Conectar
To examine the long-term effects of COVID-19 on surgical nurses.
Individuals contaminated with COVID-19 may face several metabolic or psychological issues, primarily in the respiratory, cardiovascular, nervous, musculoskeletal and renal systems during the late period. However, the long-term epidemiology is still not clear.
Descriptive cross-sectional study.
The study included nurses (n = 509) who had been diagnosed with COVID-19 at least 12 weeks before and worked in surgical departments. We collected the study data via an online survey using the snowball sampling method between December 2021 and May 2022. This study followed the Reporting of Observational Studies in Epidemiology Guideline.
The mean age of the nurses was 31.66 ± 8.74 years. Nurses stated that they were diagnosed with COVID-19 approximately 36 weeks before participating in this study. We found that the nurses mostly experienced palpitation (83.5%), headache (73.5%), dyspnea (64.1%), anosmia (57.6%), arthralgia (55.7%) and burnout (58.4%) during the late period after COVID-19.
The long-term effects of COVID-19 were related to multiple organ dysfunctions.
Since the study was conducted with healthy individuals who had previously experienced COVID-19, there is no patient contribution.
This study focuses on the long-term effects of COVID-19 on nurses. The results support the long-term effects of COVID-19 and are thought to contribute to the literature.
To identify the reasons and/or risk factors for hospital admission and/or emergency department attendance for older (≥60 years) residents of long-term care facilities.
Older adults' use of acute services is associated with significant financial and social costs. A global understanding of the reasons for the use of acute services may allow for early identification and intervention, avoid clinical deterioration, reduce the demand for health services and improve quality of life.
Systematic review registered in PROSPERO (CRD42022326964) and reported following PRISMA guidelines.
The search strategy was developed in consultation with an academic librarian. The strategy used MeSH terms and relevant keywords. Articles published since 2017 in English were eligible for inclusion. CINAHL, MEDLINE, Scopus and Web of Science Core Collection were searched (11/08/22). Title, abstract, and full texts were screened against the inclusion/exclusion criteria; data extraction was performed two blinded reviewers. Quality of evidence was assessed using the NewCastle Ottawa Scale (NOS).
Thirty-nine articles were eligible and included in this review; included research was assessed as high-quality with a low risk of bias. Hospital admission was reported as most likely to occur during the first year of residence in long-term care. Respiratory and cardiovascular diagnoses were frequently associated with acute services use. Frailty, hypotensive medications, falls and inadequate nutrition were associated with unplanned service use.
Modifiable risks have been identified that may act as a trigger for assessment and be amenable to early intervention. Coordinated intervention may have significant individual, social and economic benefits.
This review has identified several modifiable reasons for acute service use by older adults. Early and coordinated intervention may reduce the risk of hospital admission and/or emergency department.
This systematic review was conducted and reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology.
No patient or public contribution.
To evaluate factors associated with fall protection motivation to engage in fall preventive behaviour among rural community-dwelling older adults aged 55 and above using the protection motivation theory scale.
A cross-sectional study.
The study was conducted in a healthcare clinic in Malaysia, using multistage random sampling from November 2021 to January 2022. Three hundred seventy-five older adults aged 55 and older were included in the final analysis. There were 31 items in the final PMT scale. The analysis was performed within the whole population and grouped into ‘faller’ and ‘non-faller’, employing IBM SPSS version 26.0 for descriptive, independent t-test, chi-square, bivariate correlation and linear regressions.
A total of 375 older participants were included in the study. Fallers (n = 82) and non-fallers (n = 293) show statistically significant differences in the characteristics of ethnicity, assistive device users, self-rating of intention and participation in previous fall prevention programmes. The multiple linear regression model revealed fear, coping appraisal and an interaction effect of fear with coping appraisal predicting fall protection motivation among older adults in rural communities.
Findings from this study demonstrated that coping appraisal and fear predict the protection motivation of older adults in rural communities. Older adults without a history of falls and attaining higher education had better responses in coping appraisal, contributing to a reduction in perceived rewards and improving protection motivation. Conversely, older adults from lower education backgrounds tend to have higher non-preventive behaviours, leading to a decline in fall protection motivation.
