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1.
Pituitary ; 25(6): 927-937, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2014318

ABSTRACT

PURPOSE: Acute pituitary referrals to neurosurgical services frequently necessitate emergency care. Yet, a detailed characterisation of pituitary emergency referral patterns, including how they may change prospectively is lacking. This study aims to evaluate historical and current pituitary referral patterns and utilise state-of-the-art machine learning tools to predict future service use. METHODS: A data-driven analysis was performed using all available electronic neurosurgical referrals (2014-2021) to the busiest U.K. pituitary centre. Pituitary referrals were characterised and volumes were predicted using an auto-regressive moving average model with a preceding seasonal and trend decomposition using Loess step (STL-ARIMA), compared against a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) algorithm, Prophet and two standard baseline forecasting models. Median absolute, and median percentage error scoring metrics with cross-validation were employed to evaluate algorithm performance. RESULTS: 462 of 36,224 emergency referrals were included (referring centres = 48; mean patient age = 56.7 years, female:male = 0.49:0.51). Emergency medicine and endocrinology accounted for the majority of referrals (67%). The most common presentations were headache (47%) and visual field deficits (32%). Lesions mainly comprised tumours or haemorrhage (85%) and involved the pituitary gland or fossa (70%). The STL-ARIMA pipeline outperformed CNN-LSTM, Prophet and baseline algorithms across scoring metrics, with standard accuracy being achieved for yearly predictions. Referral volumes significantly increased from the start of data collection with future projected increases (p < 0.001) and did not significantly reduce during the COVID-19 pandemic. CONCLUSION: This work is the first to employ large-scale data and machine learning to describe and predict acute pituitary referral volumes, estimate future service demands, explore the impact of system stressors (e.g. COVID pandemic), and highlight areas for service improvement.

2.
European Stroke Journal ; 7(1 SUPPL):355, 2022.
Article in English | EMBASE | ID: covidwho-1928135

ABSTRACT

Background: During the second wave of COVID-19, India suffered from a catastrophic outburst of cases and rapid transmission of disease due to the highly infectious delta strain (B.1.617.2). Patients infected with this strain underwent prolonged hospitalisations, suffered from severe symptoms. A sudden surge of fungal infections, primarily Mucormycosis was observed. Methods: We conducted a case-control study to study various risk factors and form of intracranial involvement in cases of Mucormycosis. Results: Study included 121 patients in total;out of which 61 were Mucormycosis patients with prior COVID-19 infection. 30 out of 61 Mucormycosis patients had intracranial involvement with majority having stroke in the form of the either infarct (10 patients, 34%);or haemorrhage (3 patients, 10%) and thrombosis of artery involvement (8 patients, 29%). Other intracranial form of involvement was abscess (6 patients, 20%) and meningitis (2 patients, 7%). The most frequent type of infarcts were lacunar infarcts and the most common location of infarcts were middle cerebral artery (MCA) or anterior cerebral artery (ACA). Patients were treated with administration of Amphotericin B and Posaconazole. Telephonic follow-up was conducted after a time period of about 90 days and their health condition was recorded on basis of modified ranking scale (mRS). Out of the 30 Mucormycosis infection patients displaying the occurrence of stroke, 10 patients could not survive. q Conclusion: Intracranial Mucormycosis in COVID19 patients presenting with stroke were observed frequently and had mortality in about one-third cases.

3.
European Stroke Journal ; 7(1 SUPPL):179-180, 2022.
Article in English | EMBASE | ID: covidwho-1928109

ABSTRACT

Background: The world was witness to a pandemic never experienced by this generation. The call to arms was answered by each branch of medicine, each fighting separate wars. The war, we as neurologists faced was the “Battle for the Vessels”. Health care workers are a precious resource in Low-Middle-Income-Countries. Hence, exposure to a covidpositive patient for a “full hour” during thrombolysis, isn't warranted. Hence Tenecteplase use which fits the bill “ideally” and “literally” was analysed in this study against Alteplase in strokes with covid-positivity. We analyse the factors which affect their action and the role covid had, in each scenario. Methods: This is an ambi-spective observational study of 37 patients in an apex tertiary-care centre in India. Routine stroke variables were assessed including follow-up imaging, functional outcomes at 3 months. The results were also analysed with the thrombolysis data from covidnegative individuals too in the same period. Results: Among the covid-positive patients 62.16% patients received tenecteplase while 37.83% received alteplase. Although the baseline characteristics were similar, the time-metrics for thrombolysis were significantly favourable in the tenecteplase arm. The median-hospital stay was shorter in the tenecteplase group as was the in-hospital mortality. On follow-up at 3 months, the median mRS-score was significantly favourable in the tenecteplase group. Conclusions: Thrombolysis during the pandemic has been a challenge in many ways especially in resource limited settings. This study shows that there needs to be a conscious and judicial transition towards tenecteplase during the pandemic, where healthcare workers are a precious resource too.

