Tag: nevin manimala

J Natl Cancer Inst. 2023 Nov 7:djad224. doi: 10.1093/jnci/djad224. Online ahead of print.

ABSTRACT

BACKGROUND: Males have 2-3-fold greater risk of cancer than females at most shared anatomic sites, possibly reflecting enhanced immune surveillance against cancer in females. We examined whether these sex differences remained among immunocompromised adults.

METHODS: Using the Transplant Cancer Match (TCM) Study, we estimated the male-to-female incidence rate ratio in TCM (M: F IRRTransplant) for 15 cancer sites diagnosed between 1995-2017 using Poisson regression. M: F IRRs in the general population (M: F IRRGP) were calculated using expected cancer counts from the Surveillance, Epidemiology, and End Results Program, standardized to the transplant population on age, race/ethnicity, and diagnosis year. M: F IRRs were compared using a chi-square test.

RESULTS: Among 343,802 solid organ transplants, 211,206 (61.4%) were among men and 132,596 (38.6%) among women. An excess cancer incidence in males was seen in transplant recipients, but the sex difference was attenuated for cancers of the lip (M: F IRRTransplant: 1.81 vs M: F IRRGP: 3.96; P < 0.0001), stomach (1.51 vs 2.09; P = 0.002), colorectum (0.98 vs 1.43; P < 0.0001), liver (2.39 vs 3.44; P = 0.002), kidney (1.67 vs 2.24; P < 0.0001), bladder (2.02 vs 4.19; P < 0.0001), Kaposi sarcoma (1.79 vs 3.26; P = 0.0009), and non-Hodgkin lymphoma (1.34 vs 1.64; P < 0.0001). The M: F IRRTransplant was not statistically different from the M: F IRRGP for other cancer sites.

CONCLUSIONS: Although male solid organ transplant recipients have higher cancer incidence than females, the attenuation in the M: F ratio for many cancers studied relative to the general population might suggest the importance of immunosurveillance, with some loss of advantage in females due to immunosuppression following transplantation.

PMID:37944040 | DOI:10.1093/jnci/djad224

Gerontologist. 2023 Nov 7:gnad152. doi: 10.1093/geront/gnad152. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVES: Sleep disorders often predict or co-occur with cognitive decline. Yet, little is known how the relationship unfolds among older adults at risk for cognitive decline.To examine the associations of sleep disorders with cognitive decline in older adults with unimpaired cognition, or impaired cognition (mild cognitive impairment [MCI] and dementia).

RESEARCH DESIGN AND METHODS: 5,822 participants (Mage=70) of the National Alzheimer’s Coordinating Center database with unimpaired or impaired cognition were followed for three subsequent waves. Four types of clinician-diagnosed sleep disorders were reported: sleep apnea, hyposomnia/insomnia, REM sleep behavior disorder, or “other.” Cognition over time was measured by the Montreal Cognitive Assessment (MoCA) or an estimate of general cognitive ability (GCA) derived from scores based on 12 neuropsychological tests. Growth curve models were estimated adjusting for covariates.

RESULTS: In participants with impaired cognition, baseline sleep apnea was related to better baseline MoCA performance (b=0.65, 95%CI=[0.07, 1.23]) and less decline in GCA over time (b=0.06, 95%CI=[0.001, 0.12]). Baseline insomnia was related to better baseline MoCA (b=1.54, 95%CI=[0.88, 2.21]) and less decline in MoCA over time (b=0.56, 95%CI=[0.20, 0.92]). Furthermore, having more sleep disorders (across the four types) at baseline predicted better baseline MoCA and GCA, and less decline in MoCA and GCA over time. These results were only found in those with impaired cognition and generally consistent when using self-reported symptoms of sleep apnea or insomnia.

DISCUSSION AND IMPLICATIONS: Participants with sleep disorder diagnoses may have better access to healthcare, which may help maintain cognition through improved sleep.

PMID:37944004 | DOI:10.1093/geront/gnad152

BJU Int. 2023 Nov 9. doi: 10.1111/bju.16115. Online ahead of print.

ABSTRACT

OBJECTIVES: To compare the diagnostic performance and radiological staging impact of ⁶⁸ Ga-prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) compared to ⁹⁹ Tc whole-body bone scan (WBBS) for the detection of skeletal metastasis in the primary staging of prostate cancer (PCa).