These results contribute important information to nurses working with older adults with inadequate health literacy in rural communities, especially when planning and designing fall prevention interventions. The findings would benefit all nurses, healthcare providers, researchers and academicians who provide care for older adults.
Participants were briefed about the study, and their consent was obtained. They were only required to answer the questionnaire through interviews. Older individuals aged fifty-five and above in rural communities at the healthcare clinic who could read, write or understand Malay or English were included. Those who were suffering from mental health problems and refused to participate in the study were excluded from the study. Their personal information remained classified and not recorded in the database during the data entry or analysis.
Early identification and intervention of the frailty of the elderly will help lighten the burden of social medical care and improve the quality of life of the elderly. Therefore, we used machine learning (ML) algorithm to develop models to predict frailty risk in the elderly.
A prospective cohort study.
We collected data on 6997 elderly people from Chinese Longitudinal Healthy Longevity Study wave 6–7 surveys (2011–2012, 2014). After the baseline survey in 1998 (wave 1), the project conducted follow-up surveys (wave 2–8) in 2000–2018. The osteoporotic fractures index was used to assess frailty. Four ML algorithms (random forest [RF], support vector machine, XGBoost and logistic regression [LR]) were used to develop models to identify the risk factors of frailty and predict the risk of frailty. Different ML models were used for the prediction of frailty risk in the elderly and frailty risk was trained on a cohort of 4385 elderly people with frailty (split into a training cohort [75%] and internal validation cohort [25%]). The best-performing model for each study outcome was tested in an external validation cohort of 6997 elderly people with frailty pooled from the surveys (wave 6–7). Model performance was assessed by receiver operating curve and F2-score.
Among the four ML models, the F2-score values were similar (0.91 vs. 0.91 vs. 0.88 vs. 0.90), and the area under the curve (AUC) values of RF model was the highest (0.75), followed by LR model (0.74). In the final two models, the AUC values of RF and LR model were similar (0.77 vs. 0.76) and their accuracy was identical (87.4% vs. 87.4%).
Our study developed a preliminary prediction model based on two different ML approaches to help predict frailty risk in the elderly.
The presented models from this study can be used to inform healthcare providers to predict the frailty probability among older adults and maybe help guide the development of effective frailty risk management interventions.
Detecting frailty at an early stage and implementing timely targeted interventions may help to improve the allocation of health care resources and to reduce frailty-related burden. Identifying risk factors for frailty could be beneficial to provide tailored and personalized care intervention for older adults to more accurately prevent or improve their frail conditions so as to improve their quality of life.
The study has adhered to STROBE guidelines.
No patient or public contribution.
On 6 April 2022, the UK government implemented mandatory kilocalorie (kcal) labelling regulations for food and drink products sold in the out-of-home food sector (OHFS) in England. Previous assessments of kcal labelling practices in the UK OHFS found a low prevalence of voluntary implementation and poor compliance with labelling recommendations. This study aimed to examine changes in labelling practices preimplementation versus post implementation of mandatory labelling regulations in 2022.
In August–December 2021 (preimplementation) and August–November 2022 (post implementation), large OHFS businesses (250 or more employees) subject to labelling regulations were visited. At two time points, a researcher visited the same 117 food outlets (belonging to 90 unique businesses) across four local authorities in England. Outlets were rated for compliance with government regulations for whether kcal labelling was provided at any or all point of choice, provided for all eligible food and drink items, provided per portion for sharing items, if labelling was clear and legible and if kcal reference information was displayed.
There was a significant increase (21% preimplementation vs 80% post implementation, OR=40.98 (95% CI 8.08 to 207.74), p
The number of large businesses in the OHFS providing kcal labelling increased following the implementation of mandatory labelling regulations. However, around one-fifth of eligible outlets sampled were not providing kcal labelling 4–8 months after the regulations came into force, and the majority of businesses only partially complied with government guidance. More effective enforcement may be required to further improve kcal labelling practices in the OHFS in England.
