A Review of Stellate Ganglion Block as an Adjunctive Treatment Modality.

Peripheral nerve blocks are becoming increasingly used as adjunctive treatment modalities for a variety of conditions refractory to medical management. Right or left stellate ganglion blocks (SGB) are a specific type of peripheral nerve block that target the sympathetic blockade of neuronal impulses using the injection of local anesthetic and steroids into nerve bundles in the cervical area. This review article is intended to summarize the common uses of stellate ganglion blocks and explain the procedural technique, which has evolved with technological advances in ultrasonography. The similarities between these disease processes are centered around sympathetic hyperactivity. This sympathetic overdrive state is created by increased levels of nerve growth factor (NGF), which causes a cascade of sympathetic sprouting resulting in increased norepinephrine (NE) systemically. Reversal of this cascade by local anesthetic injection into the stellate ganglion thereby reduces NGF and sympathetic sprouting subsequently lowering overall norepinephrine levels. This is the unifying theory by which SGB is able to provide resolution for the varied clinical conditions described in this article.  This review article discusses the physiology of several conditions where stellate ganglion blocks are being investigated as an adjunct treatment modality, including anosmia, PTSD, long-COVID, chronic fatigue syndrome, menopausal hot flashes, and ventricular tachyarrhythmias. Overall, the current literature supporting the use of stellate ganglion blocks for several esoteric conditions is limited; however, case reports to date have shown promising evidence-based results supporting their use as an adjunctive treatment among patients with refractory symptoms to existing treatment algorithms. In conclusion, SGB should be considered among patients with refractory symptoms for medical management in the conditions discussed in this article. Further research is needed to delineate which patients will benefit from the use of SGB, the use of subsequent blocks and timelines in between injections, and unilateral versus bilateral blockade.

Stellate Ganglion Block for Long COVID Symptom Management: A Case Report.

Stellate ganglion block (SGB) is gaining increasing acceptance as a treatment modality for various medical conditions. It works by blocking neuronal transmissions which in turn alleviates sympathetically-driven disease processes. Many of the prolonged sequelae of long COVID are thought to be mediated by dysregulation of the autonomic nervous system, and SGB is being investigated as a potential option for symptomatic management of long COVID. This case report demonstrates the efficacy of SGB in a previously healthy patient for the management of long COVID symptoms including fatigue, post-exertional malaise, shortness of breath, and gastrointestinal symptoms. .

Incidence And Clinical Factors Associated with the Occurrence of Pneumothorax, Pneumomediastinum, and Subcutaneous Emphysema in Critically Ill Patients with COVID-19

Aims and objectives: Primary: To identify the incidence and clinical factors associated with the occurrence of pneumothorax and pneumomediastinum in critically ill patients with COVID- 19 pneumonia. Secondary: To observe the ventilatory/ respiratory parameters prior to occurrence of pneumothorax and pneumomediastinum. To study the clinical outcomes of patients in these patients. Materials and methods: Study design: Retrospective, observational study. All patients above 18 years of age patients admitted to the COVID ICU, prior to November 2020, diagnosed positive for SARS-CoV-2 by reverse transcriptase polymerase chain reaction (RT-PCR) or positive rapid antigen test AND with pneumothorax, subcutaneous emphysema, or pneumomediastinum detected clinically/radiologically were included. We performed a retrospective review of all cases admitted prior to November 30, 2020. All patients admitted to the ICU during this period with a positive RT PCR test were included. Around 409 cases were screened. CXR and CT scans of the chest were reported by the radiologist. The casefiles, serial X-rays, and computed tomography (CT) chest were scrutinized by the PI/CoPI to identify pneumothorax, pneumomediastinum or subcutaneous emphysema (hereby referred to as the events). Once included, clinical details prior to occurrence of event and interventions done, outcomes of patients was collected and analysed. Results: 409 cases were screened and 59 cases were identified to have an event or a combination of events. The incidence of pneumothorax was 8.5%, pneumomediastinum 2.9%, and subcutaneous emphysema 9.2% in our study. The mean age of patients who developed either event was 55.5 years. 55% were males and 44% were females. 83% of patients had at least 1 comorbidity. Mean APACHE 2 scores at time of ICU admission was 22.5. The average duration to occurrence of either event was noted to be 8.8 days post ICU admission and 6.2 days post intubation. 10 patients developed either barotrauma event while on spontaneous respiration indicating a possible mechanism of pSILI. 83% of the time the event was picked up by chest X-ray. The most common intervention was ICD placement (71% of cases) while 1 pneumothorax was tackled with pigtail insertion. Isolated pneumomediastinum cases were not intervened. The average time to resolution of the event was 6 days post intervention. 2 out of the 60 patients in our study developed a cardiac arrest following a pneumothorax. 40% of the patients had a VAE/VAP at the time of developing any of the events. Using Chi-squared test, presence of a ventilator-associated pneumonia (VAP/VAE) was significantly associated with the occurrence of pneumothorax/subcutaneous emphysema. Logistic regression showed that higher PEEP (>10 cmH2O), higher APACHE2 scores, and higher age (>60 years) were associated with the occurrence of pneumothorax but not the other 2 events. In our subset, patients with higher age and higher SOFA scores at the time of the event had a higher incidence of mortality. Overall mortality in our study cohort was 79.9%. Conclusion: The incidence of pneumothorax was 8.5%, pneumomediastinum 2.9%, and subcutaneous emphysema 9.2% in our study. Presence of a VAP/VAE was associated with occurrence of pneumothorax. Higher age and SOFA scores were associated a higher mortality in our subset of patients.

