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Analytical Chemistry Oct 2023A "chemical linearization" approach was applied to synthetic peptide macrocycles to enable their de novo sequencing from mixtures using nanoliquid chromatography-tandem...
A "chemical linearization" approach was applied to synthetic peptide macrocycles to enable their de novo sequencing from mixtures using nanoliquid chromatography-tandem mass spectrometry (nLC-MS/MS). This approach─previously applied to individual macrocycles but not to mixtures─involves cleavage of the peptide backbone at a defined position to give a product capable of generating sequence-determining fragment ions. Here, we first established the compatibility of "chemical linearization" by Edman degradation with a prominent macrocycle scaffold based on -Cys peptides cross-linked with the -xylene linker, which are of major significance in therapeutics discovery. Then, using macrocycle libraries of known sequence composition, the ability to recover accurate de novo assignments to linearized products was critically tested using performance metrics unique to mixtures. Significantly, we show that linearized macrocycles can be sequenced with lower recall compared to linear peptides but with similar accuracy, which establishes the potential of using "chemical linearization" with synthetic libraries and selection procedures that yield compound mixtures. Sodiated precursor ions were identified as a significant source of high-scoring but inaccurate assignments, with potential implications for improving automated de novo sequencing more generally.
PubMed: 37724843
DOI: 10.1021/acs.analchem.3c01742 -
Foods (Basel, Switzerland) May 2024This paper presents the first assessment of dietary exposure to aflatoxin M1 (AFM1) and associated health risks through milk and dairy product consumption in Armenia....
This paper presents the first assessment of dietary exposure to aflatoxin M1 (AFM1) and associated health risks through milk and dairy product consumption in Armenia. Data on AFM1 in raw milk were obtained from an annual residue monitoring program. Additionally, commonly consumed dairy products (pasteurized milk, cheese, sour cream, curd cheese) were sampled, considering the sources of raw milk used by dairy companies. Per capita consumption of raw milk was sourced from national food balance databases, while individual consumption data for dairy products was collected via a 24 h recall survey with 1400 adult respondents. Detectable levels of AFM1 were observed in 7.14% of raw milk samples (up to 0.334 μg/kg) and, albeit at lower amounts (up to 0.009 µg/kg), in 30% and 40% of sour cream and curd cheese, respectively. The AFM1 levels were lower than the national maximum permitted level (0.5 μg/kg); however, levels in raw milk exceeded the EU ML (0.05 μg/kg). The estimated margin of exposure values for dairy products indicated no significant risk, whereas a reasonable worst-case estimate, using the measurable levels of AFM1 in raw milk consumption indicated a potential public health concern. This study provides a scientific basis for evaluating aflatoxin issues in the Caucasus area.
PubMed: 38790817
DOI: 10.3390/foods13101518 -
BMC Public Health Jul 2023This report describes two L. monocytogenes outbreak investigations that occurred in March and September of 2018 and that linked illness to a food premises located in an...
BACKGROUND
This report describes two L. monocytogenes outbreak investigations that occurred in March and September of 2018 and that linked illness to a food premises located in an Ontario cancer centre. The cancer centre serves patients from across the province.
METHODS
In Ontario, local public health agencies follow up with all reported laboratory-confirmed cases of listeriosis to identify possible sources of disease acquisition and to carry out investigations, including at suspected food premises. The Canadian Food Inspection Agency (CFIA) is notified of any Listeria-positive food product collected in relation to a case. The CFIA traces Listeria-positive product through the food distribution system to identify the contamination source and ensure the implicated manufacturing facility implements corrective measures.
RESULTS
Outbreaks one and two each involved three outbreak-confirmed listeriosis cases. All six cases were considered genetically related by whole genome sequencing (WGS). In both outbreaks, outbreak-confirmed cases reported consuming meals at a food premises located in a cancer centre (food premises A) before illness onset. Various open deli meat samples and, in outbreak two, environmental swabs (primarily from the meat slicer) collected from food premises A were genetically related to the outbreak-confirmed cases. Food premises A closed as a result of the investigations.
CONCLUSIONS
When procuring on-site food premises, healthcare facilities and institutions serving individuals with immuno-compromising conditions should consider the potential health risk of foods available to their patient population.