PATIENTS AND METHODS: A prospective institutional database was retrospectively examined for patients who underwent both PSMA PET and WBBS within a 1 week interval for PCa primary staging. Lesions were categorised as ‘negative’, ‘equivocal’, or ‘definite’ based on nuclear medicine physician interpretation. Metastatic burden was characterised for each imaging modality according to three groups: (i) local disease (no skeletal metastases), (ii) oligometastatic disease (three or fewer skeletal metastases), or (iii) polymetastatic disease (more than three skeletal metastases).

RESULTS: There were 667 patients included. The median (interquartile range) prostate-specific antigen level was 9.2 (6.2-16) ng/mL and 60% of patients were high risk according to a modified D’Amico risk classification. The overall distribution of skeletal metastasis detection changed across the two scans overall (P = 0.003), being maintained within high-risk (P = 0.030) and low-risk (P = 0.018) groups. PSMA PET/CT identified more definite skeletal metastases compared to WBBS overall (10.3% vs 7.3%), and according to risk grouping (high: 12% vs 9%, intermediate: 4% vs 1%). Upstaging was more common with PSMA PET/CT than WBBS (P = 0.001). The maximum standardised uptake value (SUV_max ) of the primary tumour was associated with upstaging of skeletal metastases on PSMA PET/CT (P = 0.025), while age was associated with upstaging on WBBS (P = 0.021). The SUV_max of the primary tumour and metastases were both higher according to extent of metastatic disease (P = 0.001 and P < 0.001, respectively).

CONCLUSIONS: More skeletal metastases were detected with PSMA PET/CT than WBBS, resulting in a higher upstaging rate mostly in high-risk patients. The SUV_max of the primary tumour and metastases was associated with upstaging.

PMID:37943964 | DOI:10.1111/bju.16115

PLoS Comput Biol. 2023 Nov 9;19(11):e1011617. doi: 10.1371/journal.pcbi.1011617. Online ahead of print.

ABSTRACT

The islets of Langerhans are critical endocrine micro-organs that secrete hormones regulating energy metabolism in animals. Insulin and glucagon, secreted by beta and alpha cells, respectively, are responsible for metabolic switching between fat and glucose utilization. Dysfunction in their secretion and/or counter-regulatory influence leads to diabetes. Debate in the field centers on the cytoarchitecture of islets, as the signaling that governs hormonal secretion depends on structural and functional factors, including electrical connectivity, innervation, vascularization, and physical proximity. Much effort has therefore been devoted to elucidating which architectural features are significant for function and how derangements in these features are correlated or causative for dysfunction, especially using quantitative network science or graph theory characterizations. Here, we ask if there are non-local features in islet cytoarchitecture, going beyond standard network statistics, that are relevant to islet function. An example is ring structures, or cycles, of α and δ cells surrounding β cell clusters or the opposite, β cells surrounding α and δ cells. These could appear in two-dimensional islet section images if a sphere consisting of one cell type surrounds a cluster of another cell type. To address these issues, we developed two independent computational approaches, geometric and topological, for such characterizations. For the latter, we introduce an application of topological data analysis to determine locations of topological features that are biologically significant. We show that both approaches, applied to a large collection of islet sections, are in complete agreement in the context both of developmental and diabetes-related changes in islet characteristics. The topological approach can be applied to three-dimensional imaging data for islets as well.

PMID:37943957 | DOI:10.1371/journal.pcbi.1011617

Science. 2023 Nov 10;382(6671):eabo7201. doi: 10.1126/science.abo7201. Epub 2023 Nov 10.

ABSTRACT

We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. We discovered a noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>490 ligand-bound x-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2400 compounds) for this campaign were shared rapidly and openly, creating a rich, open, and intellectual property-free knowledge base for future anticoronavirus drug discovery.

PMID:37943932 | DOI:10.1126/science.abo7201

Science. 2023 Nov 10;382(6671):669-674. doi: 10.1126/science.adi6000. Epub 2023 Nov 9.

ABSTRACT

Prediction-powered inference is a framework for performing valid statistical inference when an experimental dataset is supplemented with predictions from a machine-learning system. The framework yields simple algorithms for computing provably valid confidence intervals for quantities such as means, quantiles, and linear and logistic regression coefficients without making any assumptions about the machine-learning algorithm that supplies the predictions. Furthermore, more accurate predictions translate to smaller confidence intervals. Prediction-powered inference could enable researchers to draw valid and more data-efficient conclusions using machine learning. The benefits of prediction-powered inference were demonstrated with datasets from proteomics, astronomy, genomics, remote sensing, census analysis, and ecology.

