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1.
Annals of Surgical Oncology ; 29(SUPPL 2):S424, 2022.
Article in English | EMBASE | ID: covidwho-1928243

ABSTRACT

INTRODUCTION: The COVID-19 pandemic peaked in New York City in Spring 2020. From March 20-May 4, all elective operations were suspended due to the number of hospitalized Covid-19 patients. In this study, we sought to describe patterns of care for surgical patients during this time. METHODS: An IRB approved retrospective review was performed of patients who presented to our hospital system from March-May 2020, diagnosed with breast cancer or atypia. RESULTS: We identified 75 patients with breast cancer and 19 patients with atypia. According to standard of care, 55/75 (73%) cancer patients would have undergone upfront surgery. 2/55 (4%) instead were treated with neoadjuvant chemotherapy (NCT), 34/55 (62%) with neoadjuvant endocrine therapy (NET), and 19/55 (34%) had no immediate treatment. 12/19 (63%) with no immediate treatment had DCIS only. 7 had invasive disease, and mean days from diagnosis to surgery was 63 days (range 47-79). One patient had a positive node. A total of 20/75 (27%) patients needed NCT based on advanced stage or molecular profile and had no delay in starting treatment. Of the 34 NET patients, 5 (14.7%) were treated for approximately 6 months and 24 (70.6%) were treated for approximately 6 weeks as a bridge to surgery only. Of the 34 patients who received NET, 5 (14.7%) had an apparent decrease in T stage: 3 patients with clinical T1 disease had no residual disease. 2 had clinical T2 and ultimately had pathological T1 disease. Of the 19 patients with atypia, 6 (31.6%) started chemoprevention preoperatively and 1 patient was already receiving it for a previous LCIS diagnosis. All underwent subsequent surgery and 1/19 (5.3%) patients was upstaged to DCIS. CONCLUSIONS: During the peak of Covid-19, with delay of surgery, we observed an increased utilization of NET when compared to usual treatment patterns, with no apparent adverse effects. While further studies are needed to validate our results, we may see more wide spread use of NET in the future to temporize patients as needed.

2.
Annals of Surgical Oncology ; 29(SUPPL 2):424-424, 2022.
Article in English | Web of Science | ID: covidwho-1848857
3.
International Journal of Technologies in Higher Education ; 19(1):76-90, 2022.
Article in French | Web of Science | ID: covidwho-1822652

ABSTRACT

The impact of online courses on students' motivation, academic engagement, and satisfaction of psychological needs during the first waves of COVID-19 was examined with a questionnaire administered to a sample of teachers in training (n = 272) of a French-speaking university in Montreal (Canada). Analysis of the data reveals that participants were less motivated, less engaged and had more problems meeting their psychological needs in online courses than in traditional face-to-face classes. These results are discussed from the perspective of literature currently available on the subject and future developments in online training.

4.
Mov Disord ; 37(6): 1289-1294, 2022 06.
Article in English | MEDLINE | ID: covidwho-1763265

ABSTRACT

BACKGROUND: Telehealth has been widely adopted in providing Parkinson's disease care during the coronavirus disease 2019 pandemic. OBJECTIVE: The aim of this study was to survey people living with Parkinson's disease (PwPD) about their attitudes toward and utilization of telehealth services. METHODS: A survey was administered to PwPD via Parkinson's Foundation and Columbia University mailing lists. RESULTS: Of 1,163 responses, 944 complete responses were analyzed. Telehealth awareness was 90.2% (850/942), and utilization was 82.8% (780/942). More than 40% of PwPD were equally or more satisfied with telehealth compared with in-person visits in all types of services used. The highest satisfaction was observed in speech-language pathology appointments (78.8%, 52/66) followed by mental health services (69.2%, 95/137). CONCLUSIONS: In selected circumstances and indications, such as speech-language pathology and mental health services, telehealth may be a useful tool in the care of PwPD beyond the coronavirus disease 2019 pandemic. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson Movement Disorder Society.


