Relationship between admission selection tools and student attrition in the early years of medical school.

Objectives: Placement in medical schools is highly sought after worldwide with fierce competition among applicants. However, some of the best students withdraw after being accepted to medical school. The aim of this study was to investigate early student attrition within the first 2 years of medical school and determine its relationship to admission selection tools. Methods: Quantitative research was conducted at the College of Medicine and Health Sciences from 2016 until 2020, during which time routine admission data and students' examination results for the first 2 years were collected and analyzed. Results: The attrition rate during the study period was 31.7%. High school and college written examination scores were significantly related to completing the premedical program (p = 0.001 and p = 0.002, respectively). Female students scored significantly higher in multiple mini interviews (MMIs) compared with male counterparts (p < 0.001). However, the difference in MMI score was not related to student attrition (p = 0.148). Conclusion: The cause of early attrition is complex and cannot be attributed to a single factor.Undergraduate high school score and written admission examination results were statistically significant factors in relation to student attrition rate and low academic performance. The results of this study showed that the female students scored significantly higher in the multiple MMI tests compared to male students. However, MMI score alone was not significantly related to student attrition.

Utilizing machine learning for survival analysis to identify risk factors for COVID-19 intensive care unit admission: A retrospective cohort study from the United Arab Emirates.

BACKGROUND: The current situation of the unprecedented COVID-19 pandemic leverages Artificial Intelligence (AI) as an innovative tool for addressing the evolving clinical challenges. An example is utilizing Machine Learning (ML) models-a subfield of AI that take advantage of observational data/Electronic Health Records (EHRs) to support clinical decision-making for COVID-19 cases. This study aimed to evaluate the clinical characteristics and risk factors for COVID-19 patients in the United Arab Emirates utilizing EHRs and ML for survival analysis models. METHODS: We tested various ML models for survival analysis in this work we trained those models using a different subset of features extracted by several feature selection methods. Finally, the best model was evaluated and interpreted using goodness-of-fit based on calibration curves,Partial Dependence Plots and concordance index. RESULTS: The risk of severe disease increases with elevated levels of C-reactive protein, ferritin, lactate dehydrogenase, Modified Early Warning Score, respiratory rate and troponin. The risk also increases with hypokalemia, oxygen desaturation and lower estimated glomerular filtration rate and hypocalcemia and lymphopenia. CONCLUSION: Analyzing clinical data using AI models can provide vital information for clinician to measure the risk of morbidity and mortality of COVID-19 patients. Further validation is crucial to implement the model in real clinical settings.

MRI Markers of Degenerative Disc Disease in Young Patients With Multiple Sclerosis.

Background and Purpose: Evidence has emerged for an association between degenerative disc disease (DDD) and multiple sclerosis (MS). The purpose of the current study is to determine the presence and extent of cervical DDD in young patients (age <35) with MS, an age cohort that is less well studied for these changes. Methods: Retrospective chart review of consecutive patients aged <35 referred from the local MS clinic who were MRI scanned between May 2005 and November 2014. 80 patients (51 female and 29 male) with MS of any type ranging between 16 and 32 years of age (average 26) were included. Images were reviewed by 3 raters and assessed for presence and extent of DDD, as well as cord signal abnormalities. Interrater agreement was assessed using Kendall's W and Fleiss' Kappa statistics. Results: Substantial to very good interrater agreement was observed using our novel DDD grading scale. At least some degree of DDD was found in over 91% of patients. The majority scored mild (grade 1, 30-49%) to moderate (grade 2, 39-51%) degenerative changes. Cord signal abnormality was seen in 56-63%. Cord signal abnormality, when present, occurred exclusively at degenerative disc levels in only 10-15%, significantly lower than other distributions (P < .001 for all pairwise comparisons). Conclusions: MS patients demonstrate unexpected cervical DDD even at a young age. Future study is warranted to investigate the underlying etiology, such as altered biomechanics. Furthermore, cord lesions were found to occur independently of DDD.