Study protocol and analysis strategy preregistered on Open Science Framework (https://osf.io/pfnm6/).
To estimate all-cause mortality in ventilator-associated pneumonia (VAP) and determine whether antibiotic duration beyond 8 days is associated with reduction in all-cause mortality in patients admitted with VAP in the intensive care unit.
A prospective cohort study of patients diagnosed with VAP based on the National Healthcare Safety Network definition and clinical criteria.
Single tertiary care hospital in Southern India.
100 consecutive adult patients diagnosed with VAP were followed up for 28 days postdiagnosis or until discharge.
The incidence of mortality at 28 days postdiagnosis was measured. Tests for association and predictors of mortality were determined using 2 test and multivariate Cox regression analysis. Secondary outcomes included baseline clinical parameters such as age, underlying comorbidities as well as measuring total length of stay, number of ventilator-free days and antibiotic-free days.
The overall case fatality rate due to VAP was 46%. There was no statistically significant difference in mortality rates between those receiving shorter antibiotic duration (5–8 days) and those on longer therapy. Among those who survived until day 9, the observed risk difference was 15.1% between both groups, with an HR of 1.057 (95% CI 0.26 to 4.28). In 70.4% of isolates, non-fermenting Gram-negative bacilli were identified, of which the most common pathogen isolated was Acinetobacter baumannii (62%).
In this hospital-based cohort study, there is insufficient evidence to suggest that prolonging antibiotic duration beyond 8 days in patients with VAP improves survival.
Conflicting evidence for the association between COVID-19 and adverse perinatal outcomes exists. This study examined the associations between maternal COVID-19 during pregnancy and adverse perinatal outcomes including preterm birth (PTB), low birth weight (LBW), small-for-gestational age (SGA), large-for-gestational age (LGA) and fetal death; as well as whether the associations differ by trimester of infection.
The study used a retrospective Mexican birth cohort from the Instituto Mexicano del Seguro Social (IMSS), Mexico, between January 2020 and November 2021.
We used the social security administrative dataset from IMSS that had COVID-19 information and linked it with the IMSS routine hospitalisation dataset, to identify deliveries in the study period with a test for SARS-CoV-2 during pregnancy.
PTB, LBW, SGA, LGA and fetal death. We used targeted maximum likelihood estimators, to quantify associations (risk ratio, RR) and CIs. We fit models for the overall COVID-19 sample, and separately for those with mild or severe disease, and by trimester of infection. Additionally, we investigated potential bias induced by missing non-tested pregnancies.
The overall sample comprised 17 340 singleton pregnancies, of which 30% tested positive. We found that those with mild COVID-19 had an RR of 0.89 (95% CI 0.80 to 0.99) for PTB and those with severe COVID-19 had an RR of 1.53 (95% CI 1.07 to 2.19) for LGA. COVID-19 in the first trimester was associated with fetal death, RR=2.36 (95% CI 1.04, 5.36). Results also demonstrate that missing non-tested pregnancies might induce bias in the associations.
In the overall sample, there was no evidence of an association between COVID-19 and adverse perinatal outcomes. However, the findings suggest that severe COVID-19 may increase the risk of some perinatal outcomes, with the first trimester potentially being a high-risk period.
This study aimed to assess the administration technique of eye medications, its determinants and disposal practices among ophthalmic outpatients.
An analytical cross-sectional study was conducted.
Brhan Aini Ophthalmic National Referral Hospital in Asmara, Eritrea.
Samples of ophthalmic outpatients aged
Data were collected from August 2021 to September 2021, using an interview-based questionnaire. The collected data were entered and analysed using CSPro (V.7.3) and SPSS (V.26), respectively. Descriptive statistics and independent samples t-test were performed. P-values less than 0.05 were considered as significant.