Topics: Humans; Listeria monocytogenes; Foodborne Diseases; Food Microbiology; Neoplasms; Listeriosis; Disease Outbreaks; Ontario
PubMed: 37507665
DOI: 10.1186/s12889-023-16371-7 -
International Journal of Applied Earth... Sep 2023Geospatial datasets derived from remote sensing data by means of machine learning methods are often based on probabilistic outputs of abstract nature, which are...
Geospatial datasets derived from remote sensing data by means of machine learning methods are often based on probabilistic outputs of abstract nature, which are difficult to translate into interpretable measures. For example, the Global Human Settlement Layer GHS-BUILT-S2 product reports the probability of the presence of built-up areas in 2018 in a global 10 m × 10 m grid. However, practitioners typically require interpretable measures such as binary surfaces indicating the presence or absence of built-up areas or estimates of sub-pixel built-up surface fractions. Herein, we assess the relationship between the built-up probability in GHS-BUILT-S2 and reference built-up surface fractions derived from a highly reliable reference database for several regions in the United States. Furthermore, we identify a binarization threshold using an agreement maximization method that creates binary built-up land data from these built-up probabilities. These binary surfaces are input to a spatially explicit, scale-sensitive accuracy assessment which includes the use of a novel, visual-analytical tool which we call focal precision-recall signature plots. Our analysis reveals that a threshold of 0.5 applied to GHS-BUILT-S2 maximizes the agreement with binarized built-up land data derived from the reference built-up area fraction. We find high levels of accuracy (i.e., county-level F-1 scores of almost 0.8 on average) in the derived built-up areas, and consistently high accuracy along the rural-urban gradient in our study area. These results reveal considerable accuracy improvements in human settlement models based on Sentinel-2 data and deep learning, as compared to earlier, Landsat-based versions of the Global Human Settlement Layer.
PubMed: 37975073
DOI: 10.1016/j.jag.2023.103469 -
Frontiers in Aging Neuroscience 2024The lack of cognitive awareness, anosognosia, is a clinical deficit in Alzheimer's disease (AD) dementia. However, an increased awareness of cognitive function,...
INTRODUCTION
The lack of cognitive awareness, anosognosia, is a clinical deficit in Alzheimer's disease (AD) dementia. However, an increased awareness of cognitive function, hypernosognosia, may serve as a marker in the preclinical stage. Subjective cognitive decline (SCD) might correspond to the initial symptom in the dynamic trajectory of awareness, but SCD might be absent along with low awareness of actual cognitive performance in the preclinical stage. We hypothesized that distinct meta-cognitive profiles, both hypernosognosia and anosognosia, might be identified in preclinical-AD. This research evaluated the association between cerebrospinal fluid (CSF) AD biomarkers and the awareness of episodic memory, further exploring dyadic (participant-partner) SCD reports, in the preclinical Alzheimer's continuum.
METHODS
We analyzed 314 cognitively unimpaired (CU) middle-aged individuals (mean age: 60, SD: 4) from the ALFA+ cohort study. Episodic memory was evaluated with the delayed recall from the Memory Binding Test (MBT). Awareness of episodic memory, meta-memory, was defined as the normalized discrepancy between objective and subjective performance. SCD was defined using self-report, and dyadic SCD profiles incorporated the study partner's report using parallel SCD-Questionnaires. The relationship between CSF Aβ42/40 and CSF p-tau181 with meta-memory was evaluated with multivariable regression models. The role of SCD and the dyadic contingency was explored with the corresponding stratified analysis.
RESULTS
CSF Aβ42/40 was non-linearly associated with meta-memory, showing an increased awareness up to Aβ-positivity and a decreased awareness beyond this threshold. In the non-SCD subset, the non-linear association between CSF Aβ42/40 and meta-memory persisted. In the SCD subset, higher Aβ-pathology was linearly associated with increased awareness. Individuals presenting only study partner's SCD, defined as unaware decliners, exhibited higher levels of CSF p-tau181 correlated with lower meta-memory performance.