Evaluation of Dynamics of Renal Recovery in COVID-19 Patients with Acute Kidney Injury Admitted to Intensive Care Unit: An Observational Study (COVID-AKI Study)

Aim and background: The prevalence of acute kidney injury (AKI) among COVID-19 patients admitted to ICU was 46%. There is a paucity of data on renal recovery in a cohort of patients with AKI. Since COVID-19 is considered a public health issue, the estimates from this study might help in prognostication and health resource management. Objective: To evaluate the predictors and dynamics of renal recovery in critically ill COVID-19 patients with AKI. To study the duration and magnitude of AKI, the proportion of patients dependent on dialysis at hospital discharge, and mortality among COVID-AKI patients. Materials and methods: A single-centre, observational study was conducted in a mixed adult ICU from March 1, 2020, to February 1, 2021. COVID-19 patients who presented with or developed AKI as per KDIGO criteria within 7 days of ICU admission were included. Baseline characteristics, hemodynamic parameters, and renal recovery kinetics were captured till the discharge of the patient. Patients were followed up till 90 days post-discharge. Logistic regression with best subset selection was performed with renal recovery as an outcome (recovery is defined as attaining AKI stage 0 by KDIGO definition or 33% reduction of serum creatine from baseline) and APACHE II, rapidity of onset and progression of AKI, the magnitude of AKI, inflammatory markers, comorbidities, and P/F ratios as predictor variables. There were no multicollinearities, influential observations. Penalized-likelihood criteria (AIC and BIC models) were applied and a model with the lowest AIC or BIC was considered as the best fit to predict nonrecovery from AKI. Results: 200 patients' data were analysed, of which 67 patients recovered from AKI. Of the 67 patients, 16, 9, and 10 patients had transient AKI (<48 hours), persistent AKI (2-7 days), and AKD (7-90 days), respectively. Dialysis was required for 136 patients. The average duration for recovery from AKI was 7.4 days. The best fit model with the lowest BIC that predicted nonrecovery from AKI were: the combination of APACHE II, day onset of AKI, and magnitude of AKI. Results of logistic regression showed admission APACHE II, day onset of AKI, and magnitude of AKI were statistically significant in predicting non-recovery from AKI [OR 1.1 (p < 0.001;95% CI 1.06-1.16), OR 1.6 (p = 0.001;1.24-2.24), and OR 2.9 (p < 0.001;2.03-4.36), respectively]. This model had sufficient discrimination with AUC 0.86 and was well calibrated [Hosmer-Lemeshow (HL) chi2, p = 0.06]. Overall mortality among COVID-AKI patients was 84%. Two patients were dependent on dialysis at hospital discharge. Upon follow-up of 31 survivors for 90 days, four deaths were recorded. Conclusion: In our study, a higher APACHE II score at admission, the longer time interval between admission to the onset of AKI and the higher magnitude of AKI during ICU stay predicted poor renal recovery. A significant proportion of our patients require dialysis support and this poses a challenge on hospital resources and financial burden to the family. We observed higher mortality among COVID-19 patients with AKI compared to those with AKI not associated with COVID-19.

A review on triterpenoids from plant sources as potential antiviral agents

Viral infections are considered as leading a health issue globally. Numerous numbers of biologically active anti-viral agents have been identified from plants and other organisms. Particularly, terpenoids are a major component of the plant secondary metabolites and a complexity of these structures is accompanied by the potency of their biological activities. It is believed that most of the terpenoids possess the bioactivity against viral infections and cancer diseases. Hence, affected by the pressing a need elevated by the spreading of seriously life-threaten viruses, this review highlights the importance of terpenoids and their activity as antiviral agents that can be employed to treat current lethal diseases such as HIV, H1N1, SARS-CoV and HSV.