A total of 333 respondents with a mean age of 56.4 (SD: 18.76) years were recruited in the study. More than half of the respondents (57.4%) did not have any information on the time interval between two successive eye medications. However, only 16.5% of the respondents managed to close their tear ducts after the administration of eye medication. The mean (SD) score for proper administration of eye medication was 4.16 (1.07) out of 7.0. Female sex (p=0.002), the absence of glaucoma (p=0.035) and the presence of cataract (p=0.014) were significant determinants of the proper administration technique of eye medication. The most favoured disposal practice for unused and/or expired eye medications was disposing of regular garbage (79.9%).
This research revealed that there was an inappropriate administration technique and disposal practices of eye medications among ophthalmic outpatients. This requires immediate attention from policy-makers, programme managers and healthcare professionals to ensure the appropriate use of eye medications by the patients.
This study aims to evaluate the prevalence and associated factors of lower back pain (LBP) among farmers, rickshaw pullers and office workers in Bangladesh, while also investigating potential contributors within these occupational groups.
This cross-sectional study aimed to determine the prevalence of LBP, associated factors and management procedures among farmers, rickshaw pullers and office workers in Bangladesh through face-to-face interviews.
The study was conducted in different parts of the Bogura district in Bangladesh.
A total of 396 participants were included in the final analysis, all the participants were men and adult in age.
Data were collected using a semi-structured questionnaire based on previous research. Bivariate and multivariable logistic regression analyses were performed to identify significant associations.
The prevalence of LBP was found to be 42.7% among the participants. Farmers and rickshaw pullers had approximately four-times and three-times higher odds of experiencing LBP compared with office workers. Other significant factors associated with LBP included living in a nuclear family, having a normal body weight, lacking professional training, having a chronic disease, having a family history of LBP and experiencing numbness in the legs or feet. The majority of respondents sought medical attention and took medication for their LBP.
The study underscores occupational differences in LBP prevalence, emphasising the necessity for tailored interventions and occupational health policies. Identifying specific risk factors and management practices in these professions can aid in developing effective prevention strategies and enhancing healthcare services.
To examine family planning through the community’s perception, belief system and cultural impact; in addition to identifying the determining factors for family planning uptake.
A descriptive exploratory study.
Three communities were selected from three local government areas, each in the three senatorial districts in Ekiti State.
The study was conducted among young unmarried women in the reproductive age group who were sexually active as well as married men and women in the reproductive age group who are currently living with their partners and were sexually active.
Eight focus group discussions were conducted in the community in 2019 with 28 male and 50 female participants. The audio recordings were transcribed, triangulated with notes and analysed using QSR NVivo V.8 software. Community perception, beliefs and perceptions of the utility of family planning, as well as cultural, religious and other factors determining family planning uptake were analysed.
The majority of the participants had the perception that family planning helps married couple only. There were diverse beliefs about family planning and mixed reactions with respect to the impact of culture and religion on family planning uptake. Furthermore, a number of factors were identified in determining family planning uptake—intrapersonal, interpersonal and health system factors.
The study concluded that there are varied reactions to family planning uptake due to varied perception, cultural and religious beliefs and determining factors. It was recommended that more targeted male partner engagement in campaign would boost family planning uptake.
To compare the characteristics of patients with type 2 diabetes mellitus in general practice and those included in randomised controlled trials on which clinical practice guidelines are based.
Cross-sectional comparative study.
We asked 45 general practitioners from three French Departments to identify the 15 patients with type 2 diabetes mellitus they most recently saw in consultation. In parallel, we selected randomised controlled trials included in the Cochrane systematic review on which the clinical practice guidelines for type 2 diabetes mellitus were based.
We included 675 patients with type 2 diabetes mellitus, and data were collected from 23 randomised controlled trials, corresponding to 36 059 patients.
Characteristics of general-practice patients were extracted from medical records by a unique observer. The same baseline characteristics of patients included in randomised controlled trials from the Cochrane systematic review were extracted and meta-analysed. We assessed standardised differences between these two series of baseline characteristics. A difference greater than 0.10 in absolute value was considered meaningful.
