Flat Γ Moiré Bands in Twisted Bilayer WSe_{2}

The recent observation of correlated phases in transition metal dichalcogenide moiré systems at integer and fractional filling promises new insight into metal-insulator transitions and the unusual states of matter that can emerge near such transitions. Here, we combine real- and momentum-space mapping techniques to study moiré superlattice effects in 57.4° twisted WSe_{2} (tWSe_{2}). Our data reveal a split-off flat band that derives from the monolayer Γ states. Using advanced data analysis, we directly quantify the moiré potential from our data. We further demonstrate that the global valence band maximum in tWSe_{2} is close in energy to this flat band but derives from the monolayer K states which show weaker superlattice effects. These results constrain theoretical models and open the perspective that Γ-valley flat bands might be involved in the correlated physics of twisted WSe_{2}.

Alkylphosphonium carboxylate ionic liquids with tuned microscopic structures and properties

Eleven pure alkylphosphonium carboxylate ionic liquids (ILs) were synthesised following a reliable and accessible route. Tetrabutylphosphonium and tetradecyltrihexylphosphonium cations were associated to a variety of [R-COO]^- anions with R varying from shorter to longer linear alkyl chains; smaller to bulkier branched alkyl chains; cyclic saturated aliphatic and aromatic moieties; and one heterocyclic aromatic ring containing nitrogen. A combined experimental and molecular simulation study allowed the full characterization of the physico-chemical properties, the structure and the thermal stability of the synthesized ILs. Although slightly more viscous than their imidazolium counterparts, the viscosities of the prepared salts decrease dramatically with temperature and are comparable to other ILs above 50 °C, a manageable temperature as they are thermally stable up to temperatures above 250 °C, even under an oxidizing atmosphere. The microscopic structure of the phophonium ILs is rich and has been studied both experimentally using SAXS and by molecular dynamics simulation using state of the art polarizable force fields whose parameters were determined when necessary. Unique and surprising anion-anion correlations were found for the tetrazolate-based IL allowing to explain some of the unique physical-chemical properties of this phosphonium salt.

The CLEAR X-ray emission spectrometer available at the CLAESS beamline of ALBA synchrotron

The CLEAR X-ray emission spectrometer installed at the CLAESS beamline of the ALBA synchrotron is described. It is an energy-dispersive spectrometer based on Rowland circle geometry with 1 m-diameter circle. The energy dispersion is achieved by the combination of a diced analyzer crystal and a unidimensional detector. A single unconventional dynamically bent analyzer crystal (Si 111) permits a wide energy range to be covered, just by exploiting its different reflections (333, 444, 555, 777, 888): 6-22 keV, with a spectrometer efficiency that decreases above 11 keV because of the Si detector thickness (Mythen, 350 µm), while the relative scattering intensities for the Si 333, 444, 555, 777 and 888 reflections correspond to 36, 40, 21, 13 and 15, respectively. The provided energy resolution is typically below 1-2 eV and depends on the beam size, working Bragg angle and reflection exploited. In most cases the energy dispersion ranges from 10 to 20 eV and can be enlarged by working in the out-of-Rowland geometry up to 40 eV. The spectrometer works in full backscattering geometry with the beam passing through the two halves of the analyzer. The vacuum beam path and the particular geometry allow a typical average noise of only 0.5 counts per second per pixel. The spectrometer is mainly used for measuring emission lines and high-resolution absorption spectra, with a typical scanning time for highly concentrated systems of around half an hour, including several repeats. The intrinsic energy dispersion allows systematic collection of resonant X-ray emission maps by measuring high-resolution absorption spectra. Moreover, it allows spectra to be measured on a single-shot basis. Resonant inelastic X-ray scattering experiments to probe electronic excitations are feasible, although the spectrometer is not optimized for this purpose due to the limited energy resolution and scattering geometry provided. In that case, to minimize the quasi-elastic line, the spectrometer is able to rotate along the beam path. Advantages and disadvantages with respect to other existing spectrometers are highlighted.

P-080 Intrauterine insemination (IUI) success rates in patients with suboptimal total processed motile sperm count (TPMSC) using a second consecutive semen sample

Study question

Is it effective to request a second consecutive semen sample in patients with less than 1 million TPMSC in a first semen sample for IUI?

Summary answer

In men with TPMSC under one million, the issuing of a second consecutive sample reached pregnancy rates similar to those published for IUI.

What is known already

IUI offers a comparable cumulative live birth rate in 3-4 cycles compared to IVF and can be preferred as a cost-effective first-line treatment in mild male factor or unexplained infertility. The quality of the processed semen sample is an important factor for the IUI success.

The WHO recommends an abstinence period to ensure best quality of semen samples. However, it has been observed that when men with moderate male factor who are unable to meet the minimum requirements for IUI are asked to produce a second sample better counts are obtained; questioning the time correlation between abstinence and semen quality.

Study design, size, duration

This was a retrospective study conducted in the reproductive medicine unit of a private hospital in Chile between July 2015 and March 2021. All patients who underwent IUI in the study period that had an TPMSC less than 1 million in the eyaculate and to whom a second consecutive sample was requested were included.

Participants/materials, setting, methods

118 patients who underwent 140 IUI cycles were included in the study. All the patients with an PMSC under 1 million at the time of the IUI were requested a second consecutive semen sample within an hour or two from the previous eyaculate. The second samples were processed and used for insemination. The primary outcome was pregnancy rate. The secondary outcomes were semen quality (TPMSC of first and second semen samples).

Main results and the role of chance

Between 2015 and 2021 there were 140 IUI cycles in which a second consecutive semen sample was requested, including 118 patients. Overall 17 pregnancies were achieved. The pregnancy rate per cycle was 12,14% and the pregnancy rate per patient was 14,4%. The live birth rate per patient was 10,2%.

Regarding the sperm sample analysis, the median TPMSC of the first semen sample was 261.437. The median PMSC of the second consecutive sample was 7.315.000. 126 patients had an TPMSC of 0 in the first semen sample while only 9 patients had an IMSC of 0 in the second sample. In five cases a third consecutive sample was requested of which 4 patients had their cycles canceled because they did not meet the target PMSC. One patient had an TPMSC of 1.687.000 in the third sample.

Finally, of the 118 patients who did not meet the requirements for IUI with the first sample, only 19 cycles were canceled.