PMID:37943906 | DOI:10.1126/science.adi6000

PLoS One. 2023 Nov 9;18(11):e0294069. doi: 10.1371/journal.pone.0294069. eCollection 2023.

ABSTRACT

Numerous vital signs are reported in association with stress response assessment, but their application varies widely. This work provides an overview over methods for stress induction and strain assessment, and presents a multimodal experimental study to identify the most important vital signs for effective assessment of the response to acute mental stress. We induced acute mental stress in 65 healthy participants with the Mannheim Multicomponent Stress Test and acquired self-assessment measures (Likert scale, Self-Assessment Manikin), salivary α-amylase and cortisol concentrations as well as 60 vital signs from biosignals, such as heart rate variability parameters, QT variability parameters, skin conductance level, and breath rate. By means of statistical testing and a self-optimizing logistic regression, we identified the most important biosignal vital signs. Fifteen biosignal vital signs related to ventricular repolarization variability, blood pressure, skin conductance, and respiration showed significant results. The logistic regression converged with QT variability index, left ventricular work index, earlobe pulse arrival time, skin conductance level, rise time and number of skin conductance responses, breath rate, and breath rate variability (F1 = 0.82). Self-assessment measures indicated successful stress induction. α-amylase and cortisol showed effect sizes of -0.78 and 0.55, respectively. In summary, the hypothalamic-pituitary-adrenocortical axis and sympathetic nervous system were successfully activated. Our findings facilitate a coherent and integrative understanding of the assessment of the stress response and help to align applications and future research concerning acute mental stress.

PMID:37943894 | DOI:10.1371/journal.pone.0294069

PLoS One. 2023 Nov 9;18(11):e0293023. doi: 10.1371/journal.pone.0293023. eCollection 2023.

ABSTRACT

BACKGROUND: The clinical manifestations of Corona Virus Disease of 2019 (COVID-19) varied from patient to patient with evidence of multi-organ involvement. Many patients continue to have a wide range of symptoms for variable periods of time. The long-term effects of COVID-19 infection (post COVID-19 illness or syndrome) are not yet fully explored. This study aims to shed light on the clinical manifestations of the acute COVID-19 infection as well as post COVID-19 syndrome among the United Nations Relief and Works Agency for Palestine Refugee (UNRWA) staff in Jordan.

METHODS: A clinical case-series was conducted on a sample of COVID-19 positive employees of the UNRWA staff in Jordan. A structured questionnaire based mainly on World Health Organization (WHO) Case Report Form (CRF) verified tool for post COVID-19 was used. A sample of 366 out of a total of 1322 confirmed cases was systemically selected and included in the present study. Data were collected from UNRWA medical records and phone interviews. Data were analyzed using the Statistical Package for Social Sciences (SPSS) software.

RESULTS: The calculated Case Fatality Ratio was 0.7%. The incidence of COVID-19 among UNRWA staff in Jordan during the period of our study was 20.1%. A total of 366 respondents, 220 (60.1%) females and 146 (39.9%) males were included in the study. The mean (SD) age was 44.2 (8.0) years. Most of the infected (97.8%) developed acute COVID-19 symptoms. Fatigue, fever, joint pain, loss of smell and taste, and cough were the most common symptoms. According to WHO clinical classification of acute illness severity, 65.0% had mild illness. Only 28.7% of all subjects fully recovered from the infection, while most of them (71.3%) continued to suffer from many symptoms. Persistent fatigue (39.7%), shortness of breath (SOB) with activity (18.8%), anxiety (17.4%), forgetfulness (16.9%), trouble in concentrating (16.7%), and depressed mood (15.8%) were the most frequently reported.

CONCLUSION: Post COVID-19 illness was very common (71.3%) calling for UNRWA to continue assessment of post COVID-19 syndrome and the medical and psychological needs of affected staff. Despite vaccination, only 2.2% of the infected were asymptomatic. Reinfection was unusually high (24%).

PMID:37943893 | DOI:10.1371/journal.pone.0293023

PLoS One. 2023 Nov 9;18(11):e0289077. doi: 10.1371/journal.pone.0289077. eCollection 2023.

ABSTRACT

BACKGROUND: Physical activity (PA) is associated with various health benefits, especially in improving chronic health conditions. However, the metabolic changes in host metabolism in response to PA remain unclear, especially in racially/ethnically diverse populations.

OBJECTIVE: This study is to assess the metabolic profiles associated with the frequency of PA in White and African American (AA) men.