General-practice patients were older than randomised controlled trial patients (mean (SD) 68.8 (1.1) vs 59.9 years (standardised difference 0.8)) and had a higher body mass index (mean (SD) 31.5 (6.9) vs 28.2 kg/m2 (standardised difference 0.5)) but smoked less (11.0% vs 29.3% (standardised difference –0.6)). They more frequently used antihypertensive drugs (82.1% vs 37.5% (standardised difference 1.2)) but less frequently had a myocardial infarction (7.6% vs 23.1% (standardised difference –1.1)).
Patients with type 2 diabetes mellitus cared for in general practice differ in a number of important aspects from patients included in randomised controlled trials on which clinical practice guidelines are based. This situation hampers the applicability of these guidelines. Future randomised trials should include patients who better fit the ‘average’ general-practice patient with type 2 diabetes mellitus to help improve the translation of study findings in daily practice.
by Viktor Laskov, David Rothbauer, Hana Malikova
Radiomic features are usually used to predict target variables such as the absence or presence of a disease, treatment response, or time to symptom progression. One of the potential clinical applications is in patients with Parkinson’s disease. Robust radiomic features for this specific imaging method have not yet been identified, which is necessary for proper feature selection. Thus, we are assessing the robustness of radiomic features in dopamine transporter imaging (DaT). For this study, we made an anthropomorphic head phantom with tissue heterogeneity using a personal 3D printer (polylactide 82% infill); the bone was subsequently reproduced with plaster. A surgical cotton ball with radiotracer (123I-ioflupane) was inserted. Scans were performed on the two-detector hybrid camera with acquisition parameters corresponding to international guidelines for DaT single photon emission tomography (SPECT). Reconstruction of SPECT was performed on a clinical workstation with iterative algorithms. Open-source LifeX software was used to extract 134 radiomic features. Statistical analysis was made in RStudio using the intraclass correlation coefficient (ICC) and coefficient of variation (COV). Overall, radiomic features in different reconstruction parameters showed a moderate reproducibility rate (ICC = 0.636, p 0.9, pby Alessandra De Giani, Federica Perillo, Alberto Baeri, Margherita Finazzi, Federica Facciotti, Patrizia Di Gennaro
The human gut is a complex environment where the microbiota and its metabolites play a crucial role in the maintenance of a healthy state. The aim of the present work is the reconstruction of a new in vitro minimal human gut microbiota resembling the microbe-microbe networking comprising the principal phyla (Bacillota, Bacteroidota, Pseudomonadota, and Actinomycetota), to comprehend the intestinal ecosystem complexity. In the reductionist model, we mimicked the administration of Maitake extract as prebiotic and a probiotic formulation (three strains belonging to Lactobacillus and Bifidobacterium genera), evaluating the modulation of strain levels, the release of beneficial metabolites, and their health-promoting effects on human cell lines of the intestinal environment. The administration of Maitake and the selected probiotic strains generated a positive modulation of the in vitro bacterial community by qPCR analyses, evidencing the prominence of beneficial strains (Lactiplantibacillus plantarum and Bifidobacterium animalis subsp. lactis) after 48 hours. The bacterial community growths were associated with the production of metabolites over time through GC-MSD analyses such as lactate, butyrate, and propionate. Their effects on the host were evaluated on cell lines of the intestinal epithelium and the immune system, evidencing positive antioxidant (upregulation of SOD1 and NQO1 genes in HT-29 cell line) and anti-inflammatory effects (production of IL-10 from all the PBMCs). Therefore, the results highlighted a positive modulation induced by the synergic activities of probiotics and Maitake, inducing a tolerogenic microenvironment.by Gabriel Ott, Yannik Schaubelt, Juan Miguel Lopez Alcaraz, Wilhelm Haverkamp, Nils Strodthoff
Cardiovascular diseases remain the leading global cause of mortality. Age is an important covariate whose effect is most easily investigated in a healthy cohort to properly distinguish the former from disease-related changes. Traditionally, most of such insights have been drawn from the analysis of electrocardiogram (ECG) feature changes in individuals as they age. However, these features, while informative, may potentially obscure underlying data relationships. In this paper we present the following contributions: (1) We employ a deep-learning model and a tree-based model to analyze ECG data from a robust dataset of healthy individuals across varying ages in both raw signals and ECG feature format. (2) We use explainable AI methods to identify the most discriminative ECG features across age groups.(3) Our analysis with tree-based classifiers reveals age-related declines in inferred breathing rates and identifies notably high SDANN values as indicative of elderly individuals, distinguishing them from younger adults. (4) Furthermore, the deep-learning model underscores the pivotal role of the P-wave in age predictions across all age groups, suggesting potential changes in the distribution of different P-wave types with age. These findings shed new light on age-related ECG changes, offering insights that transcend traditional feature-based approaches.by Longgang Zhao, Yuan Wang, Eric Mishio Bawa, Zichun Meng, Jingkai Wei, Sarah Newman-Norlund, Tushar Trivedi, Hatice Hasturk, Roger D. Newman-Norlund, Julius Fridriksson, Anwar T. Merchant
BackgroundCognitive impairment has multiple risk factors spanning several domains, but few studies have evaluated risk factor clusters. We aimed to identify naturally occurring clusters of risk factors of poor cognition among middle-aged and older adults and evaluate associations between measures of cognition and these risk factor clusters.