4.
Lecture Notes on Data Engineering and Communications Technologies ; 101:627-642, 2022.
Article in English | Scopus | ID: covidwho-1750627

ABSTRACT

Hospitals worldwide are struggling to cope up with patient’s admission issues related with the increasing number of COVID-19 patients’ cases mainly driven by Delta variant, as severely ill nCOVID patients are found waiting for hospital beds, which are occupied by non-critical COVID patients. To make the situation worse, people who are partially or fully vaccinated against COVID-19 are also getting re-infected. Due to the absence of prior knowledge of an index of severity for COVID-19 patients, hospitals, with limited number of ventilators and medical equipment, fail to admit patients on any priority basis. With multiple tests kit available in market till now, there is none with an instantaneous index for severity prediction for COVID. This research develops a free and user-friendly algorithm titled “SAHAYATA 1427” (renamed herein Sahayata) which predicts a factor for a patient having the probability of disease nCOVID-19 termed as “probability factor” of COVID-19 for each patient. Concurrently, the algorithm also provides an index for severity by which the patient is affected by nCOVID, termed as “severity index.” The input data is both demographic and patient provided. The severity index is determined using artificial intelligence. Using a logistic regression model with data set of existing COVID patients, Sahayata predicts the probability factor for an nCOVID-19 patient with an accuracy, precision and recall of 88.17%, 100% and 87.3%, respectively. Results indicate that it can be used effectively both at hospitals by trained medical personnel and at home by the general population. Sahayata helps the COVID-19 patients living in rural communities with smaller patients care facilities with limited equipment by providing a way for efficient treatment care. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

6.
Concurrency and Computation-Practice & Experience ; : 12, 2021.
Article in English | Web of Science | ID: covidwho-1589149

ABSTRACT

The novel-corona-virus is presently accountable for 547,782 deaths worldwide. It was first observed in China in late 2019 and, the increase in number of its affected cases seriously disturbed almost every nation in terms of its economical, structural, educational growth. Furthermore, with the advancement of data-analytics and machine learning towards enhanced diagnostic tools for the infection, the growth rate in the affected patients has reduced considerably, thereby making it critical for AI researchers and experts from medical radiology to put more efforts in this side. In this regard, we present a controlled study which provides analysis of various potential possibilities in terms of detection models/algorithms for COVID-19 detection from radiology-based images like chest x-rays. We provide a rigorous comparison between the VGG16, VGG19, Residual Network, Dark-Net as the foundational network with the Single Shot MultiBox Detector (SSD) for predictions. With some preprocessing techniques specific to the task like CLAHE, this study shows the potential of the methodology relative to the existing techniques. The highest of all precision and recall were achieved with DenseNet201 + SSD512 as 93.01 and 94.98 respectively.

7.
Journal of the Indian Chemical Society ; 98(10):7, 2021.
Article in English | Web of Science | ID: covidwho-1510016

ABSTRACT

Corona virus disease 2019 (COVID-19) endemic has havoc on the world;the causative virus of the pandemic is SARS CoV-2. Pharmaceutical companies and academic institutes are in continuous efforts to identify anti-viral therapy or vaccines, but the most significant challenge faced is the highly evolving genome of SARS CoV-2, which is imparting evolutionary selective benefits to the virus. To understand the viral mutations, we have retrieved nine hundred and thirty-four samples from different states of India via the GISAID database and analyzed the frequency of all types of point mutation in all structural, non-structural proteins, and accessory factors of SARS CoV-2. Spike glycol protein, nsp3, nsp6, nsp12, N and NS3 were the most evolving proteins. High frequency point mutations were Q496P (nsp2), A380V (nsp4), A994D (nsp3), L37F (nsp6), P323L & A97V (nsp12), Q57H (ns3), D614G (S), P13L (N), R203K (N), G204R (N) and S194L (N).

8.
Computers, Materials and Continua ; 70(1):1541-1556, 2021.
Article in English | Scopus | ID: covidwho-1405632

ABSTRACT

Like the Covid-19 pandemic, smallpox virus infection broke out in the last century, wherein 500 million deaths were reported along with enormous economic loss. But unlike smallpox, the Covid-19 recorded a low exponential infection rate and mortality rate due to advancement in medical aid and diagnostics. Data analytics, machine learning, and automation techniques can help in early diagnostics and supporting treatments of many reported patients. This paper proposes a robust and efficient methodology for the early detection of COVID-19 from Chest X-Ray scans utilizing enhanced deep learning techniques. Our study suggests that using the Prediction and Deconvolutional Modules in combination with the SSD architecture can improve the performance of the model trained at this task. We used a publicly open CXR image dataset and implemented the detection model with task-specific pre-processing and near 80:20 split. This achieved a competitive specificity of 0.9474 and a sensibility/accuracy of 0.9597, which shall help better decision-making for various aspects of identification and treat the infection. © 2021 Tech Science Press. All rights reserved.

