinical trials (30,673 participants; mean age = 71.7 ± 4.2 years; females = 49.3 ± 7.7%; median follow-up = 24.4 ± 11.1 months). Compared with placebo, there was no statistically significant difference in cardiovascular mortality [beta-blockers; odds ratio (OR) 0.79 (0.46-1.34), MRA; OR 0.90 (0.70-1.14), ACE OR 0.95 (0.59-1.53), ARB; OR 1.02 (0.87-1.19), ARNI; OR 0.97 (0.74-1.26) and SGLT2i; OR 1.00 (0.84-1.18)] or all-cause mortality [beta blockers; OR 0.75 (0.54-1.04), MRA; OR 0.90 (0.75-1.08) ACE; OR 1.05 (0.71-1.54), ARB; OR 1.03 (0.91-1.15), ARNI; OR 0.99 (0.82-1.20) and SGLT2i; OR 1.00 (0.89-1.13)]. The certainty in these estimates was low or very low. There was a significantly reduction in HF hospitalization with the use of SGLT2i [OR 0.71 (0.62-0.82), moderate certainty], ARNI [OR 0.77 (0.63-0.94), low certainty], and MRA [OR 0.81 (0.66-0.98), moderate certainty]; with corresponding P scores of 0.84, 0.68, and 0.58, respectively. In HFpEF, the use of beta-blockers, MRA, ACE/ARB/ARNI, or SGLT2i was not associated with improved cardiovascular or all-cause mortality. SGLT2i, ARNI, and MRA reduced the risk of HF hospitalizations
Impact of Social Vulnerability on Cardiac Arrest Mortality in the United States, 2016 to 2020
No full textBACKGROUND: Cardiac arrest is 1 of the leading causes of morbidity and mortality, with an estimated 340 000 out-of-hospital and 292 000 in-hospital cardiac arrest events per year in the United States. Survival rates are lower in certain racial and socioeconomic groups.
METHODS AND RESULTS: We performed a county-level cross-sectional longitudinal study using the Centers for Disease Control and Prevention\u27s Wide-Ranging Online Data for Epidemiologic Research multiple causes of death data set between 2016 and 2020 among individuals of all ages whose death was attributed to cardiac arrest. The Social Vulnerability Index is a composite measure that includes socioeconomic vulnerability, household composition, disability, individuals from racial and ethnic minority groups status and language, and housing and transportation domains. We examined the impact of social determinants on cardiac arrest mortality stratified by age, race, ethnicity, and sex in the United States. All age-adjusted mortality rate (cardiac arrest AAMRs) are reported as per 100 000. Overall cardiac arrest AAMR during the study period was 95.6. The cardiac arrest AAMR was higher for men compared with women (119.6 versus 89.9) and for the Black population compared with the White population (150.4 versus 92.3). The cardiac arrest AAMR increased from 64.8 in counties in quintile 1 of Social Vulnerability Index to 141 in quintile 5, with an average increase of 13% (95% CI, 9.8%-16.9%) in AAMR per quintile increase.
CONCLUSIONS: Mortality from cardiac arrest varies widely, with a \u3e2-fold difference between the counties with the highest and lowest social vulnerability, highlighting the differential burden of cardiac arrest deaths throughout the United States based on social determinants of health
Characteristics and Outcomes of Patients Treated With Cervical Spine Fusion at High Volume Hospitals
No full textBACKGROUND: High volume (HV) has been associated with improved outcomes in various neurosurgical procedures. The objective of this study was to explore the regional distribution of HV spine centers for cervical spine fusion and compare characteristics and outcomes for patients treated at HV centers versus lower volume centers.
METHODS: The National Inpatient Sample database 2016-2020 was queried for patients undergoing cervical spine fusion for degenerative pathology. HV was defined as case-loads greater than 2 standard deviations above the mean. Patient characteristics, procedures, and outcomes were compared.
RESULTS: Of 3895 hospitals performing cervical spine fusion for degenerative pathology, 28 (0.76%) were HV. The Mid-Atlantic and West South Central regions had the highest number of HV hospitals. HV hospitals were more likely to perform open anterior fusion surgeries (P \u3c 0.01). Patients treated at HV hospitals were less likely to have severe symptomatology or comorbidities (P \u3c 0.01 for all). When controlling for severity and demographics on multivariate analysis, HV centers had higher odds of length of stay ≤1 day, favorable discharge, and decreased total charges.