Synthesis of hydroxyapatite/polyethylene glycol 6000 composites by novel dissolution/precipitation method: optimization of the adsorption process using a factorial design: DFT and molecular dynamic.

In this work, we presented a synthesis of a composite based on HAp and PEG 6000 using a new method of synthesis dissolution precipitation to be applied for application of wastewater purification from toxic metal ions. Multiple characterization methods were used to analyze the morphology and the structure of the well-prepared compounds including FT-IR, Raman, XRD, XPS, TGA and SEM were used to conduct a composite analysis. The adsorption effectiveness of this analysis towards Pb2+ and various other hazardous metal ions found in sewage was assessed. Batch experiments were conducted to optimize the various operational parameters including adsorbent dose, temperature, pH, contact time, and initial concentration. The Langmuir isotherm was used to fit the data, and it predicted monolayer adsorption with a maximum capacity of 67 mg g-1 for HAP PEG600 and 60 mg g-1 for HAp. A pseudo-second-order equation fits the adsorption process well (0.961-0.971). The thermodynamic data support the spontaneous metal bonding to the composite receptor sites. Theoretical calculations showed that the interaction strength is very strong and gets stronger when the PEG6000 is deprotonated. The results presented here are supported by evidence acquired from experiments. Theoretical computation using Monte Carlo (MC) and Molecular Dynamic (MD) simulation models showed excellent affinity of prepared foams for the model ion Pb2+ with highly negative adsorption energy values indicating vigorous interactions of Pb2+ with the adsorbate surfaces.

Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach.

Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95-100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG.

Phenytoin's Pharmacological Interactions with Medicinal Herbs: A Review.

Physiologic changes due to aging, pregnancy, nutritional status, drug interactions, and can affect pharmacokinetics and pharmacodynamics of antiepileptic drugs. In this review article, the interactions between phenytoin and herbs recorded in the literature were summarized according to the Medline database (via PubMed). Our results revealed that, changes in phenytoin's bioavailability were reported for co-administration of herbs or herbal extracts. An increase in phenytoin blood levels was established with Piper nigrum, Mentat, and Lipidum sativum in in vitro and/or in vivo studies. In contrast, herbphenytoin interactions led to sub-therapeutic levels of phenytoin in other cases with herbs such as Cannabis, Ginkgo biloba, Morinda citrifolia, Nigella sativa, and Trigonella foenum graceum. In addition, the findings of other pharmcodynamic experiments showed that various herbs, including Zizyphus jujube, Terminalia chebula, Curcuma longa L, and Centella asiatica, improved the pharmacological impact of phenytoin. To reduce the patients' health risks, health professionals involved in their treatment are expected to be thoroughly educated about the interactions between phenytoin and medicinal plants.

Case report: Birk-Landau-Perez syndrome linked to the SLC30A9 gene-identification of additional cases and expansion of the phenotypic spectrum.

Birk-Landau-Perez syndrome (BILAPES) is an autosomal recessive cerebro-renal syndrome associated with genetic defects in the SLC30A9 gene, initially reported in 2017 in six individuals belonging to a large Bedouin kindred. The SLC30A9 gene encodes a putative mitochondrial zinc transporter with ubiquitous expression, the highest found in the brain, kidney, and skeletal muscle. Since the first report, only one additional affected patient has been described, but there were some inconsistencies, such as hearing loss, failure to thrive, and neuroimaging findings between the clinical presentation of the disease in the Bedouin family and the second patient. Here, we present two more patients from a consanguineous Middle Eastern family with features of chronic kidney disease, neurodevelopmental regression, ataxia, hearing loss, and eye abnormalities, which were largely consistent with BILAPES. Whole-exome sequencing detected a homozygous in-frame deletion c.1049_1051delCAG (p.Ala350del) in the SLC30A9 gene, which was the same variant detected in the patients from the primary literature report and the variant segregated with disease in the family. However, in the patients described here, brain MRI showed cerebellar atrophy, which was not a cardinal feature of the syndrome from the primary report. Our findings provide further evidence for SLC30A9-associated BILAPES and contribute to defining the clinical spectrum.