Limitations, reasons for caution

The study has the limitation of being a retrospective and descriptive study with no contol group. Also the group is heterogeneous because it includes patients with different female factors for infertility.

Wider implications of the findings

In developing countries and low-income settings the IUI remains a more accessible alternative in patients with infertility. Routinely recollecting a second semen sample in men with TPMSC <1 million would reduce cycle cancellation rates due to not achieving an optimal TPMSC, reaching pregnancy rates similar to those published for IUI.

Trial registration number

Not applicable

Human papillomavirus infection and lung adenocarcinoma: special benefit is observed in patients treated with immune checkpoint inhibitors

BACKGROUND

Human papilloma virus (HPV) has been associated with the development and modulation of response in a series of neoplasms. In the case of lung adenocarcinoma, its role in etiology and pathogenesis is still controversial. Considering that this infection brings foreign epitopes, it could be of prognostic significance in patients with lung adenocarcinoma treated with immunotherapy.

METHODS

In a retrospective cohort study we evaluated the presence of HPV genomic material in lung adenocarcinoma primary lesions with the INNO-LiPA platform. Viral replication was also evaluated by detecting the presence of oncoprotein E6/E7 messenger RNA (mRNA) by quantitative RT-PCR. To confirm possible hypotheses regarding viral oncogenesis, vascular endothelial growth factor (VEGF) and hypoxia-inducible factor 1 (HIF1) were evaluated with stromal fibrosis and immunoscore.

RESULTS

A total of 133 patients were included in the analysis, of whom 34 tested positive for HPV, reaching an estimated prevalence of 25.6% [95% confidence interval (CI) 18.2% to 32.9%]. E6/7 mRNA was identified in 28 out of the 34 previously positive cases (82.3%). In immune checkpoint inhibitor (ICI)-treated patients, the median overall survival reached 22.3 months [95% CI 19.4 months- not reached (NR)] for HPV-negative and was not reached in HPV-positive (HPV+) ones (95% CI 27.7-NR; P = 0.008). With regard to progression-free survival, HPV- patients reached a median of 9.2 months (95% CI 7.9-11.2 months) compared to 14.3 months (95% CI 13.8-16.4 months) when HPV was positive (P = 0.001). The overall response rate for HPV+ patients yielded 82.4% compared to 47.1% in negative ones. No differences regarding programmed death-ligand 1, VEGF, HIF1, stromal fibrosis, or immunoscore were identified.

CONCLUSIONS

In patients with HPV+ lung adenocarcinoma, a significant benefit in overall response and survival outcomes is observed.

Reconfiguring traditional EKG interpretation with artificial intelligence – a reliable, time-saving alternative?

Background

Time and accuracy are key factors that may make or break an efficient triage and management in most medical premises, particularly so when expedited diagnosis saves lives - a not so uncommon scenario in the field of cardiology. By studying the different variables involved in cardiologist-EKG interactions that lead to the identification and management of different cardiovascular entities, we delved into the applications of Artificial Intelligence (AI) algorithms in order to improve upon the classic, but dated, EKG methodology. With this study, we pit our algorithm against cardiologists to perform a thorough analysis of the time invested to diagnose an EKG as Normal, as well as an assessment of the accuracy of said label.

Purpose

To present a faster and reliable AI-guided EKG interpretation methodology that outperforms cardiologists' capabilities in identifying Normal EKG records.

Methods

The International Telemedical System (ITMS) developed and tested an EKG assessing AI algorithm and incorporated it into the workflow of their Telemedicine Integrated Platform, a digital EKG reading program where cardiologists continuously report their findings remotely in real time. During the month of April 2019; 35 ITMS cardiologists reported a grand total of 61,441 EKG records, later subjecting them to the AI algorithm, implemented through the “One Click Report” process. Through this simple 2-step approach, the algorithm provides a suggestion of “Normal” or “Abnormal” to the cardiologist based on the patterns of the fiducial points included in said EKG reports. A comparison of the time of normal EKG diagnosis is made and the correlation between AI and cardiologists is assessed.

Results

On average, our AI algorithm discerned a normal EKG within 30.63s (95% CI 26.51s to 34.75s), in solid contrast with cardiologists' interpretations alone, which amounted to 83.54s (95% CI from 69.43s to 97.65s). This accounts for an overall saving of 52.91s (95% CI 42.45s to 63.83s) by implementing this innovative methodology in a cardiologist practice. In addition, this method correctly reported 23,213 Normal EKG records out of the total 25,013 AI output, reaching a 92.8% correlation between man and machine. The total average time saved in normal EKG readings with AI (23,213) would accrue an approximate of 20,470 minutes (ie, 42 8-hours work shifts worth of time dedicated to diagnosing a normal EKG).

Conclusions

The implementation of automated AI-driven technologies into daily EKG interpretation tasks poses an attractive time-saving alternative for faster and accurate results in a modern cardiology practice. By further expanding on the concept of an intelligent EKG characterization device, a more efficient and patient-centered clinical exercise will ensue.

Funding Acknowledgement

Type of funding source: None

Enhancing AI-guided STEMI detection algorithms by incorporating higher quality fiduciary EKG elements

Background

As EKG interpretation paradigms to a physician-free milieu, accumulating massive quantities of distilled pre-processed data becomes a must for machine learning techniques. In our pursuit of reducing ischemic times in STEMI management, we have improved our Artificial Intelligence (AI)-guided diagnostic tool by following a three-step approach: 1) Increase accuracy by adding larger clusters of data. 2) Increase the breadth of EKG classifications to provide more precise feedback and further refine the inputs which ultimately reflects in better and more accurate outputs. 3) Improving the algorithms' ability to discern between cardiovascular entities reflected in the EKG records.

Purpose

To bolster our algorithm's accuracy and reliability for electrocardiographic STEMI recognition.

