Amorphous Solid Dispersion of Niclosamide with Water-Soluble β-Cyclodextrins for Dissolution and Bioavailability Enhancement

Cyclodextrins (CDs) are cyclic oligosaccharides widely employed for the solubility enhancement of poorly water-soluble drugs. Niclosamide is a BCS class II drug for tapeworm infections and is currently under repurposing for various other indications, including COVID-19. Due to its low aqueous solubility, a high daily dose (2 g) is required for clinical efficacy. Herein, we investigate the potential of beta-cyclodextrin (β-CD) and its sulfobutylether and hydroxypropyl derivatives for the dissolution enhancement of niclosamide. The solid dispersions were prepared by kneading the drug and cyclodextrins together by adding solvent, water: methanol (1 : 1 v/v). Among various CDs studied, 2-Hydroxypropyl-β-cyclodextrin (HP-β-CD) in the 1: 2 molar ratio (SB-IC-N4 batch) shows the most significant improvement in water solubility of niclosamide (6.3 vs. 182 μg/ml), resulting in 2-fold improved in-vitro dissolution. The comparative oral pharmacokinetics in Wistar rats at 50 mg/kg produced 1.69-fold higher plasma exposure of niclosamide. The spectral characterization provided molecular insights into interactions of niclosamide with HP-β-CD. These results suggest that the dispersion of niclosamide with HP-β-CD aid in faster dissolution and better drug bioavailability. © 2023 Wiley-VCH GmbH.

Qualitative Analysis of Psychological Framework of Online Classes during Covid-19 using NVIVO

The purpose of this paper is to present the results of a study on whether or not online teaching-learning is a viable option for ensuring the quality of education in the future. The pandemic situation has brought major challenges to the educational institutions and institutions have to ensure that learning continues uninterrupted through virtual platforms using current technologies and tools. The theoretical framework for the survey is derived from secondary sources such as research papers, newspaper articles, and magazine articles on the topic. ProQuest and EBSCO were used for this. The study combines exploratory and descriptive methods. The paper shows that the future of the online education is not sustainable;it is just a situational arrangement and soon will be replaced by the old, traditional offline pattern, if the situation allows. © 2023, Rajarambapu Institute Of Technology. All rights reserved.

A novel circular approach to analyze the challenges associated with micro-nano plastics and their sustainable remediation techniques.

The mismanagement of consumer-discarded plastic waste (CDPW) has raised global environmental concerns about climate change. The COVID-19 outbreak has generated ∼1.6 million tons of plastic waste per day in the form of personal protective equipment (masks, gloves, face shields, and sanitizer bottles). These plastic wastes are either combustible or openly dumped in aquatic and terrestrial environments. Open dumping upsurges emerging contaminants like micro-nano plastics (MNPs) that directly enter the ecosystem and cause severe impacts on flora and fauna. Therefore, it has become an utmost priority to determine sustainable technologies that can degrade or treat MNPs from the environment. The present review assesses the sources and impacts of MNPs, various challenges, and issues associated with their remediation techniques. Accordingly, a novel sustainable circular model is recommended to increase the degradation efficiency of MNPs using biochemical and biological methods. It is also concluded that the proposed model does not only overcome environmental issues but also provides a sustainable secondary resource to meet the sustainable development goals (SDGs).

Technology and HealthCare in the Coming Era

The fast-emerging technologies in this century fueled by covid19 crises has taught every stakeholder (technology giants, pharmacists, hospitals, patients etc.) a great lesson. Hospital with good healthcare system will have some of these technologies while there are least developed countries with no infrastructure and no technology. Using technology in healthcare will change the working of existing healthcare system to brighter one. This paper will portray different technology trends like Artificial Intelligence, Machine Learning, Block chain, Robotic Surgery, Smart devices, telemedicine and so on. The promises and the obstacles with impact of the technology are discussed too. © 2022 IEEE.