Unraveling Lifelong Brain Morphometric Dynamics: A Protocol for Systematic Review and Meta-Analysis in Healthy Neurodevelopment and Ageing.

A high incidence and prevalence of neurodegenerative diseases and neurodevelopmental disorders justify the necessity of well-defined criteria for diagnosing these pathologies from brain imaging findings. No easy-to-apply quantitative markers of abnormal brain development and ageing are available. We aim to find the characteristic features of non-pathological development and degeneration in distinct brain structures and to work out a precise descriptive model of brain morphometry in age groups. We will use four biomedical databases to acquire original peer-reviewed publications on brain structural changes occurring throughout the human life-span. Selected publications will be uploaded to Covidence systematic review software for automatic deduplication and blinded screening. Afterwards, we will manually review the titles, abstracts, and full texts to identify the papers matching eligibility criteria. The relevant data will be extracted to a 'Summary of findings' table. This will allow us to calculate the annual rate of change in the volume or thickness of brain structures and to model the lifelong dynamics in the morphometry data. Finally, we will adjust the loss of weight/thickness in specific brain areas to the total intracranial volume. The systematic review will synthesise knowledge on structural brain change across the life-span.

Novel compound heterozygous variants (c.971delA/c.542C > T) in SLC1A4 causes spastic tetraplegia, thin corpus callosum, and progressive microcephaly: a case report and mutational analysis.

Spastic tetraplegia, thin corpus callosum, and progressive microcephaly (SPATCCM) are linked to SLC1A4 genetic variants since the first reported case in 2015. SLC1A4 encodes for the neutral amino acid transporter ASCT1 which is involved in the transportation of serine between astrocytes and neurons. Although most of the reported cases are of Ashkenazi Jewish ancestry, SPATCCM has also been reported in Irish, Italian, Czech, Palestinian, and Pakistani ethnicities. Herein, we report two Pakistani male siblings from a non-consanguineous marriage presented with global developmental delay associated with spastic quadriplegia, microcephaly, and infantile spasm. Since infancy, both siblings suffered from microcephaly with brain MRI demonstrating generalized atrophy of the frontal, temporal, and parietal lobes with a prominence of the subarachnoid spaces, widening of the Sylvian fissures, and enlargement of the ventricular system not compatible with the chronological age of both patients associated with thinning of the corpus callosum. Whole-exome sequencing of both affected brothers revealed novel compound heterozygous variants in the SLC1A4 gene (NM_003038) segregating from their parents. The maternal c.971delA (p.N324Tfs*29) deletion variant disturbs the transcript reading frame leading to the generation of a premature stop codon and its subsequent degradation by nonsense-mediated mRNA decay as detected through expression analysis. The paternal c.542C > T (p.S181F) missense variant was predicted deleterious via multiple in silico prediction tools as the amino acid substitution is speculated to affect the overall ASCT1 structural confirmation due to the loss of an H-bond at the core of the protein at this position which might affect its function as concluded from the simulation analysis. The presented cases expand the genetic and clinical spectrum of ASCT1 deficiency and support the importance of including SLC1A4 gene screening in infants with unexplained global neurodevelopmental delay regardless of ethnicity.

Multimodal diagnostics in multiple sclerosis: predicting disability and conversion from relapsing-remitting to secondary progressive disease course - protocol for systematic review and meta-analysis.

