Tag: nevin manimala

J Infect Dev Ctries. 2023 Apr 30;17(4):454-460. doi: 10.3855/jidc.17065.

ABSTRACT

INTRODUCTION: The COVID-19 pandemic has disproportionately affected patients with preexisting comorbidities, particularly dialysis patients. The aim of this study was to determine predictors of mortality in this population.

METHODOLOGY: We conducted an observational, retrospective, cohort study collecting data from pre and post-vaccine from the electronic medical records of a single dialysis center at Hygeia International Hospital Tirana, Albania.

RESULTS: Of 170 dialysis patients, 52 were diagnosed with COVID-19. The prevalence of COVID-19 infection in our study was 30.5%. The mean age was 61.5 ± 12.3 years and 65.4% were men. The mortality rate in our cohort was 19.2%. Mortality rates were higher in patients with diabetic nephropathy (p < 0.04) and peripheral vascular disease (p < 0.01). Elevated C- reactive protein (CRP) (p < 0.018), high red blood cell distribution width (RDW) (p < 0.03), and low lymphocyte and eosinophil counts, were found to be risk factors for severe COVID-19 disease. ROC analysis identified lymphopenia and eosinopenia as the strongest predictors of mortality. After the vaccine administration, the mortality rate in the vaccinated population was 8%, in contrast to the 66.7% mortality rate that was found in the unvaccinated group (p < 0.001).

CONCLUSIONS: Our study revealed that risk factors for the development of severe COVID-19 infection were RDW, low lymphocyte and eosinophil counts, elevated levels of CRP. Lymphopenia and eosinopenia were determined as the most important predictors of mortality, in our cohort. Mortality was notably lower among vaccinated patients.

PMID:37159892 | DOI:10.3855/jidc.17065

J Infect Dev Ctries. 2023 Apr 30;17(4):425-431. doi: 10.3855/jidc.17428.

ABSTRACT

INTRODUCTION: Previous studies that identified the prognostic factors for the severity of the new coronavirus disease 2019 (COVID-19) in different populations have generated controversial conclusions. The lack of a standard definition of COVID-19 severity and the differences between clinical diagnoses might make it difficult to provide optimum care according to the characteristics of each population.

METHODOLOGY: We investigated the factors that impacted the severe outcome or death from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in patients treated at the Mexican Institute of Social Security in Yucatán, México in 2020. A cross-sectional study of COVID-19 confirmed cases was done to know the prevalence and association of the demographic and clinical characteristics with a severe or fatal outcome. Information from the National Epidemiological Surveillance System (SINAVE) database was used and SPSS v 21 was used for statistical analyses. We used the World Health Organization (WHO) and the Centers for Diseases Control and Prevention (CDC) symptomatology classifications to define severe cases.

RESULTS: Diabetes and pneumonia increased the risk of death and having diabetes was a prognostic factor for severe illness following SARS-CoV-2 infection.

CONCLUSIONS: Our results highlight the influence of cultural and ethnic factors, the necessity to standardize the parameters for clinical diagnoses, and to use the same criteria for the definition of COVID-19 severity to establish the clinical conditions that contribute to the pathophysiology of this disease in each population.

PMID:37159885 | DOI:10.3855/jidc.17428

J Infect Dev Ctries. 2023 Apr 30;17(4):477-484. doi: 10.3855/jidc.17212.

ABSTRACT

INTRODUCTION: Enterobacteriaceae that produce extended-spectrum beta-lactamase (ESBL) are quickly spreading, posing a threat to world healthcare.

METHODOLOGY: 138 gram-negative bacteria were collected from different samples (stool, urine, wound, blood, tracheal aspirate, catheter tip, vaginal swab, sputum, and tracheal aspirate) from hospitalized patients. Samples were subcultured and identified in accordance with their biochemical reactions and culture characteristics. Against all the isolated Enterobacteriaceae, an antimicrobial susceptibility test was performed. VITEK®2 system, phenotypic confirmation, and Double-Disk Synergy Test (DDST) had been utilized to identify the ESBLs.