Subject(s)
COVID-19 , Parkinson Disease , Telemedicine , Attitude , Humans , Parkinson Disease/psychology , Parkinson Disease/therapy , Surveys and Questionnaires
5.
2021 IEEE International Conference on Big Data, Big Data 2021 ; : 5620-5625, 2021.
Article in English | Scopus | ID: covidwho-1730883

ABSTRACT

The COVID-19 pandemic has brought a devastating impact on human health across the globe, and people are still observing face-masking as a preventive measure to contain the spread of COVID-19. Coughing is one of the major transmission mediums of COVID-19, and early cough detection could play a significant r ole i n p reventing t he s pread o f t his life-threatening virus. Many approaches have been proposed for developing systems to detect coughing and other respiratory symptoms in literature, but earable devices are not well-studied and investigated for respiratory symptom detection. In this work, we posited an acoustic research prototype (earable device) - eSense that has acoustic and IMU sensors embedded into user-convenient earbuds to address the following issues: (i) feasibility of the earables in detecting respiratory symptoms, and (ii) scalability of trained machine learning models in the presence of unseen data samples. We performed experimentation with both shallow and deep learning models on the eSense collected data samples. We observed that the deep learning model outperforms the shallow learning models achieving 97% accuracy. Furthermore, we investigated the scalability of the deep learning model on unseen datasets and noticed that the performance of the deep learning model deteriorates when trained on a particular dataset and tested on an unseen dataset. To mitigate such challenges, we postulated an adversarial domain adaptation technique that helps improve the performance of our respiratory symptoms detection framework by a substantial margin. © 2021 IEEE.