BACKGROUND: The number of patients diagnosed with multiple sclerosis (MS) has increased significantly over the last decade. The challenge is to identify the transition from relapsing-remitting to secondary progressive MS. Since available methods to examine patients with MS are limited, both the diagnostics and prognostication of disease progression would benefit from the multimodal approach. The latter combines the evidence obtained from disparate radiologic modalities, neurophysiological evaluation, cognitive assessment and molecular diagnostics. In this systematic review we will analyse the advantages of multimodal studies in predicting the risk of conversion to secondary progressive MS. METHODS AND ANALYSIS: We will use peer-reviewed publications available in Web of Science, Medline/PubMed, Scopus, Embase and CINAHL databases. In vivo studies reporting the predictive value of diagnostic methods will be considered. Selected publications will be processed through Covidence software for automatic deduplication and blind screening. Two reviewers will use a predefined template to extract the data from eligible studies. We will analyse the performance metrics (1) for the classification models reflecting the risk of secondary progression: sensitivity, specificity, accuracy, area under the receiver operating characteristic curve, positive and negative predictive values; (2) for the regression models forecasting disability scores: the ratio of mean absolute error to the range of values. Then, we will create ranking charts representing performance of the algorithms for calculating disability level and MS progression. Finally, we will compare the predictive power of radiological and radiomical correlates of clinical disability and cognitive impairment in patients with MS. ETHICS AND DISSEMINATION: The study does not require ethical approval because we will analyse publicly available literature. The project results will be published in a peer-review journal and presented at scientific conferences. PROSPERO REGISTRATION NUMBER: CRD42022354179.

Management of Blunt Sternal Fractures in a Community-Based Hospital.

Background: Sternal fractures are not commonly observed in patients with blunt trauma. The routine use of computed tomography (CT) in the evaluation of chest trauma helps identify these fractures. We studied the incidence, injury mechanism, management, and outcome of sternal fractures in patients with blunt trauma treated at our community-based hospital. Methods: We retrospectively reviewed the chest CT scans of all patients with blunt trauma who were presented to our community-based hospital from October 2010 to March 2019. The study variables included age at the time of injury, sex, mechanism of injury, type, and site of fracture, associated injuries, Glasgow Coma Scale, Injury Severity Score, need for intensive care unit admission, hospital stay, and long-term outcome. Results: In total, 5632 patients with blunt trauma presented to our hospital during the study period, and chest CT scan was performed for 2578 patients. Sternal fractures were diagnosed in 63 patients. The primary mechanism of injury was a motor vehicle collision. The most common site of fracture was the body of the sternum (47 patients; 74.6%). Twenty (31.7%) patients had an isolated sternal fracture with no other injuries. Seven (11.1%) patients were discharged directly from the emergency department. Two patients died (overall mortality rate, 3.2%) and two experienced long-term disability. Conclusions: The incidence of sternal fractures in our patient population was similar to that reported by tertiary hospitals. Patients with a sternal fracture and normal cardiac enzyme levels and electrocardiogram may be safely discharged from the emergency department, provided there are no other major injuries.

Deep Learning-Based Automatic Assessment of Lung Impairment in COVID-19 Pneumonia: Predicting Markers of Hypoxia With Computer Vision.

Background: Hypoxia is a potentially life-threatening condition that can be seen in pneumonia patients. Objective: We aimed to develop and test an automatic assessment of lung impairment in COVID-19 associated pneumonia with machine learning regression models that predict markers of respiratory and cardiovascular functioning from radiograms and lung CT. Materials and Methods: We enrolled a total of 605 COVID-19 cases admitted to Al Ain Hospital from 24 February to 1 July 2020 into the study. The inclusion criteria were as follows: age ≥ 18 years; inpatient admission; PCR positive for SARS-CoV-2; lung CT available at PACS. We designed a CNN-based regression model to predict systemic oxygenation markers from lung CT and 2D diagnostic images of the chest. The 2D images generated by averaging CT scans were analogous to the frontal and lateral view radiograms. The functional (heart and breath rate, blood pressure) and biochemical findings (SpO2, H C O 3 - , K +, Na +, anion gap, C-reactive protein) served as ground truth. Results: Radiologic findings in the lungs of COVID-19 patients provide reliable assessments of functional status with clinical utility. If fed to ML models, the sagittal view radiograms reflect dyspnea more accurately than the coronal view radiograms due to the smaller size and the lower model complexity. Mean absolute error of the models trained on single-projection radiograms was approximately 11÷12% and it dropped by 0.5÷1% if both projections were used (11.97 ± 9.23 vs. 11.43 ± 7.51%; p = 0.70). Thus, the ML regression models based on 2D images acquired in multiple planes had slightly better performance. The data blending approach was as efficient as the voting regression technique: 10.90 ± 6.72 vs. 11.96 ± 8.30%, p = 0.94. The models trained on 3D images were more accurate than those on 2D: 8.27 ± 4.13 and 11.75 ± 8.26%, p = 0.14 before lung extraction; 10.66 ± 5.83 and 7.94 ± 4.13%, p = 0.18 after the extraction. The lung extraction boosts 3D model performance unsubstantially (from 8.27 ± 4.13 to 7.94 ± 4.13%; p = 0.82). However, none of the differences between 3D and 2D were statistically significant. Conclusion: The constructed ML algorithms can serve as models of structure-function association and pathophysiologic changes in COVID-19. The algorithms can improve risk evaluation and disease management especially after oxygen therapy that changes functional findings. Thus, the structural assessment of acute lung injury speaks of disease severity.