Methods

Dataset: A total of 7,286 12-lead EKG records of 10-seconds length with a sampling frequency of 500 Hz obtained from Latin America Telemedicine Infarct Network from April 2014 to December 2019. This included the following balanced classes: angiographically confirmed STEMI, branch blocks, non-specific ST-T abnormalities, normal, and abnormal (200+ CPT codes, excluding the ones included in other classes). Labels of each record were manually checked by cardiologists to ensure precision (Ground truth). Pre-processing: First and last 250 samples were discarded to avoid a standardization pulse. Order 5 digital low pass filters with a 35 Hz cut-off was applied. For each record, the mean was subtracted to each individual lead. Classification: Determined classes were “STEMI” and “Not-STEMI” (A combination of randomly sampled normal, branch blocks, non-specific ST-T abnormalities and abnormal records – 25% of each subclass). Training & Testing: A 1-D Convolutional Neural Network was trained and tested with a dataset proportion of 90/10, respectively. The last dense layer outputs a probability for each record of being STEMI or Not-STEMI. Additional testing was performed with a subset of the original complete dataset of unconfirmed STEMI. Performance indicators (accuracy, sensitivity, and specificity) were calculated for each model and results were compared with our previous findings from past experiments.

Results

Complete STEMI data: Accuracy: 95.9% Sensitivity: 95.7% Specificity: 96.5%; Confirmed STEMI: Accuracy: 98.1% Sensitivity: 98.1% Specificity: 98.1%; Prior Data obtained in our previous experiments are shown below for comparison.

Conclusion(s)

After the addition of clustered pre-processed data, all performance indicators for STEMI detection increased considerably between both Confirmed STEMI datasets. On the other hand, the Complete STEMI dataset kept a strong and steady set of performance metrics when compared with past results. These findings not only validate the consistency and reliability of our algorithm but also connotes the importance of creating a pristine dataset for this and any other AI-derived medical tools.

Funding Acknowledgement

Type of funding source: None

Countdown to physician-free EKG interpretation

Background

With the introduction of electronic medical records and other digital platforms, the classification and coding of different medical entities have become a complex, cumbersome task that is prone to diagnostic inconsistencies and errors. By incorporating Artificial Intelligence (AI) to a massive database of EKG records, we have developed an innovative methodology to accurately discriminate an EKG as “normal” or “abnormal”. We firmly believe that this algorithm sets up medicine on a path of complete computer-aided EKG interpretation.

Purpose

To present a viable AI-guided filter that can accurately discriminate between normal and abnormal EKG within a cardiologist-annotated EKG database.

Methods

An observational, retrospective, case-control study. Samples: A total of 140,000 randomly sampled 12-lead ECG of 10-seconds length with a sampling frequency of 500 [Hz] from Brazil (BR) and Colombia (CO) (divided as 70,000 normal and 70,000 abnormal EKG records per country dataset) were derived from the private International Telemedical System (ITMS) database from September 2018 to July 2019. Only de-identified records were used, records with artifacts were excluded. Preprocessing: Only the first 2s of each short lead and 9s of the long lead were considered. This data includes mobile (MOB) and transtelephonic (TTP) EKGs (50/50 ratio). Limb leads I, II and III and precordial leads V1, V2, V3 and V5 were used. The mean was removed from each lead. Training Sets: Four models were trained as depicted in the figure below. Each training dataset has 25,000 Normal and 25,000 Abnormal records, where 10% of the total records were used as a validation set. The test sets included 10,000 normal, and 10,000 abnormal records each. Testing and Class Assigning: An inception convolutional neural network was implemented; Each model was tested with 5,000 normal and 5,000 abnormal records of the corresponding country and transmission type with which they were trained. “Normal” or “Abnormal” labels were assigned to each EKG record and were compared to the cardiologists' reports; performance indicators (accuracy, sensitivity, and specificity) were calculated for each model.

Results

An overall accuracy of 82.4%; sensitivity of 88.7%; and specificity of 76.2% was achieved amongst the 4 testing models (Separate results of each training set are shown below).

Conclusion(s)

AI enables the interpretation of digital EKG records to be exercised in an organized, accurate, and straightforward manner, taking into consideration the multiple potential entities that can be diagnosed through this historical triage tool. By quickly identifying the normal records, the cardiologist is able to invest efforts in treating patients in a timely manner.

Funding Acknowledgement

Type of funding source: None

Moving in sync – concordance betweena artificial intelligence and cardiologist on detecting normal electrocardiograms

Background

Merging modern technologies with classic diagnostic tests often results in a sense of insecurity within the medical community, particularly so with potentially life-saving studies such as the electrocardiogram (EKG). In order to provide a greater sense of trust between Artificial Intelligence (AI) and cardiologists, we provide an AI-driven algorithm capable of accurately and reliably characterize an EKG as normal within a highly complex, cardiologist-reviewed EKG database and report the degree of concordance between this machine vs physician scenario.

Purpose

To provide a dependable and accurate AI algorithm that conducts EKG interpretation in a cardiologist-tier manner.

Methods

The International Telemedical System (ITMS) developed and tested an EKG assessing AI algorithm and incorporated it into the workflow of their Telemedicine Integrated Platform, a digital EKG reading program where cardiologists continuously report their findings remotely in real-time. During the month of April 2,019; 35 ITMS cardiologists reported a grand total of 61,441 EKG records, later submitting them to the AI algorithm implemented through the “One Click Report” process. Through this simple 2-step approach, the algorithm provides a suggestion of “Normal” or “Abnormal” to the cardiologist based on the patterns of the fiducial points included in said EKG reports. Confirmation of these suggestions by the cardiologists ensued.

Results

Overall, cardiologists confirmed 23,213 out of 25,013 AI outputs for “Normal” EKGs, demonstrating a concordance of 92.8% for Normal diagnosis.

Conclusion

Through this methodology, we provide an AI technology that can be reliably applied and trusted in EKG digital platforms to identify and suitably label a normal EKG. Further testing will accrue into a multi label algorithm compatible with abnormal cardiovascular entities, potentially precluding the role of the cardiologist for triaging, particularly in the prehospital setting. We anticipate that this approach will become a promising methodology in modern cardiology practice.

Funding Acknowledgement

Type of funding source: None

Enriching artificial intelligence ST-elevation myocardial infarction (STEMI) detection algorithms with differential diagnoses

Background

STEMI outcomes, although improved with systems of care, are hamstrung by delayed presentation and prevaricates of a 12-lead ECG. We report an artificial intelligence (AI) guided, single lead EKG algorithm for a self-administered tool to reliably detect STEMI and trigger ambulance dispatch.

Purpose

To provide a reliable and improved AI-guided Single Lead EKG methodology.