6.
Blood ; 138:4058, 2021.
Article in English | EMBASE | ID: covidwho-1582388

ABSTRACT

[Formula presented] PV, NR and MMP contributed equally Introduction Patients with red blood cell disorders (RBCD), chronic life threating multisystemic disorders in their severe forms, are likely to be at increased risk of complications from SARS-Cov-2 (Covid-19), but evidence in this population is scarce due to its low frequency and heterogeneous distribution. ERN-EuroBloodNet, the European Reference Network in rare hematological disorders, established a European registry to determine the impact of COVID-19 on RBCD patients and identify risk factors predicting severe outcomes. Methods The ERN-EuroBloodNet registry was established in March 2020 by Vall d'Hebron Research Institute based on REDcap software in accordance with the Regulation (EU) 2016/679 on personal data. The local Research Ethics Committee confirmed that the exceptional case of the pandemic justifies the waiver of informed consent. The ERN-EuroBloodNet registry on RBCD and COVID-19 is endorsed by the European Hematology Association (EHA). Eligible patients had confirmed RBCD and COVID-19. Data collected included demographics, diagnosis, comorbidities, treatments, and COVID-19 (severity grade, clinical manifestations, acute events, treatments, hospitalization, intensive care unit, death). For analysis of COVID-19 severity, two groups were established 1) Mild: asymptomatic or mild symptoms without clinical pneumonia and 2) Severe: pneumonia requiring oxygen/respiratory support and/or admission to intensive care unit. Continuous variables were compared using the Wilcoxon rank-sum test or Kruskall Wallis test, while categorical variables were analyzed using the Chi-square test or Fisher's Exact test. Relevant factors influencing disease or severity were examined by the logistic regression adjusted for age. Results As of June 2021, 42 medical centers from 10 EU countries had registered 373 patients: 191 Sickle cell disease (SCD), 156 Thalassemia major and intermedia (THAL) and 26 other RBCD. 84% of the SCD patients were reported by Spain, Belgium, Italy and The Netherlands and 92% of the THAL patients by Italy and Greece. The mean age of SCD was lower (22.5y) than of THAL (39.6y) with pediatric population accounting for 50.5% in SCD and 9% in THAL (p <0.001). Splenectomy and comorbidities were higher in THAL (51.3% and 65.8%) than in SCD (16% and 48.1%) (p<0.001, p=0.002). Age and BMI correlated with COVID-19 severity, as described in the general population (p=0.002, p<0.001). Fig 1 shows age distribution and COVID-19 severity by disease severity groups. The mean age for severe COVID-19 was lower in patients with severe SCD (SS/SB0 vs SC/SB+: 23.3y vs 67.5y) and THAL (major vs intermedia: 43.5 vs 51.3y) (p<0.001). Potential risk factors such as elevated ferritin, current chelation or history of splenectomy did not confer additional risk for developing severe COVID-19 in any patient group. Only diabetes as a comorbidity correlated with severity grade in SCD (p=0.011) and hypertension in THAL (p=0.014). While severe COVID-19 infection in SCD was associated with both ACS (p<0.001) and kidney failure requiring treatment (p=0.001), this was not predicted by a history of previous ACS or kidney disease in steady state. Overall, 14.8% RBC patients needed oxygen/respiratory support, 4.4% were admitted to ICU with an overall mortality rate of 0.8% (no deaths were registered in pediatric age), much lower than reported in other similar cohorts. Discussion Results obtained so far show that severe COVID-19 occurs at younger ages in more aggressive forms of SCD and THAL. Current preventive approaches (shielding, vaccinations) focus on age over disease severity. Our data highlights the risk of severe COVID-19 infection in some young patients, particularly those with SS/SB0 SCD, suggesting that immunization should be considered in this pediatric group as well. Results between similar sized cohorts of RBCD patients vary between each other and those presented here, highlighting the importance of collecting all of these small cohorts together to ensure adequate statistical p wer so that definitive risk factors (eg. age, genotype, comorbidities) can be reliably identified and used to guide management of patients with these rare disorders in the light of the ongoing pandemic. [Formula presented] Disclosures: Longo: Bristol Myers Squibb: Honoraria;BlueBird Bio: Honoraria. Bardón-Cancho: Novartis Oncology Spain: Research Funding. Flevari: PROTAGONIST COMPANY: Research Funding;ADDMEDICA: Consultancy, Research Funding;BMS: Research Funding;IMARA COMPANY: Research Funding;NOVARTIS COMPANY: Research Funding. Voskaridou: BMS: Consultancy, Research Funding;IMARA: Research Funding;NOVARTIS: Research Funding;ADDMEDICA: Consultancy, Research Funding;GENESIS: Consultancy, Research Funding;PROTAGONIST: Research Funding. Biemond: GBT: Honoraria, Research Funding, Speakers Bureau;Novartis: Honoraria, Research Funding, Speakers Bureau;Novo Nordisk: Honoraria;Celgene: Honoraria;Sanquin: Research Funding. Nur: Celgene: Speakers Bureau;Roche: Speakers Bureau;Novartis: Research Funding, Speakers Bureau. Beneitez-Pastor: Agios: Honoraria;Alexion: Honoraria;Novartis: Honoraria;Forma Therapeutics: Honoraria. Pepe: Chiesi Farmaceutici S.p.A: Other: no profit support;Bayer S.p.A.: Other: no profit support. de Montalembert: Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees;Addmedica: Membership on an entity's Board of Directors or advisory committees;BlueBirdBio: Membership on an entity's Board of Directors or advisory committees;Vertex: Membership on an entity's Board of Directors or advisory committees. Glenthøj: Agios: Consultancy;Novo Nordisk: Honoraria;Novartis: Consultancy;Alexion: Research Funding;Bluebird Bio: Consultancy;Bristol Myers Squibb: Consultancy;Saniona: Research Funding;Sanofi: Research Funding. Benghiat: Novartis: Consultancy;BMS: Consultancy. Labarque: Novartis: Consultancy;Bayer: Consultancy;Sobi: Consultancy;NovoNordisk: Consultancy;Octapharma: Consultancy. Diamantidis: Genesis Pharma: Honoraria;Uni-Pharma: Honoraria;Bristol Myers Squibb: Consultancy;IONIS Pharmaceuticals: Research Funding;NOVARTIS, Genesis Pharma SA: Research Funding. Kerkhoffs: Sanofi: Research Funding;Terumo BCT: Research Funding. Iolascon: Celgene: Other: Advisory Board;Bluebird Bio: Other: Advisory Board. Taher: Vifor Pharma: Consultancy, Research Funding;Agios Pharmaceuticals: Consultancy;Ionis Pharmaceuticals: Consultancy, Research Funding;Bristol Myers Squibb: Consultancy, Research Funding;Novartis: Consultancy, Research Funding. Colombatti: Novartis: Membership on an entity's Board of Directors or advisory committees;Global Blood Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding;Novonordisk: Membership on an entity's Board of Directors or advisory committees;Forma Therapeutics: Membership on an entity's Board of Directors or advisory committees;Addmedica: Membership on an entity's Board of Directors or advisory committees;BlueBirdBio: Research Funding. Mañú Pereira: Novartis: Research Funding;Agios Pharmaceuticals: Research Funding.