Novel deep transfer learning model for COVID-19 patient detection using X-ray chest images.

Around the world, more than 250 countries are affected by the COVID-19 pandemic, which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This outbreak can be controlled only by the diagnosis of the COVID-19 infection in early stages. It is found that the radiographic images are ideal for the fastest diagnosis of COVID-19 infection. This paper proposes an ensemble model which detects the COVID-19 infection in the early stage with the use of chest X-ray images. The transfer learning enables to reuse the pretrained models. The ensemble learning integrates various transfer learning models, i.e., EfficientNet, GoogLeNet, and XceptionNet, to design the proposed model. These models can categorize patients as COVID-19 (+), pneumonia (+), tuberculosis (+), or healthy. The proposed model enhances the classifier's generalization ability for both binary and multiclass COVID-19 datasets. Two popular datasets are used to evaluate the performance of the proposed ensemble model. The comparative analysis validates that the proposed model outperforms the state-of-art models in terms of various performance metrics.

Efficacy of online mental health program "EmoAid " during the COVID-19 pandemic

Background: COVID-19 has triggered public health crises all around the globe. It has caused mental health issues not only in infected cases but also in uninfected cases. The world was put under strict/restricted lockdown to contain the transmission of COVID-19 diseases. All important aspects of life including therapies and counseling went online. "EmoAid " is one such online mental health program started during the COVID-19 to ease psychological distress. Aim: The current study assessed the efficacy of "EmoAid " program. Methods: One hundred and twenty-two participants completed depression, anxiety, and stress scale-21 online at three time periods;before the program (T1), after the program (T2), and 3 weeks after the program to study the maintenance effect (T3). Results: The results suggested that the program was successful in decreasing symptoms of depression, anxiety, and stress among participants. The maintenance effect of the program was also observed. Conclusion: The results highlight the importance of "EmoAid, " an online mental health program in lowering the levels of psychological distress during the pandemic, however, a randomized controlled study is required to validate the results.

ASMBoT: An Intelligent Sanitizing Robot in the Coronavirus Outbreak

Technology plays a vital role in our lives to meet basic hygiene necessities. Currently, the whole world is facing an epidemic situation and the practice of using sanitizers is common nowadays. Sanitizers are used by people to sanitize their hands and bodies. It is also used for sanitizing objects that come into contact with the machine. While sanitizing a small area, people manage to sanitize via pumps, but it becomes difficult to sanitize the same area every day. One of the most severe sanitation concerns is a simple, economic and efficient method to adequately clean the indoor and outdoor environments. In particular, effective sanitization is required for people working in a clinical environment. Recently, some commonly used sanitizer techniques include electric sanitizer spray guns, electric sanitizer disinfectants, etc. However, these sanitizers are not automated, which means a person is required to roam personally with the device to every place to spray the disinfectant or sanitize an area. Therefore, a novel, cost-effective automatic sanitizing machine (ASM) named ASMBoT is designed that can dispense the sanitizer effectively by solving the aforementioned problems. © 2022 IEEE.

Clinical and Virologic Outcomes with Early Adintrevimab (ADI) Monoclonal Antibody Therapy in Mild and Moderate COVID-19