RESULTS: Of the 138 samples studied, the prevalence of ESBL-producing infections among the clinical samples of the present study was 26.8 % (n = 37). E. coli was the commonest ESΒL producer at 51.4% (n = 19) followed by K. pneumoniae at 27% (n = 10). The potential risk factors for the ESBL development that produces bacteria were as follows, patients with the presence of indwelling devices, previous history of hospital admission, and usage of antibiotics. ESBL is statistically (p ≤ 0.05) higher among the patients with indwelling devices, ICU admission, who had a previous hospital admission in the last 6 months as well as who was given antibiotics (quinolones and/or cephalosporins) in the last 6 months. One hundred thirty-two (95.7%) of ESBL isolates were resistant to amoxicillin, while the lowest resistance was for fosfomycin (15.2%).

CONCLUSIONS: ESBL-producing Enterobacteriaceae are highly prevalent in Turaif General Hospital setting with some potential risk factors. A strict policy to be made available on the usage of antimicrobials in hospitals and clinics should be established.

PMID:37159882 | DOI:10.3855/jidc.17212

JCO Clin Cancer Inform. 2023 May;7:e2200174. doi: 10.1200/CCI.22.00174.

ABSTRACT

PURPOSE: Real-world data (RWD) derived from electronic health records (EHRs) are often used to understand population-level relationships between patient characteristics and cancer outcomes. Machine learning (ML) methods enable researchers to extract characteristics from unstructured clinical notes, and represent a more cost-effective and scalable approach than manual expert abstraction. These extracted data are then used in epidemiologic or statistical models as if they were abstracted observations. Analytical results derived from extracted data in this way may differ from those given by abstracted data, and the magnitude of this difference is not directly informed by standard ML performance metrics.

METHODS: In this paper, we define the task of postprediction inference, which is to recover similar estimation and inference from an ML-extracted variable that would be obtained from abstracting the variable. We consider fitting a Cox proportional hazards model that uses a binary ML-extracted variable as a covariate and evaluate four approaches for postprediction inference in this setting. The first two approaches only require the ML-predicted probability, while the latter two additionally require a labeled (human abstracted) validation data set.

RESULTS: Our results for both simulated data and EHR-derived RWD from a national cohort demonstrate that we can improve inference from ML-extracted variables by leveraging a limited amount of labeled data.

CONCLUSION: We describe and evaluate methods for fitting statistical models using ML-extracted variables subject to model error. We show that estimation and inference is generally valid when using extracted data from high-performing ML models. More complex methods that incorporate auxiliary labeled data provide further improvements.

PMID:37159871 | DOI:10.1200/CCI.22.00174

Int J Biostat. 2023 May 10. doi: 10.1515/ijb-2022-0040. Online ahead of print.

ABSTRACT

In case-control studies, odds ratios (OR) are calculated from 2 × 2 tables and in some instances, we observe small cell counts or zero counts in one of the cells. The corrections to calculate the ORs in the presence of empty cells are available in literature. Some of these include Yates continuity correction and Agresti and Coull correction. However, the available methods provided different corrections and the situations where each could be applied are not very apparent. Therefore, the current research proposes an iterative algorithm of estimating an exact (optimum) correction factor for the respective sample size. This was evaluated by simulating data with varying proportions and sample sizes. The estimated correction factor was considered after obtaining the bias, standard error of odds ratio, root mean square error and the coverage probability. Also, we have presented a linear function to identify the exact correction factor using sample size and proportion.

PMID:37159838 | DOI:10.1515/ijb-2022-0040

PeerJ. 2023 May 3;11:e15288. doi: 10.7717/peerj.15288. eCollection 2023.

ABSTRACT

BACKGROUND: The modern lifestyle trend of pet ownership is undoubtedly beneficial for both physical and mental health. Research has shown a connection between pet ownership and staff self-compassion. However, there has not been any evidence linking pet ownership to self-compassion in the nurse population.

AIMS: To investigate the current status of pet ownership among nurses and explore the influence of pet ownership on self-compassion among nurses.

METHODS: An online survey was conducted in July 2022 with 1,308 nurses in China. Data were collected using a general information questionnaire and a self-compassion scale. To compare categorical variables, the independent t test, one-way ANOVA, and multiple linear regression analysis were utilized. SPSS software was used for the statistical analysis.