7.
BJS Open ; 5(SUPPL 1):i12, 2021.
Article in English | EMBASE | ID: covidwho-1493708

ABSTRACT

Introduction: The anaesthetic management for surgeries during the COVID-19 pandemic has posed unique challenges. Safety of all healthcare workers is an additional concern along with heightened risk to patients during General Anesthesia (GA). COVID-19 pneumonia and aerosol generation may be exacerbated during airway intervention and GA. We aimed to assess the change in the mode of anaesthesia due to the pandemic. Methods: A research consortium led by WHO Collaboration Centre for Research in Surgical Care Delivery in Low and Middle Income countries, India, conducted this retrospective cross-sectional study in 12 hospitals across the country.We compared the anaesthesia preferences during pandemic (April 2020) to a corresponding pre pandemic period (April 2019) Results: A total of 636 out of 2,162 (29.4%) and 156 out of 927 (16.8%) surgeries were performed under GA in April 2019 and April 2020 respectively, leading to a fall of 13% in usage of GA. A 5% reduction in GA and a 12% increase in the usage of regional anaesthesia was observed for cesarean sections. There was no significant change in anesthesia for laparotomies and fracture surgeries. However, 14% increase in GA usage was observed in surgeries for local soft tissue infections and necrotic tissues. Conclusion: Though overall usage of GA reduced marginally, the change was mainly contributed by anesthesia for caesarean births. The insignificant change in anaesthesia for other surgeries may be attributed to the lack of facilities for spinal anaesthesia and may reflect the risk taking behaviour of healthcare professionals in COVID-19 pandemic.

8.
Dubai Med. J. ; : 6, 2021.
Article in English | Web of Science | ID: covidwho-1448075

ABSTRACT

Background: COVID-19 patients are at increased risk of coagulopathy. This coagulopathy may be due to a severe pro-inflammatory state (cytokine storm) and/or by viral sepsis. This can sometimes lead to consumption coagulopathy and decreased platelet count, leading to increased risk of bleeding and may manifest like hematomas in atypical locations. These bleeding manifestations may be spontaneous or can be induced by even minor trauma. Cases: It is a single-center retrospective analysis. Four patients with a confirmed diagnosis of COVID-19 depicting increased risk of bleeding manifestations were included. Patients in our study were managed as per guidelines recommended by the Ministry of Health and Family Welfare Directorate General of Health Services, Government of India. Results: All patients were male. The mean age was 56 +/- 18.64 years. One patient was managed conservatively with discontinuation of anticoagulants, volume resuscitation, and transfusion of blood products. Drainage with incision was done for 2 patients. One was managed with pigtail drainage. Conclusion: The effect of anticoagulants given in therapy and their varied presentations are discussed in this article. The article concludes that we need vigilant observation to identify this complication in the early period, resulting in successful management.

9.
1st International Conference on Applied Intelligence and Informatics, AII 2021 ; 1435:29-42, 2021.
Article in English | Scopus | ID: covidwho-1391763

ABSTRACT

Conjunctivitis is a common ocular disease characterized by infection or swelling in the outer membrane of human eye. This contagious ocular disease could be controlled and well treated by medicines depending upon it’s category. To realize the connection between Conjunctivitis and other viral diseases, even for COVID-19, timely detection plays an important role. In this study, we have designed a mobile healthcare application (iConDet) through which initial level of Conjunctivitis detection is possible. Deep learning techniques have been used upon the Conjunctivitis dataset prepared by us in support of the claim and to achieve the desired accuracy of 84%. © 2021, Springer Nature Switzerland AG.