Background. Peak SARS-CoV-2 viral replication occurs in the upper respiratory tract in presymptomatic and early symptomatic phases. Administration of a monoclonal antibody may be most beneficial in the early time period immediately after symptom onset. Here we describe the effect of early therapy on efficacy in patients receiving ADI. Methods. High risk patients with mild or moderate COVID-19 were enrolled in the ADI treatment study (STAMP), with primary endpoint of COVID-19 related hospitalization or all-cause death through Day 29 in patients with disease due to confirmed or suspected SARS-CoV-2 variants other than Omicron. Patients were randomized 1:1 to receive ADI or placebo administered by a single intramuscular (IM) injection. For this subgroup analysis, patients that had received therapy within 3 days of symptom onset were evaluated. Results. In the overall population, the study met the primary endpoint demonstrating 66% relative risk reduction of COVID-19 hospitalization or all cause death in 336 patients. Among 261 patients receiving therapy within 3 days of symptom onset (n=133 ADI, n=128 placebo), ADI was associated with a statistically significant reduction in the risk of COVID-19-related hospitalization or all-cause death through Day 29 compared with placebo (4 [3%] vs. 15 [11.7%], standardized risk difference -8%, 95% CI: -14.11, -1.86, p=0.0106), demonstrating a 72% standardized relative risk reduction in favor of ADI. When given as early therapy, ADI provided a greater reduction in viral load from baseline to Day 5 compared with placebo as assessed by saliva samples, with an adjusted least-squares mean difference of -0.97 log10 copies/mL (95% CI: -1.540, -0.391;p=0.0011). No study drug related SAEs, including deaths, and no hypersensitivity reactions were reported. Conclusion. Early therapy with a single dose of ADI 300 mg IM provided a 72% reduction in the risk of COVID-19 related hospitalization and all-cause death compared to placebo in high-risk ambulatory patients with mild to moderate COVID-19. Therapy within the first 3 days also led to a greater reduction in viral load compared to placebo and favorable outcomes in patients who are at high risk for progression of disease.

Higher Doses of Adintrevimab, an Extended Half-Life Monoclonal Antibody, for the Treatment and Prevention of COVID-19: Preliminary Results from a Phase 1 Single Ascending-Dose Study

Background. Adintrevimab is a fully human IgG1 monoclonal antibody engineered to have potent and broad neutralization against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other SARS-like CoVs with pandemic potential. Adintrevimab is being assessed in two separate phase 2/3 clinical trials: the EVADE trial for prevention of COVID-19 in both post-exposure and preexposure settings and the STAMP trial for treatment of COVID-19. Here we report higher doses being evaluated in a healthy volunteer study given that emerging variants may have varying susceptibilities to adintrevimab. Previous results 300 mg IM, 600 mg IM, and 500 mg IV cohorts have been reported. Methods. This is an ongoing Phase 1, randomized, placebo (PBO)-controlled, single ascending-dose study of adintrevimab administered intramuscularly (IM) or intravenously (IV) to healthy adults aged 18-50 years with no current SARS-CoV-2 infection. Participants were randomized 8:2 in 3 high dose cohorts (N=10/cohort: n=8 adintrevimab, n=2 PBO): adintrevimab 1200 mg IM, 1200 mg IV, and 4500 mg IV. Safety, tolerability, and pharmacokinetics (PK) were assessed up to 21 days post dose. Results. Overall, 30 participants received adintrevimab (n=24) or PBO (n=6). Blinded safety data for all cohorts and PK for 1200 mg IV are reported. Through 21 days post dose all doses were well-tolerated, with no study drug-related adverse events (AEs), serious AEs, injection site reactions, or hypersensitivity reactions reported. The observed PK profile of the 1200 mg IV dose included Cmax of 423+/-105 mug/ml. Comparison of 500 mg and 1200 mg IV doses indicate dose proportionality of Cmax and exposure (AUC Day 21). Conclusion. A single dose of adintrevimab, up to 4500 mg, was well tolerated. These preliminary safety data and PK support potential use of higher doses of adintrevimab as needed to address emerging SARS-CoV-2 variants.