METHODS: Using the untargeted metabolomics data collected from 698 White and AA participants (mean age: 38.0±8.0, age range: 20-50) from the Louisiana Osteoporosis Study (LOS), we conducted linear regression models to examine metabolites that are associated with PA levels (assessed by self-reported regular exercise frequency levels: 0, 1-2, and ≥3 times per week) in White and AA men, respectively, as well as in the pooled sample. Covariates considered for statistical adjustments included race (only for the pooled sample), age, BMI, waist circumstance, smoking status, and alcohol drinking.

RESULTS: Of the 1133 untargeted compounds, we identified 7 metabolites associated with PA levels in the pooled sample after covariate adjustment with a false discovery rate of 0.15. Specifically, compared to participants who did not exercise, those who exercised at a frequency ≥3 times/week showed higher abundances in uracil, orotate, 1-(1-enyl-palmitoyl)-2-oleoyl-GPE (P-16:0/18:1) (GPE), threonate, and glycerate, but lower abundances in salicyluric glucuronide and adenine in the pooled sample. However, in Whites, salicyluric glucuronide and orotate were not significant. Adenine, GPE, and threonate were not significant in AAs. In addition, the seven metabolites were not significantly different between participants who exercised ≥3 times/week and 1-2 times/week, nor significantly different between participants with 1-2 times/week and 0/week in the pooled sample and respective White and AA groups.

CONCLUSIONS: Metabolite responses to PA are dose sensitive and may differ between White and AA populations. The identified metabolites may help advance our knowledge of guiding precision PA interventions. Studies with rigorous study designs are warranted to elucidate the relationship between PA and metabolites.

PMID:37943870 | DOI:10.1371/journal.pone.0289077

JMIR Res Protoc. 2023 Nov 9;12:e48432. doi: 10.2196/48432.

ABSTRACT

BACKGROUND: Disparities in cancer incidence, complex care needs, and poor health outcomes are largely driven by structural inequities stemming from social determinants of health. To date, no evidence-based clinical tool has been developed to identify newly diagnosed patients at risk of poorer outcomes. Specialist cancer nurses are well-positioned to ameliorate inequity of opportunity for optimal care, treatment, and outcomes through timely screening, assessment, and intervention. We designed a nursing complexity checklist (the “Checklist”) to support these activities, with the ultimate goal of improving equitable experiences and outcomes of care. This study aims to generate evidence regarding the clinical utility of the Checklist.

OBJECTIVE: The primary objectives of this study are to provide qualitative evidence regarding key aspects of the Checklist’s clinical utility (appropriateness, acceptability, and practicability), informed by Smart’s multidimensional model of clinical utility. Secondary objectives explore the predictive value of the Checklist and concordance between specific checklist items and patient-reported outcome measures.

METHODS: This prospective mixed methods case series study will recruit up to 60 newly diagnosed patients with cancer and 10 specialist nurses from a specialist cancer center. Nurses will complete the Checklist with patient participants. Within 2 weeks of Checklist completion, patients will complete 5 patient-reported outcome measures with established psychometric properties that correspond to specific checklist items and an individual semistructured interview to explore Checklist clinical utility. Interviews with nurses will occur 12 and 24 weeks after they first complete a checklist, exploring perceptions of the Checklist’s clinical utility including barriers and facilitators to implementation. Data describing planned and unplanned patient service use will be collected from patient follow-up interviews at 12 weeks and the electronic medical record at 24 weeks after Checklist completion. Descriptive statistics will summarize operational, checklist, and electronic medical record data. The predictive value of the Checklist and the relationship between specific checklist items and relevant patient-reported outcome measures will be examined using descriptive statistics, contingency tables, measures of association, and plots as appropriate. Qualitative data will be analyzed using a content analysis approach.

RESULTS: This study was approved by the institution’s ethics committee. The enrollment period commenced in May 2022 and ended in November 2022. In total, 37 patients with cancer and 7 specialist cancer nurses were recruited at this time. Data collection is scheduled for completion at the end of May 2023.

CONCLUSIONS: This study will evaluate key clinical utility dimensions of a nursing complexity checklist. It will also provide preliminary evidence on its predictive value and information to support its seamless implementation into everyday practice including, but not limited to, possible revisions to the Checklist, instructions, and training for relevant personnel. Future implementation of this Checklist may improve equity of opportunity of access to care for patients with cancer.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48432.

PMID:37943601 | DOI:10.2196/48432