Methods

From our cardiologist-annotated repository, we assigned a dataset of 11,118 classified ECG. Ontology organized 5 groups apportioned for an interclass balance among commoner STEMI differential diagnoses. This anonymous, pre-classified data included 5,549 STEMI, 1,391 normal, 1,393 Bundle Branch Block, 1,393 non-specific ST-T changes and 1,392 miscellaneous. Each ECG was fragmented into individual 1-lead strips. Algorithm: 1-D Convolutional Neural Networks. Gender and age were included before the last dense layer. Training and Testing: Preset 90% dataset (10,008 ECG) train, 10% test (1,110 ECG). Statistical Analysis and ROC curves: Digitized dataset, 500 samples/second, 10s duration, total 5,000 samples per lead. Statistical mean for each lead was calculated and subtracted from the original lead. Statistical values and ROC curves were assessed.

Results

Most Accurate: Lead V2 – 91%; Most Sensitive: Lead I – 92% Most Specific: Lead III – 96%. Best AUC: Lead V2 – 91%.

Conclusions

Incorporating subtypes of STEMI differential diagnosis enriches the single lead AI algorithm. Validating the derived algorithm with our entire database of 18 million ECG will further strengthen the results.

Funding Acknowledgement

Type of funding source: None

Baby steps in the path of modifying the role of cardiologists for interpreting EKG for AMI

Background

EKG interpretation is slowly transitioning to a physician-free, Artificial Intelligence (AI)-driven endeavor. Our continued efforts to innovate follow a carefully laid stepwise approach, as follows: 1) Create an AI algorithm that accurately identifies STEMI against non-STEMI using a 12-lead EKG; 2) Challenging said algorithm by including different EKG diagnosis to the previous experiment, and now 3) To further validate the accuracy and reliability of our algorithm while also improving performance in a prehospital and hospital settings.

Purpose

To provide an accurate, reliable, and cost-effective tool for STEMI detection with the potential to redirect human resources into other clinically relevant tasks and save the need for human resources.

Methods

Database: EKG records obtained from Latin America Telemedicine Infarct Network (Mexico, Colombia, Argentina, and Brazil) from April 2014 to December 2019. Dataset: A total of 11,567 12-lead EKG records of 10-seconds length with sampling frequency of 500 [Hz], including the following balanced classes: unconfirmed and angiographically confirmed STEMI, branch blocks, non-specific ST-T abnormalities, normal and abnormal (200+ CPT codes, excluding the ones included in other classes). The label of each record was manually checked by cardiologists to ensure precision (Ground truth). Pre-processing: The first and last 250 samples were discarded as they may contain a standardization pulse. An order 5 digital low pass filter with a 35 Hz cut-off was applied. For each record, the mean was subtracted to each individual lead. Classification: The determined classes were STEMI (STEMI in different locations of the myocardium – anterior, inferior and lateral); Not-STEMI (A combination of randomly sampled normal, branch blocks, non-specific ST-T abnormalities and abnormal records – 25% of each subclass). Training & Testing: A 1-D Convolutional Neural Network was trained and tested with a dataset proportion of 90/10; respectively. The last dense layer outputs a probability for each record of being STEMI or Not-STEMI. Additional testing was performed with a subset of the original dataset of angiographically confirmed STEMI.

Results

See Figure Attached – Preliminary STEMI Dataset Accuracy: 96.4%; Sensitivity: 95.3%; Specificity: 97.4% – Confirmed STEMI Dataset: Accuracy: 97.6%; Sensitivity: 98.1%; Specificity: 97.2%.

Conclusions

Our results remain consistent with our previous experience. By further increasing the amount and complexity of the data, the performance of the model improves. Future implementations of this technology in clinical settings look promising, not only in performing swift screening and diagnostic steps but also partaking in complex STEMI management triage.

Funding Acknowledgement

Type of funding source: None

Innovative techniques to construct powerful artificial intelligence algorithms for st-elevation myocardial infarction

Background

With the sudden advent of Artificial Intelligence (AI), incorporation of these technologies into key aspects of our working environment has become an ever so delicate task, especially so when dealing with time-sensitive and potentially lethal scenarios such as ST-Elevation Myocardial Infarction (STEMI) management. By further expanding into our successful experiences with AI-guided algorithms for STEMI detection, we implemented an innovative ensemble method into our methodology as we seek to improve the algorithm's predictive capabilities.

Purpose

Through the ensemble method, we combined two ML techniques to boost our previous experiments' accuracy and reliability.

Methods

Database: EKG records obtained from Latin America Telemedicine Infarct Network (Mexico, Colombia, Argentina, and Brazil) from April 2014 to December 2019. Dataset: Two separate datasets were used to train and test two sets of AI algorithms. The first comprised of 11,567 records and the second 7,286 records, each composed of 12-lead EKG records of 10-second length with sampling frequency of 500 Hz, including the following balanced classes: unconfirmed & angiographically confirmed STEMI (first model); angiographically confirmed STEMI only (second model); and, for both models, we included branch blocks, non-specific ST-T abnormalities, normal, and abnormal (200+ CPT codes, excluding the ones included in other classes). Label per record was manually checked by cardiologists to ensure precision (Ground truth). Pre-processing: First and last 250 samples were discarded to avoid a standardization pulse. An order 5 digital low pass filter with a 35 Hz cut-off was applied. For each record, the mean was subtracted to each individual lead. Classification: The determined classes were STEMI and Not-STEMI (A combination of randomly sampled normal, branch blocks, non-specific ST-T abnormalities and abnormal records – 25% of each subclass). Training & Testing: The last dense layer outputs a probability for each record of being STEMI or Not-STEMI. These probabilities were calculated for each model (Model 1 trained with Complete STEMI dataset and Model 2 trained with confirmed STEMI only dataset) and aggregated using the mean aggregation to generate the final label for each record. A 1-D Convolutional Neural Network was trained and tested with a dataset proportion of 90%/10%; respectively. Results are reported for both testing datasets (Complete and confirmed STEMI only records).

Results

Complete STEMI Dataset: Accuracy: 96.5% Sensitivity: 96.2% Specificity: 96.9% – Confirmed STEMI only Dataset: Accuracy: 98.5% Sensitivity: 98.3% Specificity: 98.6%'

Conclusion(s)

While Model 1 and Model 2 achieved similar performances with promising results on their own, applying a combination of both through the ensemble model exhibits a clear improvement in performance when applied to both datasets. This provides a blueprint for advanced automated STEMI detection through wearable devices.