Clinical, Laboratory Profile, Treatment and Outcome Of Primary Amyloidosis in the Era Of Novel Agents: An Experience from a Tertiary Care Hospital

Introduction: Diagnosis of AL amyloidosis requires demonstration of amyloid in affected tissues along with clonal plasma cells in bone marrow or presence of monoclonal light chains in blood. With the availability of serum light chain assay and immunophenotyping by flow cytometry, more cases of AL amyloidosis are being diagnosed. Here we present our experience of AL amyloidosis diagnosis and treatment in the era of modern diagnostics and therapy. Aims & Objectives: We aimed to describe the clinical presentations, laboratory features, treatment and outcomes of patients with AL amyloidosis in a single center using standard diagnostic tests and treatment with novel agents. Material(s) and Method(s): A retrospective analysis of AL amyloidosis patients, diagnosed in our hospital, a tertiary care center from January 2016 to June 2022 was conducted. The data was collected from departmental database. All statistical analyses were done by SPSS version 17. Result(s): Diagnosis of AL amyloidosis was done in 31 patients. Median age of presentation was 61 years. 25 (80.6%) were males. Major symptoms were pedal edema (38.7%) and shortness of breath (32.3%). Twenty four (77.4%) presented with ECOG PS >= 2. Most common systems involved were cardiac (54.8%) and renal (54.8%). Fourteen (45.2%) had two or more systems involvement while 17 (54.8%) had single system involvement. Lambda monoclonal light chain was present in 83.9% and kappa monoclonal light chain in 16.1%. Median M-protein was 0.59 g/dL (range 0-2 g/dL) and median bone marrow plasma cells were 6% (range-1-18%). Eighteen patients were treated;cyclophosphamide, bortezomib and dexamethasone (CyBORD) in 12/18 (66.7%) and bortezomib + dexamethasone in 6/18 (33.3%). Among 18 patients followed up with median follow up of 9 months (range 1-64 months), six expired;three due to COVID, two due to cardiac arrhythmia (during first cycle) and one due to relapse and rest 12 were alive. Among the 12 patients who were alive 6 were in complete hematological response. Conclusion(s): Our study presents the spectrum of clinical manifestations, management and outcomes of primary amyloidosis in Indian context. There is a need to increase the awareness among the physicians about amyloidosis so that early diagnosis can be made and timely treatment can be done with novel agents to improve the dismal historical results.

Clinical Profile and Treatment Outcome of Severe Aplastic Anemia in Adults: An Experience from Tertiary Care Centre in North India

Introduction: Untreated/refractory severe aplastic anemia (SAA) is associated with very high mortality. Allogenic bone marrow transplantation or immunosuppressive therapy remains mainstay of treatment but these treatments are timely available to only a select subset of patients. Recently eltrombopag has been approved for treatment of SAA. Aims & Objectives: We aimed to describe clinical profile and treatment response in patients with SAA from a tertiary care centre. Material(s) and Method(s): A retrospective analysis of patients diagnosed with SAA over a period of 7 years from January 2015-December 2021 was performed. The details of demographic profile, laboratory features, treatment given and response were analyzed. Result(s): Ninety patients were diagnosed with SAA during this period out of which 18 patients went elsewhere for treatment. Seventy-two patients who received treatment in our hospital were included in the analysis. Sixty-two patients were SAA while 10 VSAA. PNH screening was done in 24 patients, out of which 17 (70%) had small clone. The details of treatment and response achieved is shown in Table 1. Eight patients (11.1%) received matched related donor allogenic hemopoietic cell transplant, out of which one had rejection followed by auto recovery while one died 6 months later due to covid 19 disease. Sixty-four patients received immunosuppressive therapy, forty-nine (76%) responded. Recurrence of SAA occurred in two patients who has achieved complete response to ATG therapy;one received second course of horse ATG + CSA + ETP and responded again. Conclusion(s): Timely diagnosis and appropriate treatment selection is of utmost importance to achieve optimal outcome in severe aplastic anemia. Eltrombopag has become an important addition not only in front line but also in relapsed refractory aplastic anemia. Patients lacking donor, or resources for ATG should be treated with cyclosporine and eltrombopag as early as possible. (Table Presented).