10.
J Neurol ; 269(3): 1107-1113, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1391865

ABSTRACT

BACKGROUND: With the explosion of COVID-19 globally, it was unclear if people with Parkinson's disease (PD) were at increased risk for severe manifestations or negative outcomes. OBJECTIVES: To report on people with PD who had suspected or confirmed COVID-19 to understand how COVID-19 manifested in PD patients. METHODS: We surveyed PD patients who reported COVID-19 to their Movement Disorders specialists at Columbia University Irving Medical Center and respondents from an online survey administered by the Parkinson's Foundation that assessed COVID-19 symptoms, general clinical outcomes and changes in motor and non-motor PD symptoms. RESULTS: Forty-six participants with PD and COVID-19 were enrolled. Similar to the general population, the manifestations of COVID-19 among people with PD were heterogeneous ranging from asymptomatic carriers (1/46) to death (6/46). The most commonly reported COVID-19 symptoms were fever/chills, fatigue, cough, weight loss, and muscle pain. Worsening and new onset of motor and non-motor PD symptoms during COVID-19 illness were also reported, including dyskinesia, rigidity, balance disturbances, anxiety, depression, and insomnia. CONCLUSION: We did not find sufficient evidence that PD is an independent risk factor for severe COVID-19 and death. Larger studies with controls are required to understand this further. Longitudinal follow-up of these participants will allow for observation of possible long-term effects of COVID-19 in PD patients.


Subject(s)
COVID-19 , Parkinson Disease , Anxiety/diagnosis , Humans , Parkinson Disease/complications , Parkinson Disease/diagnosis , Parkinson Disease/epidemiology , SARS-CoV-2 , Surveys and Questionnaires
13.
2020 Ieee 20th International Conference on Bioinformatics and Bioengineering ; : 466-471, 2020.
Article in English | Web of Science | ID: covidwho-1322698

ABSTRACT

Stay at home order during the COVID-19 helps flatten the curve but ironically, instigate mental health problems among the people who have Substance Use Disorders. Measuring the electrical activity signals in brain using off-the-shelf consumer wearable devices such as smart wristwatch and mapping them in real time to underlying mood, behavioral and emotional changes play striking roles in postulating mental health anomalies. In this work, we propose to implement a wearable, On-device Mental Anomaly Detection (OMAD) system to detect anomalous behaviors and activities that render to mental health problems and help clinicians to design effective intervention strategies. We propose an intrinsic artifact removal model on Electroencephalogram (EEG) signal to better correlate the fine-grained behavioral changes. We design model compression technique on the artifact removal and activity recognition (main) modules. We implement a magnitude-based weight pruning technique both on convolutional neural network and Multilayer Perceptron to employ the inference phase on Nvidia Jetson Nano;one of the tightest resource-constrained devices for wearables. We experimented with three different combinations of feature extractions and artifact removal approaches. We evaluate the performance of OMAD in terms of accuracy, F1 score, memory usage and running time for both unpruned and compressed models using EEG data from both control and treatment (alcoholic) groups for different object recognition tasks. Our artifact removal model and main activity detection model achieved about approximate to 93% and 90% accuracy, respectively with significant reduction in model size (70%) and inference time (31%).