9.
International Conference on Intelligent Computing and Advances in Communication, ICAC 2020 ; 202 LNNS:7-16, 2021.
Article in English | Scopus | ID: covidwho-1340419

ABSTRACT

In the recent history of human civilization, a pandemic affecting such an enormous population like COVID-19 was about 140 years ago-The Smallpox Worldwide Epidemic (1877–1977, Deaths-500 M). It can be easily inferred that the health management system over the globe in the nineteenth century was too underdeveloped than that of today, which also refers to the fact that the present epidemic must not be allowed to last much longer as the number of deaths is increasing nonlinearly (506 K, with 10.3 M affected). While the medical community around the globe is striving to find a permanent cure, it becomes evident responsibility of all professionals who can contribute in stabilizing the medical management systems of countries particularly underdeveloped/developing countries or those with highest rate of increase in COVID-19 cases like USA, Brazil. In this regard, this study introduces a fast, robust and practically effective method for detection of COVID-19 from chest x-ray images utilizing enhanced deep learning techniques. An object detection network is proposed to be trained with publicly existing datasets. In this model, SSD is used with ResNet101 as a base layer and some pre-processing, achieving a sensitivity of 0.9495 and a specificity of 0.9247. If practically implemented, this can prove very beneficial in aiding economies and health systems of the above-mentioned countries. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

10.
Pediatric Critical Care Medicine ; 22(SUPPL 1):234, 2021.
Article in English | EMBASE | ID: covidwho-1199512

ABSTRACT

AIMS & OBJECTIVES: The Tsunami of COVID-19 ethical guidance has led clinicians to feel as if they were drinking water from a hosepipe on full blast! However, to be useful, ethics should be a practical, not a guideline discipline - we describe the support provided by a Paediatric Bioethics Centre team (PBC) to PICU during the pandemic. METHODS: GOSH-PBC is a multidisciplinary group: laypeople - including former hospital parent, healthcare and other relevant professionals - which operates across 4 domains (i) Rapid Case Reviews (RCR) where children/families/professionals consider together difficult treatment decisions. (ii) Ethical staff support (ESS) to combat moral distress/injury(MDI), (iii) Research (iv) Education. Data sourced from the bioethics database March-July 2020 RESULTS: Initially, documents composed to support/guide hospital teams including PICU Activity highest in RCR: PICU referred 14 cases, 7 Sars-Cov-2 positive and 4 MIS for consideration of innovative therapies - all proceeded. COVIDnegative referrals: 3:one innovative surgery, two RRT/limitation considerations. Parents attended 6/14 meetings via video-link, met PBC shortly afterward (6/7). Existing ESS mechanisms adapted: 'coalface' PICU drop-in sessions replaced by (a) individual informal (socially-distanced) face-to-face support (1-3/day Monday-Friday) & (b) video-link group sessions (1-8/week, mean 4.6) - most to staff deployed to local overwhelmed adult ICUs. MDI card adapted for wellbeing hub. Research -support to PICU/ID PIMS-TS/MIS and COVID-19 publications and separate ethics pieces. Education - webcasts for local, national & international PICU teams. CONCLUSIONS: Bioethics support has been fundamental in challenging COVID-19 clinical decision-making for children, families & staff. Bioethical staff support has also been key.

11.
Ghana Medical Journal ; 53(3):248-251, 2019.
Article in English | MEDLINE | ID: covidwho-1017191

ABSTRACT

Pulmonary alveolar proteinosis (PAP) is an uncommon lung disease characterized by excessive accumulation of pulmonary surfactant that usually requires treatment with whole-lung lavage. A 47-year-old female presented with history of dry cough and breathlessness for past 6months. Chest radiograph demonstrated bilateral alveolar shadows and high resolution computerized tomography thorax showed crazy paving pattern. Broncho-alveolar lavage (BAL) and transbronchial lung biopsy confirmed a diagnosis of PAP. Due to worsening hypoxemia and respiratory failure, wholelung lavage was planned and performed. Anaesthetic management involved integrated use of pre-oxygenation, complete lung isolation, one-lung ventilation with optimal positive end-expiratory pressure, vigilant use of positional manoeuvres, and use of recruitment manoeuvres for the lavaged lung. We have discussed valuable strategies for the anaesthetic management of patients undergoing this multifaceted procedure in a case of severe PAP. Funding: None declared.

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