Seroconversion After COVID 19 Vaccination And Disease In Adults With Hematological Diseases

Introduction: Patients with hematological diseases have impaired humoral immunity secondary to disease itself and due to its treatment. Treatment can be either steroids, chemotherapy and chemoimmunotherapy. The immune response to covid 19 vaccine or disease may be significant impaired in haematological diseases. Aims & Objectives: None of the approved vaccine available in India (Covaxin, covishield and sputnik) has been approved for hemato oncology and transplant patients but most of our patients had received either of these vaccines as per national recommendations. Hereby we did retrospective study to look for antibody response in haematological diseases. Material(s) and Method(s): We retrospectively analysed the serological response to covid 19 disease and vaccination in 50 patients. Patients were divided into myeloid malignancies (Acute myeloid leukemia and MDS) n = 11, lymphoid malignacies n = 12 (CLL N = 8, Non Hodgkin Lymphoma n = 3, Pre B ALL n = 1), plasma cell dyscrasis (n = 8) (Multiple myeloma n = 6, AL amyloidosis n = 2), ITP N = 10 (patients on steroids, rituximab), MPNs n = 6 (CML N = 5, ET N = 1), AIHA N = 3. The complete history including the history of covid 19 diseases and covid vaccination was taken. Treatment history of the patient and antibody formation was analysed. Result(s): SARS COV2 IgG antibody was tested in a total of 50 patients. Overall antibody response was present in 90.1% patients. The total igG antibody in myeloid malignancies was 482.45 (12-2000) u/ml, while MPN, lymphoid malignancies, plasma cell dyscrasis had median antibody 140.04 u /ml (0.2-250), 47.9.9u/ml (3.04-200), 1416.9u/ml (20-40,000) respectively. 2 patients with plasma cell dyscrasis and 2 patients with CML had no antibody formation. The benign disorders including itp on steroids and rituximab had median antibody of 262.72 u/ml (6.54-1358) and AIHA had 281.6 (45- 400) u/ml. The results are shown in table 1. Conclusion(s): This Study demonstrates low immunogenicity, mainly in patients with lymphoproliferative disorders, as well as with certain drugs, including mainly anti-CD20 antibodies, Bruton tyrosine kinase inhibitors. However, better humoral response rates are seen in plasma cell dyscrasis on treatment. (Table Presented).

Detection of Improperly Worn Face Masks using Deep Learning – A Preventive Measure Against the Spread of COVID-19

Coronavirus disease 2019 has had a pressing impact on people all around the world. Ceasing the spread of this infectious disease is the urgent need of the hour. A vital method of protection against the virus is wearing masks in public areas. Not merely wearing masks but wearing masks properly can ensure that the respiratory droplets do not get transmitted to other people. In this paper, we have proposed a deep learning-based model, which can be used to detect people who are not wearing their face masks properly. A convolutional neural network model based on the concept of transfer learning is trained on a self-made dataset of images and implemented with light-weighted neural network called MobileNetV2 for mobile architectures. OpenCV is used with Caffe framework to detect faces in an input frame which are further forwarded to our trained convolutional neural network for classification. The method has been implemented on various input images and classification results have been obtained for the same. The experimental results show that the proposed model achieves a testing accuracy and training accuracy of 93.58% and 92.27% respectively. Optimal results with high confidence scores and correct classification have also been achieved when the proposed model was tested on individual input images. © 2022, Universidad Internacional de la Rioja. All rights reserved.

Resilience of Healthcare Providers during COVID-19 Pandemic: A Rapid Assessment using Digital Platform