Funding Acknowledgement

Type of funding source: None

Artificial intelligence methodology: multi-label classification of abnormal EKG records

Background

Our previous experience with Artificial Intelligence (AI)-conducted EKG characterization displayed outstanding results in fast and reliable identification of Normal EKGs within the International Telemedical System (ITMS)'s massive record repository. By expanding the array of recognizable cardiovascular entities, we upgraded our methodology to accurately discriminate an anomaly amongst a highly complex database of EKG records.

Purpose

To present a feasible AI-guided filter that can accurately discriminate and classify Normal and Abnormal EKG records within a multilabeled cardiologist-annotated EKG database.

Methods

ITMS developed and tested the “One Click”' process, a “Normal/Abnormal” EKG assessing AI algorithm, by incorporating it into their digital EKG reading platform where cardiologists continuously report their findings remotely in real time. To ameliorate the diagnostic range of the algorithm, a separate dataset of 121,641 12-lead EKG records was consolidated from the ITMS database from October 2011 to January 2019. Only de-identified data was used. Preprocessing: The first 2s of each short lead and 9s of the long lead were considered. Limb leads I, II and III; and precordial leads V1, V2, V3, and V5 were used. The mean was removed from each lead. AI models/Classification: Two models were created and tested independently based on the method of EKG acquisition (69,852 records transtelephonic [TTP]; 52,259 mobile transmission [MOB]). Each record is categorized into six disjoint classes based on the most common types of cardiac disorders (Low/null co-occurrence pathologies in these datasets were grouped into analogous groups). Training/Testing: Distribution of both sets per transmission type was performed through a greedy algorithm, which identified multiple diagnoses per EKG record and labeled it separately to the corresponding group, ensuring sufficient samples per class. Detailed class distribution is shown below. An inception convolutional neural network was implemented; “Normal” or “Abnormal” labels were assigned to each EKG record independently and were compared to cardiologists' reports; performance indicators were calculated for each model and group.

Results

MOB model accrued an average accuracy of 86.7%; sensitivity of 90.5%; and specificity of 83.9%. TTP model yielded an average accuracy of 77.2%; sensitivity of 91.1%; and specificity of 69.4% (Lower values were attributed to the “Ventricular Complexes” group, which challenged the algorithm by having a smaller ratio of abnormal exams). Detailed results of each training set are shown below.

Conclusion

Providing an effective and reliable multilabel-capable EKG triaging tool remains a challenging but attainable goal. Continuous systematic enhancement of our AI-driven methodology has led us to satisfactory, yet imperfect results which compel us to further study and improve our efforts to provide a trustworthy cardiologist-friendly triage device.

Funding Acknowledgement

Type of funding source: None

Quality of Education over Level of Education in Associations with Executive Functioning in Older American Indians

Objective

Education is strongly associated with neuropsychological performance, with many tests normed to education level. Educational achievement, often measured in years of school completed, is associated with executive functioning and overall health. However, education level does not account for the quality of education, which may be a better educational index for racial/ethnic minority groups. Older American Indians (AIs) suffer disproportionate rates of many age-related chronic diseases, and many have suffered traumatic educational experiences, such as mandatory boarding schools away from families and tribes that were focused on assimilation rather than subject knowledge. This study investigated the relationship between executive functioning and indices of the quality of education in older AIs.

Participants and Method

Participants were recruited from a southwestern urban area, who self-identified as AI, aged 55 to 80 years (n = 42). Participants who completed questionnaires, interviews, achievement (Wide Range Achievement Tests, WRAT), and executive functioning tests (WASI and verbal semantic fluency, VSF) over two sessions were included in this analysis.

Results

Educational achievement was positively correlated with executive functioning measures. WRAT Reading scores were significantly associated with WASI Matrix scores. WRAT reading scores were also significantly associated with VSF animals and plants, but not VSF fruits and vegetables scores. Education level was positively correlated with WASI Matrix, WASI Similarities, and VSF plants scores, but to a lesser extent than WRAT Reading scores.

Conclusions

Our findings suggest that, for AIs, education quality may be a better measure of education than education level. WRAT reading, as an index of education quality, had a stronger correlation with executive functioning measures. These results are consistent with the literature on other racial/ethnic groups and highlight the need for further exploration of cultural context.

Health Literacy is Linked to Executive Functioning in Older American Indians

Objective

Health literacy (HL), the ability to understand one’s own medical conditions and navigate the healthcare system, is strongly associated with both educational achievement and executive functioning. Many American Indians (AIs) have experienced trauma in early education, such as forced removal into harsh boarding school systems; these experiences may impact their educational achievement, and subsequently, HL. Further, AIs disproportionately experience a myriad of chronic diseases. The goal of this study is to investigate the relationship between HL and executive functioning in older AIs.

Participants and Method

Participants were recruited from a southwestern urban area, who self-identified as AIs, aged 55 to 80 (n = 43). Participants were asked to complete a series of questionnaires, interviews, and neuropsychological tests over two sessions, the second session was optional. This poster investigates the data from participants who completed both sessions. We created a composite variable of HL measures, (Rapid Estimate of Adult Literacy in Medicine, Newest Vital Sign, and the Short Test of Functional Health Literacy in Adults verbal and numeracy z-scores).

Results

HL was positively correlated with both WASI Matrix Reasoning and WASI Similarities. Similarly, HL was positively correlated with Verbal Semantic Fluency (VSF) – Animals and Plants. However, there was no significant correlation between HL and VSF – Fruits and Vegetables or HL and Trails Making – B.

Conclusions

These findings suggest that HL and executive functioning are linked in older AIs. Discrepancies between the executive functioning tests (VSF – Animals, Fruits/Vegetables, and Plants) may be due to resources, culture, and education quality. These findings highlight the need for further investigation of cultural context and the accompanying development of culturally appropriate measures.