CONCLUSIONS: Patients who underwent cervical spine fusion surgery at HV hospitals were less complex and had increased odds of length of stay ≤1, favorable discharge, and total charges in the lower 25th percentile than patients treated at non-HV hospitals. Physician comfort, patient selection, institutional infrastructure, and geographic characteristics likely play a role
Growth Hormone Therapy Does Not Impact the Development of Intracranial Hypertension in Children With Chiari Malformation
No full textOBJECTIVES: Patients with Chiari malformation (CM) are prone to a variety of neurological sequelae, including benign intracranial hypertension (BIH). In these patients, BIH is attributed to impaired cerebrospinal fluid (CSF) flow due to anatomical abnormalities of the posterior fossa. Occasionally, patients with CM may require growth hormone therapy (GHT), which can increase the production of CSF. It is thought that patients with CM who undergo GHT are at high risk of BIH-associated symptoms (BIHAS). We describe the incidence of neurological symptoms in 34 patients with CM before and during GHT. METHODS: The database of a pediatric endocrinology center was queried for patients with CM who received GHT from 2010-22. Records were reviewed for adverse events. Demographic and radiological data were collected and analyzed. Patients with neoplastic disease, active inflammation, or acute trauma were excluded. CM diagnoses were independently assigned by a neuroradiology department. Patients were grouped based on the presence and nature of symptoms before and during GHT. Relationships between starting dose/BMI and occurrence of BIHAS/all GHT-associated symptoms were evaluated. RESULTS: GHT was not associated with new-onset or worsening of preexisting BIHAS in 33 out of 34 patients with CM. Five complex patients continued to have preexisting BIHAS, which did not worsen. Of the four patients who developed new-onset BIHAS during GHT, three patients\u27 symptoms were attributed to other medical conditions. No patient permanently discontinued GHT due to BIHAS. CONCLUSIONS: Growth hormone therapy is likely a safe treatment in patients with Chiari malformation and is unlikely to cause BIHAS
Response to Low-Dose Oral Minoxidil for Androgenetic Alopecia Is Not Associated With Clinically Significant Blood-Pressure Changes: A Retrospective Study
No full textExploring Berberine as a Novel Therapeutic Agent Against Staphylococcus aureus Infections: Targeting Inflammation and Antibiotic Resistance
Get PDFStaphylococcus aureus is a versatile Gram-positive bacterium commonly found on human skin. While often harmless, it can cause various infections when the skin is compromised, ranging from minor issues to life-threatening conditions in immunocompromised individuals.
The bacterium’s ability to resist antibiotics, particularly methicillin, underscores its clinical challenge. Staphylococcus aureus can also acquire vancomycin resistance. It evades immune responses through multiple mechanisms, including capsule formation, interference with immune proteins like staphylococcal protein A, and secretion of toxins such as Panton-Valentine Leukocidin. Understanding these mechanisms is crucial for developing effective strategies against Staphylococcus aureus infections.
The immune response to Staphylococcus aureus infections is orchestrated through a complex interplay of innate and adaptive immune mechanisms. Upon encountering Staphylococcus aureus, innate immune cells such as neutrophils, monocytes/macrophages, dendritic cells, and natural killer cells are mobilized to the infection site. Neutrophils play a crucial role in the initial defense by phagocytosing bacteria and releasing antimicrobial peptides and reactive oxygen species through an oxidative burst. Monocytes differentiate into macrophages that engulf bacteria and secrete cytokines and chemokines to recruit and activate other immune cells. Dendritic cells process and present Staphylococcus aureus antigens to T cells, initiating adaptive immune responses. T cells, including CD4+ and CD8+ subsets, differentiate into various effector and memory cells to coordinate long-term immune defense. B cells produce antibodies that neutralize toxins and facilitate bacterial clearance. The inflammatory cytokines IL-1β, IL-6, TNF-α, and IFN-γ orchestrate immune responses, regulating inflammation and coordinating immune cell communication. These immune components form a robust defense system against Staphylococcus aureus, although the bacterium’s ability to evade immune detection poses challenges in treatment and vaccine development.