MethodsWe used data from the National Health and Nutrition Examination Survey (NHANES) III (training dataset, n = 4074) and the NHANES 2011–2014 (validation dataset, n = 2510). Risk factors were selected based on the literature. We used both traditional logistic models and support vector machine methods to construct a composite score of risk factor clusters. We evaluated associations between the risk score and cognitive performance using the logistic model by estimating odds ratios (OR) and 95% confidence intervals (CI).
ResultsUsing the training dataset, we developed a composite risk score that predicted undiagnosed cognitive decline based on ten selected predictive risk factors including age, waist circumference, healthy eating index, race, education, income, physical activity, diabetes, hypercholesterolemia, and annual visit to dentist. The risk score was significantly associated with poor cognitive performance both in the training dataset (OR Tertile 3 verse tertile 1 = 8.15, 95% CI: 5.36–12.4) and validation dataset (OR Tertile 3 verse tertile 1 = 4.31, 95% CI: 2.62–7.08). The area under the receiver operating characteristics curve for the predictive model was 0.74 and 0.77 for crude model and model adjusted for age, sex, and race.
ConclusionThe model based on selected risk factors may be used to identify high risk individuals with cognitive impairment.
by Leslie Hayden, James M. Lightner, Stacy Strausborger, Teri J. Franks, Nora L. Watson, Michael R. Lewin-Smith
The role that inhaled particulate matter plays in the development of post-deployment lung disease among US service members deployed to Southwest Asia during the Global War on Terrorism has been difficult to define. There is a persistent gap in data addressing the relationship between relatively short-term (months to a few years) exposures to high levels of particulate matter during deployment and the subsequent development of adverse pulmonary outcomes. Surgical lung biopsies from deployed service members and veterans (DSMs) and non-deployed service members and veterans (NDSMs) who develop lung diseases can be analyzed to potentially identify residual deployment-specific particles and develop associations with pulmonary pathological diagnoses. We examined 52 surgical lung biopsies from 25 DSMs and 27 NDSMs using field emission scanning electron microscopy (FE-SEM) with energy dispersive x-ray spectroscopy (EDS) to identify any between-group differences in the number and composition of retained inorganic particles, then compared the particle analysis results with the original histopathologic diagnoses. We recorded a higher number of total particles in biopsies from DSMs than from NDSMs, and this difference was mainly accounted for by geologic clays (illite, kaolinite), feldspars, quartz/silica, and titanium-rich silicate mixtures. Biopsies from DSMs deployed to other Southwest Asia regions (SWA-Other) had higher particle counts than those from DSMs primarily deployed to Iraq or Afghanistan, due mainly to illite. Distinct deployment-specific particles were not identified. Particles did not qualitatively associate with country of deployment. The individual diagnoses of the DSMs and NDSMs were not associated with elevated levels of total particles, metals, cerium oxide, or titanium dioxide particles. These results support the examination of particle-related lung disease in DSMs in the context of comparison groups, such as NDSMs, to assist in determining the strength of associations between specific pulmonary pathology diagnoses and deployment-specific inorganic particulate matter exposure.by Juan F. Zapata-Acevedo, Mónica Losada-Barragán, Johann F. Osma, Juan C. Cruz, Andreas Reiber, Klaus G. Petry, Amael Caillard, Audrey Sauldubois, Daniel Llamosa Pérez, Aníbal José Morillo Zárate, Sonia Bermúdez Muñoz, Agustín Daza Moreno, Rafaela V. Silva, Carmen Infante-Duarte, William Chamorro-Coral, Rodrigo E. González-Reyes, Karina Vargas-Sánchez