DISCUSSION
These results suggested that distinct meta-cognitive profiles can be identified in preclinical-AD. While most individuals might experience an increased awareness associated with the entrance in the AD continuum, hypernosognosia, some might be already losing insight and stepping into the anosognosic trajectory. This research reinforced that an early anosognosic profile, although at increased risk of AD-related decline, might be currently overlooked considering actual diagnostic criteria, and therefore its medical attention delayed.
PubMed: 38872632
DOI: 10.3389/fnagi.2024.1394460 -
Frontiers in Neurology 2024Upwards of 50% of acute ischemic stroke (AIS) survivors endure varying degrees of disability, with a recurrence rate of 17.7%. Thus, the prediction of outcomes in AIS...
BACKGROUND AND OBJECTIVES
Upwards of 50% of acute ischemic stroke (AIS) survivors endure varying degrees of disability, with a recurrence rate of 17.7%. Thus, the prediction of outcomes in AIS may be useful for treatment decisions. This study aimed to determine the applicability of a machine learning approach for forecasting early outcomes in AIS patients.
METHODS
A total of 659 patients with new-onset AIS admitted to the Department of Neurology of both the First and Second Affiliated Hospitals of Bengbu Medical University from January 2020 to October 2022 included in the study. The patient' demographic information, medical history, Trial of Org 10,172 in Acute Stroke Treatment (TOAST), National Institute of Health Stroke Scale (NIHSS) and laboratory indicators at 24 h of admission data were collected. The Modified Rankine Scale (mRS) was used to assess the 3-mouth outcome of participants' prognosis. We constructed nine machine learning models based on 18 parameters and compared their accuracies for outcome variables.
RESULTS
Feature selection through the Least Absolute Shrinkage and Selection Operator cross-validation (Lasso CV) method identified the most critical predictors for early prognosis in AIS patients as white blood cell (WBC), homocysteine (HCY), D-Dimer, baseline NIHSS, fibrinogen degradation product (FDP), and glucose (GLU). Among the nine machine learning models evaluated, the Random Forest model exhibited superior performance in the test set, achieving an Area Under the Curve (AUC) of 0.852, an accuracy rate of 0.818, a sensitivity of 0.654, a specificity of 0.945, and a recall rate of 0.900.
CONCLUSION
These findings indicate that RF models utilizing general clinical and laboratory data from the initial 24 h of admission can effectively predict the early prognosis of AIS patients.
PubMed: 38938777
DOI: 10.3389/fneur.2024.1407152 -
Euro Surveillance : Bulletin Europeen... May 2024is a bacterium widely distributed in the environment. Listeriosis is a severe disease associated with high hospitalisation and mortality rates. In April 2019,...
is a bacterium widely distributed in the environment. Listeriosis is a severe disease associated with high hospitalisation and mortality rates. In April 2019, listeriosis was diagnosed in two hospital patients in Finland. We conducted a descriptive study to identify the source of the infection and defined a case as a person with a laboratory-confirmed serogroup IIa sequence type (ST) 37. Six cases with ST 37 were notified to the Finnish Infectious Diseases Registry between 2015 and 2019. Patient interviews and hospital menus were used to target traceback investigation of the implicated foods. In 2021 and 2022, similar ST 37 was detected from samples of a ready-to-eat plant-based food product including fava beans. Inspections by the manufacturer and the local food control authority indicated that the food products were contaminated with after pasteurisation. Our investigation highlights the importance that companies producing plant-based food are subject to similar controls as those producing food of animal origin. Hospital menus can be a useful source of information that is not dependent on patient recall.
Topics: Humans; Listeria monocytogenes; Listeriosis; Disease Outbreaks; Finland; Food Microbiology; Female; Male; Foodborne Diseases; Middle Aged; Aged; Food Contamination; Adult; Fabaceae
PubMed: 38726694
DOI: 10.2807/1560-7917.ES.2024.29.19.2300488 -
BMJ Open May 2024Radiologist shortages threaten the sustainability of breast cancer screening programmes. Artificial intelligence (AI) products that can interpret mammograms could...
Protocol for evaluating the fitness for purpose of an artificial intelligence product for radiology reporting in the BreastScreen New South Wales breast cancer screening programme.