In this study, berberine, a natural compound derived from plants like goldenseal and barberry, was explored for its potential as an inhibitor of Staphylococcus aureus mediated inflammation. Known for its broad antimicrobial properties and therapeutic benefits in traditional medicine systems such as Traditional Chinese Medicine and Ayurveda, berberine has been extensively studied for its ability to combat various infections and inflammatory conditions without causing liver damage. It acts by disrupting bacterial cell membranes, inhibiting biofilm formation, and reducing virulence factors, making it a promising candidate against antibiotic-resistant strains like methicillin-resistant Staphylococcus aureus. Furthermore, berberine exhibits potent anti- inflammatory effects by modulating cytokine production and inflammatory pathways, which could mitigate excessive inflammation during Staphylococcus aureus infections.
We hypothesized that berberine can significantly inhibit TNF-α production, gene expression involved in inflammatory pathways, and reactive-oxygen species production in response to Staphylococcus aureus stimulation in both mouse macrophage and human monocyte cell lines. The results demonstrated that berberine effectively inhibited TNF-α production in RAW 264.7 mouse macrophage and U937 human monocyte cell lines when stimulated with heat-killed Staphylococcus aureus (ATCC strain #33591). This inhibition was dose-dependent, with 20 µg/mL of berberine suppressing over 90% of TNF-α production compared to controls (p \u3c 0.001), highlighting its robust anti-inflammatory activity without inducing cytotoxic effects.
Moreover, when tested against clinical isolates of Staphylococcus aureus (EC01-EC04) isolated from the skin of eczema patients, berberine at concentrations ranging from 2.5 to 20 µg/mL consistently inhibited TNF-α production by over 90% (p \u3c 0.001) in U937 cells, reinforcing its potential clinical relevance. Berberine also significantly downregulated key genes involved in inflammatory pathways in U937 cells and inhibited reactive oxygen species production in both RAW 264.7 and U937 cells at 20 µg/mL (p \u3c 0.01), indicating its capacity to reduce oxidative stress associated with bacterial infections.
Overall, these results suggest that berberine possesses dual therapeutic actions against Staphylococcus aureus infections by suppressing inflammatory responses and oxidative stress. This study provides a strong foundation for further exploration of berberine’s clinical applications, potentially as an adjunct therapy alongside conventional antibiotics, addressing critical clinical needs in infectious and inflammatory diseases
Performance of Automated Machine Learning in Predicting Outcomes of Pneumatic Retinopexy
No full textPURPOSE: Automated machine learning (AutoML) has emerged as a novel tool for medical professionals lacking coding experience, enabling them to develop predictive models for treatment outcomes. This study evaluated the performance of AutoML tools in developing models predicting the success of pneumatic retinopexy (PR) in treatment of rhegmatogenous retinal detachment (RRD). These models were then compared with custom models created by machine learning (ML) experts. DESIGN: Retrospective multicenter study. PARTICIPANTS: Five hundred and thirty nine consecutive patients with primary RRD that underwent PR by a vitreoretinal fellow at 6 training hospitals between 2002 and 2022. METHODS: We used 2 AutoML platforms: MATLAB Classification Learner and Google Cloud AutoML. Additional models were developed by computer scientists. We included patient demographics and baseline characteristics, including lens and macula status, RRD size, number and location of breaks, presence of vitreous hemorrhage and lattice degeneration, and physicians\u27 experience. The dataset was split into a training (n = 483) and test set (n = 56). The training set, with a 2:1 success-to-failure ratio, was used to train the MATLAB models. Because Google Cloud AutoML requires a minimum of 1000 samples, the training set was tripled to create a new set with 1449 datapoints. Additionally, balanced datasets with a 1:1 success-to-failure ratio were created using Python. MAIN OUTCOME MEASURES: Single-procedure anatomic success rate, as predicted by the ML models. F2 scores and area under the receiver operating curve (AUROC) were used as primary metrics to compare models. RESULTS: The best performing AutoML model (F2 score: 0.85; AUROC: 0.90; MATLAB), showed comparable performance to the custom model (0.92, 0.86) when trained on the balanced datasets. However, training the AutoML model with imbalanced data yielded misleadingly high AUROC (0.81) despite low F2-score (0.2) and sensitivity (0.17). CONCLUSIONS: We demonstrated the feasibility of using AutoML as an accessible tool for medical professionals to develop models from clinical data. Such models can ultimately aid in the clinical decision-making, contributing to better patient outcomes. However, outcomes can be misleading or unreliable if used naively. Limitations exist, particularly if datasets contain missing variables or are highly imbalanced. Proper model selection and data preprocessing can improve the reliability of AutoML tools. FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article