Does Literacy Moderate the Relationship between Age of Migration and Cognitive Change: Results from the Washington Heights-Inwood Community Aging Project (WHICAP)

Objective

The “healthy immigrant effect” pertains to findings that Hispanics/Latinos born outside of the US tend to be physically and emotionally healthier than individuals born in the US. However, immigrant Latino groups residing in the US have higher incidences of dementia (Tang et al., 2001). Sex/gender and years of education have been found to moderate the relationship between age of migration and cognitive functioning among immigrants born in Mexico (Garcia et al., 2017; Hill et al., 2012). While years of education has been the focus of many studies, literacy (ability to read/write) has been less frequently explored as a moderator of age of migration and cognitive change. We investigated the effect of age of migration on cognitive trajectory in a diverse Latino population and explored whether literacy influences the association between age of immigration and cognitive change. We hypothesize that literacy (ability to read/write) will buffer the effects of age of migration on cognitive (memory, language, motor speed, visuospatial) trajectory.

Participants and Method

Age at baseline, English fluency, country of birth, sex/gender, and years of education were included as time-invariant covariates and literacy was tested as a moderator via multiple group modeling.

Results

Results show that age of migration is marginally and inversely associated with baseline cognitive performance (p < .01). Thus, individuals who immigrated at an older age had lower baseline cognitive scores than their counterparts. Age of migration was not associated with cognitive change. Independent of covariates, literacy did not buffer the negative effects of later age at migration on cognitive function.

Conclusions

Results suggest that literacy confers a small advantage in premorbid cognition, but does not protect against cognitive decline over time. These findings also suggest that adults who immigrate at an older age present with lower cognitive scores at baseline, but do not experience faster rates of cognitive change.

References

Tang, M. X., Cross, P., Andrews, H., Jacobs, D. M., Small, S., Bell, K., ... & Mayeux, R. (2001). Incidence of AD in African-Americans, Caribbean Hispanics, and Caucasians in northern Manhattan. Neurology, 56, 49-56. Garcia, M. A., Reyes, A. M., Downer, B., Saenz, J. L., Samper-Ternent, R. A., & Raji, M. (2018). Age of migration and the incidence of cognitive impairment: A cohort study of elder Mexican-Americans. Innovation in aging, 1, igx037. https://doi.org/10.1093/geroni/igx037. Hill, T. D., Angel, J. L., Balistreri, K. S., & Herrera, A. P. (2012). Immigrant Status and Cognitive Functioning in Late Life: An Examination of Gender Variations in the Healthy Immigrant Effect. Social Science & Medicine (1982), 75, 2076–2084. http://doi.org/10.1016/j.socscimed.2012.04.005.

Structural determination of bilayer graphene on SiC(0001) using synchrotron radiation photoelectron diffraction

In recent years there has been growing interest in the electronic properties of ‘few layer’ graphene films. Twisted layers, different stacking and register with the substrate result in remarkable unconventional couplings. These distinctive electronic behaviours have been attributed to structural differences, even if only a few structural determinations are available. Here we report the results of a structural study of bilayer graphene on the Si-terminated SiC(0001) surface, investigated using synchrotron radiation-based photoelectron diffraction and complemented by angle-resolved photoemission mapping of the electronic valence bands. Photoelectron diffraction angular distributions of the graphene C 1s component have been measured at different kinetic energies and compared with the results of multiple scattering simulations for model structures. The results confirm that bilayer graphene on SiC(0001) has a layer spacing of 3.48 Å and an AB (Bernal) stacking, with a distance between the C buffer layer and the first graphene layer of 3.24 Å. Our work generalises the use of a versatile and precise diffraction method capable to shed light on the structure of low-dimensional materials.

Chitosan-coated mesoporous MIL-100(Fe) nanoparticles as improved bio-compatible oral nanocarriers

Nanometric biocompatible Metal-Organic Frameworks (nanoMOFs) are promising candidates for drug delivery. Up to now, most studies have targeted the intravenous route, related to pain and severe complications; whereas nanoMOFs for oral administration, a commonly used non-invasive and simpler route, remains however unexplored. We propose here the biofriendly preparation of a suitable oral nanocarrier based on the benchmarked biocompatible mesoporous iron(III) trimesate nanoparticles coated with the bioadhesive polysaccharide chitosan (CS). This method does not hamper the textural/structural properties and the sorption/release abilities of the nanoMOFs upon surface engineering. The interaction between the CS and the nanoparticles has been characterized through a combination of high resolution soft X-ray absorption and computing simulation, while the positive impact of the coating on the colloidal and chemical stability under oral simulated conditions is here demonstrated. Finally, the intestinal barrier bypass capability and biocompatibility of CS-coated nanoMOF have been assessed in vitro, leading to an increased intestinal permeability with respect to the non-coated material, maintaining an optimal biocompatibility. In conclusion, the preservation of the interesting physicochemical features of the CS-coated nanoMOF and their adapted colloidal stability and progressive biodegradation, together with their improved intestinal barrier bypass, make these nanoparticles a promising oral nanocarrier.

Understanding the growth mechanism of graphene on Ge/Si(001) surfaces

The practical difficulties to use graphene in microelectronics and optoelectronics is that the available methods to grow graphene are not easily integrated in the mainstream technologies. A growth method that could overcome at least some of these problems is chemical vapour deposition (CVD) of graphene directly on semiconducting (Si or Ge) substrates. Here we report on the comparison of the CVD and molecular beam epitaxy (MBE) growth of graphene on the technologically relevant Ge(001)/Si(001) substrate from ethene (C₂H₄) precursor and describe the physical properties of the films as well as we discuss the surface reaction and diffusion processes that may be responsible for the observed behavior. Using nano angle resolved photoemission (nanoARPES) complemented by transport studies and Raman spectroscopy as well as density functional theory (DFT) calculations, we report the direct observation of massless Dirac particles in monolayer graphene, providing a comprehensive mapping of their low-hole doped Dirac electron bands. The micrometric graphene flakes are oriented along two predominant directions rotated by 30° with respect to each other. The growth mode is attributed to the mechanism when small graphene “molecules” nucleate on the Ge(001) surface and it is found that hydrogen plays a significant role in this process.

Tau pathology-mediated presynaptic dysfunction

Brain tauopathies are characterized by abnormal processing of tau protein. While somatodendritic tau mislocalization has attracted considerable attention in tauopathies, the role of tau pathology in axonal transport, connectivity and related dysfunctions remains obscure. We have previously shown using the squid giant synapse that presynaptic microinjection of recombinant human tau protein (htau42) results in failure of synaptic transmission. Here, we evaluated molecular mechanisms mediating this effect. Thus, the initial event, observed after htau42 presynaptic injection, was an increase in transmitter release. This event was mediated by calcium release from intracellular stores and was followed by a reduction in evoked transmitter release. The effect of htau42 on synaptic transmission was recapitulated by a peptide comprising the phosphatase-activating domain of tau, suggesting activation of phosphotransferases. Accordingly, findings indicated that htau42-mediated toxicity involves the activities of both GSK3 and Cdk5 kinases.