INTRODUCTION
Radiologist shortages threaten the sustainability of breast cancer screening programmes. Artificial intelligence (AI) products that can interpret mammograms could mitigate this risk. While previous studies have suggested this technology has accuracy comparable to radiologists most have been limited by using 'enriched' datasets and/or not considering the interaction between the algorithm and human readers. This study will address these limitations by comparing the accuracy of a workflow using AI alongside radiologists on a large consecutive cohort of examinations from a breast cancer screening programme. The study will combine the strengths of a large retrospective design with the benefit of prospective data collection. It will test this technology without risk to screening programme participants nor the need to wait for follow-up data. With a sample of 2 years of consecutive screening examinations, it is likely the largest test of this technology to date. The study will help determine whether this technology can safely be introduced into the BreastScreen New South Wales (NSW) population-based screening programme to address radiology workforce risks without compromising cancer detection rates or increasing false-positive recalls.
METHODS AND ANALYSIS
A retrospective, consecutive cohort of digital mammography screens from 658 207 examinations from BreastScreen NSW will be reinterpreted by the Lunit Insight MMG AI product. The cohort includes 4383 screen-detected and 1171 interval cancers. The results will be compared with radiologist single reading and the AI results will also be used to replace the second reader in a double-reading model. New adjudication reading will be performed where the AI disagrees with the first reader. Recall rates and cancer detection rates of combined AI-radiologist reading will be compared with the rates obtained at the time of screening.
ETHICS AND DISSEMINATION
This study has ethical approval from the NSW Health Population Health Services Research Ethics Committee (2022/ETH02397). Findings will be published in peer-reviewed journals and presented at conferences. The findings of this evaluation will be provided to programme managers, governance bodies and other stakeholders in Australian breast cancer screening programmes.
Topics: Humans; Breast Neoplasms; Female; Mammography; Artificial Intelligence; New South Wales; Early Detection of Cancer; Retrospective Studies; Mass Screening; Middle Aged; Research Design
PubMed: 38806433
DOI: 10.1136/bmjopen-2023-082350 -
Bioengineering (Basel, Switzerland) Jan 2024Corneal endothelial decompensation is treated by the corneal transplantation of donor corneas, but donor shortages and other problems associated with corneal...
Corneal endothelial decompensation is treated by the corneal transplantation of donor corneas, but donor shortages and other problems associated with corneal transplantation have prompted investigations into tissue engineering therapies. For clinical use, cells used in tissue engineering must undergo strict quality control to ensure their safety and efficacy. In addition, efficient cell manufacturing processes are needed to make cell therapy a sustainable standard procedure with an acceptable economic burden. In this study, we obtained 3098 phase contrast images of cultured human corneal endothelial cells (HCECs). We labeled the images using semi-supervised learning and then trained a model that predicted the cell centers with a precision of 95.1%, a recall of 92.3%, and an F-value of 93.4%. The cell density calculated by the model showed a very strong correlation with the ground truth (Pearson's correlation coefficient = 0.97, value = 8.10 × 10). The total cell numbers calculated by our model based on phase contrast images were close to the numbers calculated using a hemocytometer through passages 1 to 4. Our findings confirm the feasibility of using artificial intelligence-assisted quality control assessments in the field of regenerative medicine.
PubMed: 38247948
DOI: 10.3390/bioengineering11010071 -
Appetite Jun 2024This study investigates implicit and explicit attitudes toward products before and beyond the best-before date (BBD) using an Implicit Association Test and an online...
This study investigates implicit and explicit attitudes toward products before and beyond the best-before date (BBD) using an Implicit Association Test and an online questionnaire. Moreover, we test whether consumer perception of and behavior toward products beyond the BBD can be manipulated using a priming task. We use a three-group between-subjects design where respondents had to recall either a frugal, a wasteful, or an unrelated behavior. Results show that consumers have negative implicit associations with products beyond the BBD. Reduced health and safety perceptions, consumers' strategies to determine edibility, and general risk perception of products beyond the BBD predict consumption of these products. While recalling a frugal behavior does not have significant effects, recalling a wasteful behavior prior to evaluating products beyond the BBD leads to a decrease in the perceived safety and healthfulness of these products.
PubMed: 38876149
DOI: 10.1016/j.appet.2024.107556