RESULTS: We found that 16.9% of nurses owned at least one pet, and dogs and cats were the primary pets. The t test for independent samples showed that pet owners and non-pet owners scored differently on self-compassion (t = 3.286, p = 0.001), self-kindness (t = 3.378, p = 0.001), common humanity (t = 2.419, p = 0.016), and mindfulness (t = 2.246, p = 0.025). One-way ANOVA revealed that the highest degree was an influencing factor of self-compassion (χ ² = 1.386, p = 0.019). Multiple linear regression showed that average monthly income, pet ownership, and highest degree were the factors that influenced self-compassion most significantly (F = 8.335, p < 0.001).

CONCLUSION: The results revealed that nurses actually own pets as part of their modern lifestyle, which provides them with social support and potentially enhances their self-compassion. More efforts should be focused on the impact of pet ownership on nurses’ physical and mental health, and pet-based interventions should also be developed.

PMID:37159831 | PMC:PMC10163869 | DOI:10.7717/peerj.15288

Drug Alcohol Depend Rep. 2023 Apr 14;7:100159. doi: 10.1016/j.dadr.2023.100159. eCollection 2023 Jun.

ABSTRACT

BACKGROUND: In the United States, a disproportionate number of persons with HIV (PWH) and opioid use disorder (OUD) are involved in the justice system. Medications for OUD (MOUD) can reduce convictions and incarceration time in persons with OUD. Extended-release naltrexone (XR-NTX) has been shown to reduce craving of opioids, recurrence of use, and overdose and help achieve or maintain HIV viral suppression in PWH with OUD involved with the justice system.

OBJECTIVES: This retrospective study aimed to describe factors associated with reincarceration and to evaluate if XR-NTX was associated with reduced reincarceration among PWH and OUD who were released to the community from incarceration.

METHODS: Data from participants released to the community from incarceration from a completed randomized controlled trial was analyzed using a generalized linear model to estimate odds ratios associated with reincarceration and a Kaplan-Meier survival analysis to determine time to reincarceration and non-reincarcerated individuals were compared.

RESULTS: Of the 77 participants, 41 (53.2%) were reincarcerated during the 12-month study period. The mean time to reincarceration was 190 days (SD=108.3). Compared with participants who remained in the community, reincarcerated participants were more likely to have major depressive disorder at study baseline, increased opioid cravings, longer mean lifetime incarceration, and a higher physical quality of life score. XR-NTX was not significantly associated statistically with reincarceration in this analysis.

CONCLUSION: Reducing reincarceration is a public health priority, given the high proportion of PWH and OUD in the U.S. justice system as well as high degrees of persons returning to the community and having care interrupted due to reincarceration. This analysis determined that potentially identifying depression in recently released individuals could improve HIV outcomes, decrease recurrence of opioid use, and reduce reincarceration.

PMID:37159815 | PMC:PMC10163604 | DOI:10.1016/j.dadr.2023.100159

Int J Nanomedicine. 2023 May 2;18:2275-2293. doi: 10.2147/IJN.S394504. eCollection 2023.

ABSTRACT

INTRODUCTION: In order to diagnose and treat papillary thyroid carcinoma (PTC) accurately, phase-transition nanoparticles, P@IP-miRNA (PFP@IR780/PLGA-bPEI-miRNA338-3p), was engineered. The nanoparticles (NPs) can target the tumor cells, realize the multimodal imaging, and provide sonodynamic-gene therapy for PTC.

METHODS: P@IP-miRNA NPs were synthesized through double emulsification method, and miRNA338-3p was attached to the surface of the NPs by electrostatic adsorption. The characterization of NPs was detected to screen out qualified nanoparticles. In vitro, laser confocal microscopy and flow cytometry were used to detect the targeting and subcellular localization of NPs. Western blot, qRT-PCR, and immunofluorescence were used to detect the ability to transfect miRNA. CCK8 kit, laser confocal microscopy and flow cytometry were used to detect the inhibition on TPC-1 cells. In vivo experiments were performed based on tumor-bearing nude mice. The efficacy of combined treatment by NPs was comprehensively evaluated, and the multimodal imaging ability of NPs in vivo and in vitro was detected.