Significance of Cardiac Magnetic Resonance Feature Tracking of the Right Ventricle in Predicting Subclinical Dysfunction in Patients with Thalassemia Major.

In patients with thalassemia major (TM), cardiac magnetic resonance feature-tracking (CMR-FT) has been shown to be an effective method for diagnosing subclinical left ventricular (LV) dysfunction. This study aimed to determine whether CMR-FT could detect abnormal RV dysfunction in patients with a normal right ventricular ejection fraction (RVEF). We performed a retrospective analysis of TM patients admitted to Dubai's Rashid Hospital between July 2019 and March 2021. The inclusion criteria were TM patients with SSFP cine with T2* (T2*-weighted imaging), while exclusion criteria included any other cardiovascular disease. When there was no myocardial iron overload (MIO) (T2* ≥ 20 ms) and when there was significant MIO (T2* < 20 ms), the CMR-FT was used to correlate with EF. Among the 89 participants, there were 46 men (51.7%) and 43 women (48.3%), with a mean age of 26.14 ± 7.4 years (range from 10 to 48 years). Forty-six patients (51.69%) did not have MIO, while 43 individuals did (48.31%). Thirty-nine patients (32.6%) were diagnosed with severe MIO, while seventeen (19.1%) were diagnosed with mild to moderate MIO. A significant correlation existed between RVEF and T2* values (r = 0.274, p = 0.014) and between left ventricular ejection fraction (LVEF) and T2* values (r = 0.256, p = 0.022). Using a multiple logistic regression model with predictors such as right ventricular longitudinal strain (RVGLS), LV ejection fraction (LV EF), and hemoglobin, abnormal myocardial iron overload can be predicted. This model demonstrates an AUC of 78.3%, a sensitivity of 72%, and a specificity of 76%. In the group with preserved RVEF > 53%, the left ventricular radial strain (LVGRS) (p = 0.001), right ventricular radial strain (RVGRS) (p = 0.000), and right ventricular basal circumferential strain (RVGCS-basal) (p = 0.000) CMR-FT strain values are significantly lower than those of the control group (p > 0.05). There was no significant correlation between the LVGLS and T2*. RVGLS was ranked among the most accurate predictors of abnormal myocardial iron overload. The LVGRS, RVGRS, and RVGCS-basal CMR-FT strain values were the best predictors of subclinical RV dysfunction in the group with preserved RVEF. The most accurate way to diagnose MIO is still T2*, but FT-strain can help us figure out how MIO affects the myocardium from a pathophysiological point of view.

Correction: Thiophene derivatives as corrosion inhibitors for 2024-T3 aluminum alloy in hydrochloric acid medium.

[This corrects the article DOI: 10.1039/D2RA00185C.].

Preparation of fatty acid-amino diol condensate and its utilization as a durable nonionic softener for PAN fabrics.