Chronic neuroinflammation is characterized by increased blood-brain barrier (BBB) permeability, leading to molecular changes in the central nervous system that can be explored with biomarkers of active neuroinflammatory processes. Magnetic resonance imaging (MRI) has contributed to detecting lesions and permeability of the BBB. Ultra-small superparamagnetic particles of iron oxide (USPIO) are used as contrast agents to improve MRI observations. Therefore, we validate the interaction of peptide-88 with laminin, vectorized on USPIO, to explore BBB molecular alterations occurring during neuroinflammation as a potential tool for use in MRI. The specific labeling of NPS-P88 was verified in endothelial cells (hCMEC/D3) and astrocytes (T98G) under inflammation induced by interleukin 1β (IL-1β) for 3 and 24 hours. IL-1β for 3 hours in hCMEC/D3 cells increased their co-localization with NPS-P88, compared with controls. At 24 hours, no significant differences were observed between groups. In T98G cells, NPS-P88 showed similar nonspecific labeling among treatments. These results indicate that NPS-P88 has a higher affinity towards brain endothelial cells than astrocytes under inflammation. This affinity decreases over time with reduced laminin expression. In vivo results suggest that following a 30-minute post-injection, there is an increased presence of NPS-P88 in the blood and brain, diminishing over time. Lastly, EAE animals displayed a significant accumulation of NPS-P88 in MRI, primarily in the cortex, attributed to inflammation and disruption of the BBB. Altogether, these results revealed NPS-P88 as a biomarker to evaluate changes in the BBB due to neuroinflammation by MRI in biological models targeting laminin.by Levi Matthies, Hendrik Amir-Kabirian, Medhanie T. Gebrekidan, Andreas S. Braeuer, Ulrike S. Speth, Ralf Smeets, Christian Hagel, Martin Gosau, Christian Knipfer, Reinhard E. Friedrich
In Neurofibromatosis type 1 (NF1), peripheral nerve sheaths tumors are common, with cutaneous neurofibromas resulting in significant aesthetic, painful and functional problems requiring surgical removal. To date, determination of adequate surgical resection margins–complete tumor removal while attempting to preserve viable tissue–remains largely subjective. Thus, residual tumor extension beyond surgical margins or recurrence of the disease may frequently be observed. Here, we introduce Shifted-Excitation Raman Spectroscopy in combination with deep neural networks for the future perspective of objective, real-time diagnosis, and guided surgical ablation. The obtained results are validated through established histological methods. In this study, we evaluated the discrimination between cutaneous neurofibroma (n = 9) and adjacent physiological tissues (n = 25) in 34 surgical pathological specimens ex vivo at a total of 82 distinct measurement loci. Based on a convolutional neural network (U-Net), the mean raw Raman spectra (n = 8,200) were processed and refined, and afterwards the spectral peaks were assigned to their respective molecular origin. Principal component and linear discriminant analysis was used to discriminate cutaneous neurofibromas from physiological tissues with a sensitivity of 100%, specificity of 97.3%, and overall classification accuracy of 97.6%. The results enable the presented optical, non-invasive technique in combination with artificial intelligence as a promising candidate to ameliorate both, diagnosis and treatment of patients affected by cutaneous neurofibroma and NF1.
FreshRSS 