Plug-and-Play Oxygen Auto-flow Regulator for Low Flow Oxygen Therapy: A Prototype Development

COVID-19 has exacerbated the need for viewing medical oxygen as a precious drug and thus work towards its conservation which ensures optimal supply to the patient. This paper describes the practical implementation of a 'plug-and-play' automatic oxygen flow-rate controller for clinical use suitable for low-flow oxygen therapy. A microcontroller based electronic controller drives the extent of opening/closing of a proportional valve in line with the clinical oxygen supply line. The controller output is based on the pulse oximeter readings and the mode of operation chosen by the caregiver. It controls the oxygen flow-rate with an accuracy of 0.1 liters per minute (LPM) around the desired flow-rate. In the automatic mode, The flow-regulator is programmed through the control algorithm to enhance oxygen conservation. To ensure patient comfort, sudden changes in flow-rate are avoided and the rate of change of flow-rate is capped at 1 LPM per minute. © 2022 IEEE.

Enhancement of Manpower for Management of COVID-19 Crisis in India

The Coronavirus Disease 2019 (COVID-19) pandemic overwhelmed not just the resources in terms of infrastructure but also manpower. With many healthcare workers turning COVID-19 positive themselves, and the healthcare system being overburdened, we were short-staffed in most health institutions across India. In anticipation of a similar crisis, at our tertiary care centre, we came up with certain measures to enhance the manpower should the need arise for not just hospitals, but also the society at large.

Prediction of suitable candidates for covid-19 vaccination

In the current times, COVID-19 has taken a handful of people’s lives. So, vaccination is crucial for everyone to avoid the spread of the disease. How-ever, not every vaccine will be perfect or will get success for everyone. In the pre-sent work, we have analyzed the data from the Vaccine Adverse Event Reporting System and understood that the vaccines given to the people might or might not work considering certain demographic factors like age, gender, and multiple other variables like the state of living, etc. This variable is considered because it explains the unmentioned variables like their food habits and living conditions. The target group for this work will be the healthcare workers, government bodies & medical research organizations. We analyze the data using machine learning techniques & algorithms and predict the working of COVID-19 vaccines on specific age groups developed by significant vaccine manufacturers, i.e., PFIZER \BIONTECH and MODERNA. Data visualization and analysis interpret the vaccine impact based on the above-said variables. It becomes clear that people belonging to a specific demographic factor can have an option to choose the vaccine accordingly based on the previous history of a particular manufacturer’s vaccine getting succeeded for that demographic factor. The various machine learning algorithms we have used are Logistic Regression, Adaboost, Decision Tree, and Random Forest. We have considered the DIED variable as the target variable as this results in a high life threat. On performance measure, perspective Adaboost is showing appreciable values. The prediction of the type of vaccine to be adminis-tered could be derived using this machine learning algorithm. The accuracy we achieved based on the experiment are as follows: Decision Tree Classifier with 97.3%, Logistic Regression with 97.31%, Random Forest with 97.8%, AdaBoost with 98.1%. © 2022, Tech Science Press. All rights reserved.

Chest computed tomography scan images for classification of coronavirus by enhanced convolutional neural network and gabor filter

Coronavirus pandemic disease is caused by severe acute respiratory syndrome coronavirus 2. Generally RT-PCR or other Nucleic testing is used in order to detect covid19. In computed Tomography Scans, it can be clearly viewed that to how much extent the virus has damaged the Lungs. Computed Tomography gives the result in 15 minutes, whereas RT PCR takes 24 hours. PCR only checks whether virus is in nose or throat but the proposed model checks in lungs which is most accurate. The utilization of computed Tomography Scans will give us better and accurate results. The proposed novel model helps to recognize the corona virus in Lungs Computed Tomography Scans and achieved an accuracy of 0.93 with Gabor filter and 0.85 without Gabor filter. The existing models VGG16, VGG19, ResNet50 and Mobile Net achieves an accuracy of 0.89, 0.91, 0.91, 0.91 respectively using Gabor filter and 0.78, 0.71, 0.81 and 0.89 without using Gabor Filter. Gabor filter will help to remove the noise from the data, it is linear filter and orientation sensitive. Our model achieves an accuracy 0.93 which is better than VGG16, VGG19, ResNet50, Mobile Net models using Gabor Filter. © 2021 IEEE.