RESULTS: P@IP-miRNA NPs were successfully synthesized which have spherical shape, uniform size, good dispersion and positive potential. The encapsulation rate of IR780 was (82.58±3.92) %, the drug loading rate was (6.60±0.32) %, and the adsorption capacity of miRNA338-3p was 41.78 μg/mg. NPs have excellent tumor targeting ability, miRNA transfection ability, ROS production ability and multimodal imaging ability in vivo and in vitro. The antitumor effect of combined treatment group was the best, and the efficacy was better than that of single factor treatment group, and the difference was statistically significant.

CONCLUSION: P@IP-miRNA NPs can realize multimodal imaging and sonodynamic-gene therapy, providing a new idea for accurate diagnosis and treatment of PTC.

PMID:37159806 | PMC:PMC10163883 | DOI:10.2147/IJN.S394504

Pharmgenomics Pers Med. 2023 May 2;16:407-423. doi: 10.2147/PGPM.S404949. eCollection 2023.

ABSTRACT

PURPOSE: Methotrexate (MTX) is used as an anchor drug for the treatment of rheumatoid arthritis (RA) and there may be differences in drug action between genotypes. The purpose of this study was to investigate the relationship between clinical efficacy response and disease activity of MTX monotherapy with methylenetetrahydrofolate reductase (MTHFR) and methionine synthase reductase (MTRR) polymorphisms.

PATIENTS AND METHODS: In the study, a population of 32 patients in East China with early RA fulfilling the diagnostic standards of the American College of Rheumatology (ACR) were enrolled, all of them received MTX monotherapy. Genotyping of patients MTHFR C677T and A1298C, MTRR A66G using tetra-primer ARMS-PCR method and sanger sequencing to verify its accuracy.

RESULTS: The distribution of three polymorphic genotypes that were studied is in accordance with the Hardy-Weinberg genetic equilibrium. The patient pathology variables smoke (OR = 0.088, P = 0.037), drink alcohol (OR = 0.039, P = 0.016) and males (OR = 0.088, P = 0.037) were significantly associated with non-response to MTX. Genotype, allele distribution and genetic statistical models were not found to be related to MTX treatment response and disease activity in both the response groups and non-response groups.

CONCLUSION: Our findings suggest that the MTHFR C677T, MTHFR A1298C and MTRR A66G polymorphisms may not predict MTX clinical treatment response and disease activity in patients with early RA. The study revealed that smoke, alcohol, and males were possible influential factors for MTX non-response.

PMID:37159804 | PMC:PMC10163902 | DOI:10.2147/PGPM.S404949

Pulm Circ. 2023 Apr 1;13(2):e12233. doi: 10.1002/pul2.12233. eCollection 2023 Apr.

ABSTRACT

To better understand the impact of the COVID-19 pandemic on the care of patients with pulmonary hypertension, we conducted a retrospective cohort study evaluating health insurance status, healthcare access, disease severity, and patient reported outcomes in this population. Using the Pulmonary Hypertension Association Registry (PHAR), we defined and extracted a longitudinal cohort of pulmonary arterial hypertension (PAH) patients from the PHAR’s inception in 2015 until March 2022. We used generalized estimating equations to model the impact of the COVID-19 pandemic on patient outcomes, adjusting for demographic confounders. We assessed whether insurance status modified these effects via covariate interactions. PAH patients were more likely to be on publicly-sponsored insurance during the COVID-19 pandemic compared with prior, and did not experience statistically significant delays in access to medications, increased emergency room visits or nights in the hospital, or worsening of mental health metrics. Patients on publicly-sponsored insurance had higher healthcare utilization and worse objective measures of disease severity compared with privately insured individuals irrespective of the COVID-19 pandemic. The relatively small impact of the COVID-19 pandemic on pulmonary hypertension-related outcomes was unexpected but may be due to pre-established access to high quality care at pulmonary hypertension comprehensive care centers. Irrespective of the COVID-19 pandemic, patients who were on publicly-sponsored insurance seemed to do worse, consistent with prior studies highlighting outcomes in this population. We speculate that previously established care relationships may lessen the impact of an acute event, such as a pandemic, on patients with chronic illness.

PMID:37159803 | PMC:PMC10163321 | DOI:10.1002/pul2.12233