Polyacrylonitrile (PAN) fabrics meet the customers' requirements in many aspects. Nevertheless, PAN fabrics suffer from low moisture regain and the accumulation of electrostatic charges on the fabric surface. Some PAN products exhibit rough a surface which is uncomfortable for human skin. Herein, we synthesized a new hydrophilic nonionic softener by reacting a fatty acid (FA), extracted from wool wax, with 2-amino-2-methyl-1,3-propanediol (AMPD). Adopting the pad-dry-cure technique, the synthesized softener was chemically bound to pretreated PAN fabrics. Without deterioration of the fabrics' mechanical properties, new functions have been imparted to the treated PAN fabrics, viz., silk-like hand, induced resistance to the accumulation of electrostatic charges, and improved wettability. Fourier-transform infrared (FTIR) spectroscopy and carbon-13 nuclear magnetic resonance (13C-NMR) were utilized for the structure elucidation of the prepared softener as well as to determine whether AMPD reacts with the fatty acid through its amino or hydroxyl group. The mechanism of the preparation of the softener as well as its mode of action on PAN fabrics were proposed. The effects of the applied softener on air and water permeability, ultraviolet protection factor (UPF), stiffness, tensile properties, and yellowness of the treated fabric were studied. The scanning electron micrographs of the treated fabric revealed the existence of a layer of the applied softener on the fabric surface. The finished fabric was found to be durable against washing for up to 20 cycles in terms of the fabric smoothness and durability.

Thiophene derivatives as corrosion inhibitors for 2024-T3 aluminum alloy in hydrochloric acid medium.

Thiophene derivatives, namely (E)-thiophene-2-carbaldehyde oxime (OXM) and (E)-5-(thiophen-2-yl)-1H-tetrazole (TET), were synthesized and characterized via 1H and 13C NMR. Furthermore, their inhibitory property for AA2024-T3 in 1 M HCl solution was investigated via electrochemical impedance spectroscopy and potentiodynamic polarization at 293 K, together with DFT/B3LYP-based calculations. Numerous global and local descriptors of reactivity such as EHOMO, ELUMO, energy gap, electronegativity (χ), hardness (η), and frontier molecular orbital repartitions were investigated to describe the reactivity of each molecule. Alternatively, Monte Carlo simulations were performed under the solvation condition on the Al (111) surface to understand the adsorption behavior of the as-studied inhibitors deeply. The inhibition efficiency increased with an increase in the inhibitor concentration, achieving maximum values of 94.0% and 96% at 10-3 M, respectively. The polarization curves showed that the examined compounds act as mixed-type inhibitors. In addition, the adsorption of these compounds obeyed the Al Awady, Flory-Huggins and Temkin isotherms. The surface characterization analysis via SEM/EDX confirmed the presence of a barrier layer covering the aluminum surface. The experimental inhibition efficiencies were correlated with global descriptors, which confirmed that this theoretical study is useful for the protection of aluminum alloy metal in an acidic medium.

Patterns of structure-function association in normal aging and in Alzheimer's disease: Screening for mild cognitive impairment and dementia with ML regression and classification models.

Background: The combined analysis of imaging and functional modalities is supposed to improve diagnostics of neurodegenerative diseases with advanced data science techniques. Objective: To get an insight into normal and accelerated brain aging by developing the machine learning models that predict individual performance in neuropsychological and cognitive tests from brain MRI. With these models we endeavor to look for patterns of brain structure-function association (SFA) indicative of mild cognitive impairment (MCI) and Alzheimer's dementia. Materials and methods: We explored the age-related variability of cognitive and neuropsychological test scores in normal and accelerated aging and constructed regression models predicting functional performance in cognitive tests from brain radiomics data. The models were trained on the three study cohorts from ADNI dataset-cognitively normal individuals, patients with MCI or dementia-separately. We also looked for significant correlations between cortical parcellation volumes and test scores in the cohorts to investigate neuroanatomical differences in relation to cognitive status. Finally, we worked out an approach for the classification of the examinees according to the pattern of structure-function associations into the cohorts of the cognitively normal elderly and patients with MCI or dementia. Results: In the healthy population, the global cognitive functioning slightly changes with age. It also remains stable across the disease course in the majority of cases. In healthy adults and patients with MCI or dementia, the trendlines of performance in digit symbol substitution test and trail making test converge at the approximated point of 100 years of age. According to the SFA pattern, we distinguish three cohorts: the cognitively normal elderly, patients with MCI, and dementia. The highest accuracy is achieved with the model trained to predict the mini-mental state examination score from voxel-based morphometry data. The application of the majority voting technique to models predicting results in cognitive tests improved the classification performance up to 91.95% true positive rate for healthy participants, 86.21%-for MCI and 80.18%-for dementia cases. Conclusion: The machine learning model, when trained on the cases of this of that group, describes a disease-specific SFA pattern. The pattern serves as a "stamp" of the disease reflected by the model.