14.
British Journal of Haematology ; 193(SUPPL 1):69-70, 2021.
Article in English | EMBASE | ID: covidwho-1255346

ABSTRACT

Content: Management of patients with sickle cell disease (SCD) in the UK relies largely on transfusion therapy and hydroxycarbamide alongside supportive care. New therapies to improve morbidity and mortality are needed. Two new therapies undergoing NICE technology appraisal are crizanlizumab and voxelotor. Crizanlizumab, a p-selectin inhibitor, has been shown to reduce the frequency of vaso-occlusive crisis in patients aged 16 or over in a randomised, placebo-controlled phase 2 trial (SUSTAIN)1. Voxelotor is an HbS polymerisation inhibitor and showed increased haemoglobin levels with reduced markers of haemolysis in a phase 3 trial (HOPE)2. Both were approved by the FDA in November 2019. The eligibility criteria used in the SUSTAIN and HOPE trials have been compared against the Bristol Haematology and Oncology Centre and Oxford University Hospitals SCD cohorts to identify those patients who would be eligible for these emerging therapies. The patient databases were cross-referenced with electronic notes to identify eligibility data. Vaso-occlusive crises (VOC) were determined by calls to the haemoglobinopathy team or helpline, attendance at the Acute Haematology Unit or admission. Electronic blood results were checked to ensure haemoglobin fell within the specified ranges. Results of analysis of SCD patient database according to eligibility criteria for crizanlizumab and voxelotor as described in the SUSTAIN and HOPE trials1,2 are shown in Table 1. Results show that, out of 158 patients with eligible sickle cell disorders, only 7 (4.4%) were eligible for both therapies. 8 (5.1%) were eligible for voxelotor only, and 2 (1.3%) were only eligible for crizanlizumab. Patients with no VOC in 12 months were ineligible for either therapy (n = 75, 47.5%) and just under half of these patients had a baseline haemoglobin over the range of eligibility for voxelotor (n = 37, 23.4%). 5 patients with a single VOC in 12 months had an Hb out of range for voxelotor (3.2%). Regular transfusion therapy was the second most common exclusion (n = 29, 18.3%). Age over 65 excluded 8 (5.1%) of our patients, and 6 (3.8%) had significant comorbidity that rendered them ineligible. 15 (9.5%) of our patients had moved out of area so estimates of VOC per year are likely to be inaccurate. Other reasons for exclusion included titration of hydroxycarbamide and family planning. Though new therapies are being developed for prevention of VOC in SCD, few patients in our cohort are eligible for these. Incidence of severe VOC may be underestimated as patients self-manage at home, particularly in the current COVID-19 pandemic. This should be kept under review and closer liaison with patients about their VOC may help identify eligible patients. Strict adherence to eligibility based on that of clinical trials is likely to result in very few patients benefitting from these new therapies.

17.
Proc. - IEEE Int. Conf. Mob. Ad Hoc Smart Syst., MASS ; : 684-692, 2020.
Article in English | Scopus | ID: covidwho-1132784

ABSTRACT

In this paper, we propose an end to end goal-oriented conversational AI agent that can provide contextual information from a potential hazard site. We posit the conversational agent as a FloodBot capable of seeing, sensing, assessing hazard condition, and ultimately conversing about them. We present our domain-specific FloodBot design-solution and learning-experience from the real-time deployment in a flash flood devastated city that uses state-of-the-art deep learning models. We specifically used computer vision and pertinent natural language processing technologies to empower the conversation power of the FloodBot. To deliver such practical and usable AI, we chain multiple deep learning frameworks and create a human-friendly question-answer based dialogue system. We present our deployment details from the last five months and validate the results using ongoing COVID19's impact on the area as well. © 2020 IEEE.

19.
Proc. - IEEE Int. Conf. Bioinform. Bioeng., BIBE ; : 466-471, 2020.
Article in English | Scopus | ID: covidwho-1050260

ABSTRACT

Stay at home order during the COVID-19 helps flatten the curve but ironically, instigate mental health problems among the people who have Substance Use Disorders. Measuring the electrical activity signals in brain using off-the-shelf consumer wearable devices such as smart wristwatch and mapping them in real time to underlying mood, behavioral and emotional changes play striking roles in postulating mental health anomalies. In this work, we propose to implement a wearable, On-device Mental Anomaly Detection (OMAD) system to detect anomalous behaviors and activities that render to mental health problems and help clinicians to design effective intervention strategies. We propose an intrinsic artifact removal model on Electroencephalogram (EEG) signal to better correlate the fine-grained behavioral changes. We design model compression technique on the artifact removal and activity recognition (main) modules. We implement a magnitude-based weight pruning technique both on convolutional neural network and Multilayer Perceptron to employ the inference phase on Nvidia Jetson Nano;one of the tightest resource-constrained devices for wearables. We experimented with three different combinations of feature extractions and artifact removal approaches. We evaluate the performance of OMAD in terms of accuracy, F1 score, memory usage and running time for both unpruned and compressed models using EEG data from both control and treatment (alcoholic) groups for different object recognition tasks. Our artifact removal model and main activity detection model achieved about ≈ 93% and 90% accuracy, respectively with significant reduction in model size (70%) and inference time (31%). © 2020 IEEE.

20.
International Journal of Technologies in Higher Education ; 17(3):4-6, 2020.
Article in English | Web of Science | ID: covidwho-1049242
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