Distinct X-chromosome SNVs from some sporadic AD samples

Sporadic Alzheimer disease (SAD) is the most prevalent neurodegenerative disorder. With the development of new generation DNA sequencing technologies, additional genetic risk factors have been described. Here we used various methods to process DNA sequencing data in order to gain further insight into this important disease. We have sequenced the exomes of brain samples from SAD patients and non-demented controls. Using either method, we found a higher number of single nucleotide variants (SNVs), from SAD patients, in genes present at the X chromosome. Using the most stringent method, we validated these variants by Sanger sequencing. Two of these gene variants, were found in loci related to the ubiquitin pathway (UBE2NL and ATXN3L), previously do not described as genetic risk factors for SAD.

Presence of gapped silicene-derived band in the prototypical (3 × 3) silicene phase on silver (111) surfaces

By mapping the low-energy electronic dynamics using angle resolved photoemission spectroscopy (ARPES), we have shed light on essential electronic characteristics of the (3 × 3) silicene phase on Ag(111) surfaces. In particular, our results show a silicene-derived band with a clear gap and linear energy-momentum dispersion near the Fermi level at the Γ symmetry point of the (3 × 3) phase at several distinctive Brillouin zones. Moreover, we have confirmed that the large buckling of ~0.7 Å of this silicene structure induces the opening of a gap close to the Fermi level higher than at least 0.3 eV, in agreement with recent reported photoemission results. The two-dimensional character of the charge carriers has also been revealed by the photon energy invariance of the gapped silicene band, suggesting a limited silicene-silver hybridization, in disagreement with recent density-functional theory (DFT) predictions.

GSK-3β overexpression causes reversible alterations on postsynaptic densities and dendritic morphology of hippocampal granule neurons in vivo

Adult hippocampal neurogenesis (AHN) is crucial for the maintenance of hippocampal function. Several neurodegenerative diseases such as Alzheimer's disease (AD) are accompanied by memory deficits that could be related to alterations in AHN. Here, we took advantage of a conditional mouse model to study the involvement of glycogen synthase kinase-3β (GSK-3β) overexpression (OE) in AHN. By injecting GFP- and PSD95-GFP-expressing retroviruses, we have determined that hippocampal GSK-3β-OE causes dramatic alterations in both dendritic tree morphology and post-synaptic densities in newborn neurons. Alterations in previously damaged neurons were reverted by switching off the transgenic system and also by using a physiological approach (environmental enrichment) to increase hippocampal plasticity. Furthermore, comparative morphometric analysis of granule neurons from patients with AD and from GSK-3β overexpressing mice revealed shared morphological alterations. Taken together, these data indicate that GSK-3β is crucial for hippocampal function, thereby supporting this kinase as a relevant target for the treatment of AD.

Alternative immunomodulatory strategies for xenotransplantation: CD40/154 pathway-sparing regimens promote xenograft survival

Immunosuppressive therapies that block the CD40/CD154 costimulatory pathway have proven to be uniquely effective in preclinical xenotransplant models. Given the challenges facing clinical translation of CD40/CD154 pathway blockade, we examined the efficacy and tolerability of CD40/CD154 pathway-sparing immunomodulatory strategies in a pig-to-nonhuman primate islet xenotransplant model. Rhesus macaques were rendered diabetic with streptozocin and given an intraportal infusion of ≈ 50 000 islet equivalents/kg wild-type neonatal porcine islets. Base immunosuppression for all recipients included maintenance therapy with belatacept and mycophenolate mofetil plus induction with basiliximab and LFA-1 blockade. Cohort 1 recipients (n = 3) were treated with the base regimen alone; cohort 2 recipients (n = 5) were additionally treated with tacrolimus induction and cohort 3 recipients (n = 5) were treated with alefacept in place of basiliximab, and more intense LFA-1 blockade. Three of five recipients in both cohorts 2 and 3 achieved sustained insulin-independent normoglycemia (median rejection-free survivals 60 and 111 days, respectively), compared to zero of three recipients in cohort 1. These data show that CD40/CD154 pathway-sparing regimens can promote xenoislet survival. Further optimization of these strategies is warranted to aid the clinical translation of islet xenotransplantation.

Islet xenotransplantation using gal-deficient neonatal donors improves engraftment and function

Significant deficiencies in understanding of xenospecific immunity have impeded the success of preclinical trials in xenoislet transplantation. Although galactose-α1,3-galactose, the gal epitope, has emerged as the principal target of rejection in pig-to-primate models of solid organ transplant, the importance of gal-specific immunity in islet xenotransplant models has yet to be clearly demonstrated. Here, we directly compare the immunogenicity, survival and function of neonatal porcine islets (NPIs) from gal-expressing wild-type (WT) or gal-deficient galactosyl transferase knockout (GTKO) donors. Paired diabetic rhesus macaques were transplanted with either WT (n = 5) or GTKO (n = 5) NPIs. Recipient blood glucose, transaminase and serum xenoantibody levels were used to monitor response to transplant. Four of five GTKO versus one of five WT recipients achieved insulin-independent normoglycemia; transplantation of WT islets resulted in significantly greater transaminitis. The WT NPIs were more susceptible to antibody and complement binding and destruction in vitro. Our results confirm that gal is an important variable in xenoislet transplantation. The GTKO NPI recipients have improved rates of normoglycemia, likely due to decreased susceptibility of xenografts to innate immunity mediated by complement and preformed xenoantibody. Therefore, the use of GTKO donors is an important step toward improved consistency and interpretability of results in future xenoislet studies.

Intra- and extracellular protein interactions with tau

Tau is a sticky protein mainly expressed by neurons, which may be found in different subcellular fractions or outside the cell. Tau is mainly associated to microtubules in the cytoplasm, although besides tubulin, tau can also bind to other proteins and to itself to form different polymers, some of which are relevant in pathological disorders. In this short review, we have revised some of the interactions involving tau, both inside or outside of the cell. Different regions of tau are involved in these interactions and some of them are more conserved throughout evolution than others.