Proportional Changes in Cognitive Subdomains During Normal Brain Aging.

Background: Neuroscience lacks a reliable method of screening the early stages of dementia. Objective: To improve the diagnostics of age-related cognitive functions by developing insight into the proportionality of age-related changes in cognitive subdomains. Materials and Methods: We composed a battery of psychophysiological tests and collected an open-access psychophysiological outcomes of brain atrophy (POBA) dataset by testing individuals without dementia. To extend the utility of machine learning (ML) classification in cognitive studies, we proposed estimates of the disproportional changes in cognitive functions: an index of simple reaction time to decision-making time (ISD), ISD with the accuracy performance (ISDA), and an index of performance in simple and complex visual-motor reaction with account for accuracy (ISCA). Studying the distribution of the values of the indices over age allowed us to verify whether diverse cognitive functions decline equally throughout life or there is a divergence in age-related cognitive changes. Results: Unsupervised ML clustering shows that the optimal number of homogeneous age groups is four. The sample is segregated into the following age-groups: Adolescents ∈ [0, 20), Young adults ∈ [20, 40), Midlife adults ∈ [40, 60) and Older adults ≥60 year of age. For ISD, ISDA, and ISCA values, only the median of the Adolescents group is different from that of the other three age-groups sharing a similar distribution pattern (p > 0.01). After neurodevelopment and maturation, the indices preserve almost constant values with a slight trend toward functional decline. The reaction to a moving object (RMO) test results (RMO_mean) follow another tendency. The Midlife adults group's median significantly differs from the remaining three age subsamples (p < 0.01). No general trend in age-related changes of this dependent variable is observed. For all the data (ISD, ISDA, ISCA, and RMO_mean), Levene's test reveals no significant changes of the variances in age-groups (p > 0.05). Homoscedasticity also supports our assumption about a linear dependency between the observed features and age. Conclusion: In healthy brain aging, there are proportional age-related changes in the time estimates of information processing speed and inhibitory control in task switching. Future studies should test patients with dementia to determine whether the changes of the aforementioned indicators follow different patterns.

Synergistic effect of sericin and keratin in gelatin based nanofibers for in vitro applications.

Protein-based nanomaterials are gaining growing interest in biomedical field. The present paper evaluates the physico-chemical properties of electrospun nanofibers resulting from the combination of gelatin with keratin (from wool) and sericin (from silk) to validate their use for in vitro interaction studies. We demonstrated that that presence of sericin influences the fiber morphology at macroscopic level - i.e., wide diameter distributions by SEM and image analysis - with effects on chemical - i.e., a decrease of hydrogen bonds of NH groups verified by infrared spectroscopy - and thermal behavior of electrospun nanofibers, in comparison with gelatin-based ones. Moreover, we verified that sericin, in combination with keratin macromolecules, can amplify the biochemical signal of gelatin, improving the in-vitro stability of gelatin-based nanofibers. In vitro results confirm a synergistic effect of sericin and keratin on human Mesenchymal Stem Cells (hMSC) proliferation - increase over 50% respect to other types - associated to the enhancement of in vitro stability directly ascribable to the peculiar physical interaction among the proteins. These findings suggest the use of sericin/keratin/gelatin enriched electrospun fibers as nanostructured platforms for interface tissue engineering.