Expression of frontotemporal dementia with parkinsonism associated to chromosome 17 tau induces specific degeneration of the ventral dentate gyrus and depressive-like behavior in mice

When bearing certain frontotemporal dementia with parkinsonism (FTDP) mutations, overexpression of human tau resulted in a decrease of the dentate gyrus ventral blade, apparently due to a reduction in the proliferation of neuronal precursors and an increase in neuronal cell death. This degenerative process was accompanied by a dramatic increase in behavioral despair, as evident in the Porsolt swim test. Interestingly, we observed an increase in GABAergic innervation in the molecular layer of the dorsal dentate gyrus but not in the ventral domain. We suggest that this increase in GABAergic innervation reflects a compensatory neuroprotective response to the overexpression of toxic tau, which may prevent or delay degeneration in the dorsal blade of the dental gyrus. Finally, we suggest that this transgenic mouse, which overexpresses human FTPD tau, may serve as a useful model to study specific functions of the ventral dentate gyrus.

Nondenaturing electrophoresis as a tool to investigate tubulin complexes

A protein molecule may exist as a monomer, homo-oligomer, or hetero-oligomer in a multiprotein complex. One-dimensional (1-D) native electrophoresis has long been used to characterize tubulins and their complexes. In this chapter, we describe the simplest way to identify the state of aggregation of commercial or homemade tubulins for further studies based on 1-D electrophoresis under nondenaturing conditions. We present a series of detailed protocols that can be used to analyze the maturation of alpha- and beta-tubulins and to identify the complexes formed during the folding and dimerization pathway as well as their stability.

Tau phosphorylation, aggregation, and cell toxicity

Protein aggregation takes place in many neurodegenerative disorders. However, there is a controversy about the possible toxicity of these protein aggregates. In this review, this controversy is discussed, focussing on the tau aggregation that takes place in those disorders known as tauopathies.

A novel GSK-3beta inhibitor reduces Alzheimer's pathology and rescues neuronal loss in vivo

Amyloid deposits, neurofibrillary tangles, and neuronal cell death in selectively vulnerable brain regions are the chief hallmarks in Alzheimer's (AD) brains. Glycogen synthase kinase-3 (GSK-3) is one of the key kinases required for AD-type abnormal hyperphosphorylation of tau, which is believed to be a critical event in neurofibrillary tangle formation. GSK-3 has also been recently implicated in amyloid precursor protein (APP) processing/Abeta production, apoptotic cell death, and learning and memory. Thus, GSK-3 inhibition represents a very attractive drug target in AD and other neurodegenerative disorders. To investigate whether GSK-3 inhibition can reduce amyloid and tau pathologies, neuronal cell death and memory deficits in vivo, double transgenic mice coexpressing human mutant APP and tau were treated with a novel non-ATP competitive GSK-3beta inhibitor, NP12. Treatment with this thiadiazolidinone compound resulted in lower levels of tau phosphorylation, decreased amyloid deposition and plaque-associated astrocytic proliferation, protection of neurons in the entorhinal cortex and CA1 hippocampal subfield against cell death, and prevention of memory deficits in this transgenic mouse model. These results show that this novel GSK-3 inhibitor has a dual impact on amyloid and tau alterations and, perhaps even more important, on neuronal survival in vivo further suggesting that GSK-3 is a relevant therapeutic target in AD.

Isolation of microtubules and microtubule proteins

This unit describes various protocols for the isolation and purification of the main constituents of microtubules, chiefly alpha- and beta-tubulin, and the most significant microtubule associated proteins (MAPs), specifically MAP1A, MAP1B, MAP2, and tau. We include a classical isolation method for soluble tubulin heterodimer as the first basic purification protocol. In addition, we show how to analyze the tubulin and MAPs obtained after a phosphocellulose chromatography purification procedure. This unit also details a powerful and simple method to determine the native state of the purified tubulin based on one-dimensional electrophoresis under nondenaturing conditions (UNIT 6.5). The last protocol describes the application of a new technique that allows visualizing the quality of polymerized microtubules based on atomic force microscopy (AFM).

Lithium, a potential protective drug in Alzheimer's disease

Alzheimer's disease is characterized by the presence of two histopathological aberrant structures, the senile plaques and the neurofibrillary tangles. The main component of these tangles is the cytoskeletal protein tau in hyperphosphorylated form. Since a main tau kinase is glycogen synthase kinase 3 (GSK-3), the use of specific GSK-3 inhibitors, like lithium, could be a potential therapy in Alzheimer's disease. In this short article, we have done a review on tau phosphorylation in Alzheimer's disease and other tauopathies, and on the inhibition of kinases like GSK-3, involved in tau modification.

Extracellular tau promotes intracellular calcium increase through M1 and M3 muscarinic receptors in neuronal cells

Extracellular tau promotes an increase in the level of intracellular calcium in cultured neuronal cells. We have found that such increase is impaired in the presence of antagonists of muscarinic receptors. In order to identify the nature of those receptors, we have tested the effect of different specific muscarinic receptor antagonists on tau promoted calcium increase. Our results indicate that the increase does not take place in the presence of antagonists of muscarinic (mainly M1 and M3) receptors. A similar increase in intracellular calcium was found in non-neuronal cells transfected with cDNA of M1 and M3 muscarinic receptors when tau was added. These results suggest that observed effect of tau protein on neuronal (neuroblastoma and primary cultures of hippocampal and cortical neurons) cells is through M1 and M3 muscarinic receptors. Therefore blocking M1 and for M3 receptors, by using specific receptor antagonists, can prevent that tau toxic effect that could take place in tauopathies.

Tauopathies

Tau is a microtubule-associated protein predominantly expressed in nerve cells that promote microtubule assembly and microtubule stabilization. Tau is a cytosolic protein mainly present in axons and involved in anterograde axonal transport. In several neurodegenerative diseases, as for example Alzheimer's disease, tau metabolism is altered. Thus, alterations in the amount of the tau protein, missense mutations, posttranscriptional modifications like phosphorylation, aberrant tau aggregation or a different expression of some of its isoforms could provoke pathological effects resulting in the appearance of neuronal disorders known as tauopathies. The purpose of this work is to review the possible mechanisms for tau alterations that could lead to the onset of tau pathology. First we will focus on tau turnover, then on tau phosphorylation and, finally, on tau aggregation.