Introduction: Coronavirus Disease-2019 (COVID-19) pandemic exposed the health workforce to an unprecedented occupational hazard. While taking care of patients they always had to be conscious simultaneously for safeguarding themselves and their family members against the highly infectious virus. In West Bengal, cases were first reported in the last week of March-2020 and reached the peak around October-2020 in the first wave, once the lockdown was lifted. During the initial months, the staggering number of cases, prevailing uncertainty over case management, and untimely demise of colleagues and relatives, took their toll on the physical and mental health of doctors, paramedics, or support staff, both in the government and private sectors. Aim(s): To measure perceived stress, resilience and psychological well-being of healthcare providers using standard psychometric tools. Material(s) and Method(s): This was a cross-sectional observational study carried out among healthcare workers in hospitals located in West Bengal, India. A self-administered questionnaire was circulated through a digital platform between June-November 2020. The questionnaire was designed using Perceived Stress Scale (PSS-10), Kessler-6 (K6), and Brief Resilient Coping Scale (BRCS) to assess perceived stress, psychiatric morbidity, and resilience of the person. It had three parts, one to capture socio-demographic details of the participants including age, sex, marital status, occupation, family history of psychiatric morbidity, place of stay etc. Second part consisted of psychometric scales and third was designed to capture the views of participants on the coping strategies. Calculated sample size was 189. Result(s): Based on standard cut-off values, it was found that 65.6% subjects were under moderate or severe stress;56.6% had compromised mental well-being and 64% were not coping well with the pandemic situation. PSS were significantly poor for females(p-value<0.001),single(p-value<0.001)andthosewithout history of psychiatric morbidity (p-value <0.001) and low resilient copers (p<0.0001). Mental well-being was compromised more among married (p-value=0.01), doctors (p-value=0.008), aged <40 years (p-value=0.003), high resilient copers (p-value=0.02). Popular means of stress reliever were music and yoga/exercise. Correct and updated knowledge on disease transmission, availability of personal protective equipment, pursuing hobbies like music and gardening were few suggested measures to improve coping with stress associated with patient care. Conclusion(s): The study revealed that majority of the health workers experienced moderate to heavy degree of stress and compromised psychological well-being during the first wave of pandemic. Relationship of stress and psychological wellbeing with resilience and socio-demographic variables was not always linear. Copyright © 2022 Journal of Clinical and Diagnostic Research. All rights reserved.

Diagnosis of Covid-19 using Deep Learning

In the global health disaster of the Coronavirus infection-2019 (Covid-19) pandemic, the health sector is avidly seeking new technologies and strategies to detect and manage the spread of the Coronavirus outbreak. Artificial Intelligence (AI) is currently one of the most essential aspects of global technology since it can track and monitor the rate at which the Coronavirus develops as well as determines the danger and severity of Coronavirus patients. In this paper, we have proposed a two-stage end-to-end Deep Learning (DL) model which can be used to predict the presence and severity of Covid-19 infection in a patient as early and accurately as possible so that the spread of this viral infection can be slowed down. Hence, based on the Computed Tomography (CT) scans or chest X-rays provided by the user as an input, the DL models are built that can forecast the presence of Covid-19 in that respective patient accurately and efficiently. In this paper, 5 DL models i.e., VGG16, InceptionV3, Xception, ResNet50, and Convolution Neural Networks (CNN) are built and their comparative analysis is carried out for the diagnosis of Covid-19. On the Google Colab GPU, the models are trained for 100 epochs on a total of 1686 images of chest X-rays and CT scans. The experimental results show that out of all these models, the model based on the Xception algorithm is the most accurate one in determining the presence of the disease and provides an accuracy of 81% and 89% on CT scans and Chest x-rays respectively. © 2022 ACM.

Feasibility of large scale distress screening at an academic center and associated network sites using an adapted patient-reported outcome instrument and reflexive suicide screening

Background: Regular distress screening of patients with cancer is mandated by cancer care guidelines, with 25-50% of patients with cancer experiencing some level of distress. The COVID-19 pandemic amplified symptoms of distress and shifted oncologic care delivery towards telemedicine. In light of challenges faced with implementation of such screening, especially during this pandemic, we sought to assess the feasibility of implementing patient-focused distress screening at a large academic cancer center and its associated network sites. Method(s): We designed a modified distress screening tool with reflexive suicidality and mental health screening consisting of 1) Wellness Reflection, a questionnaire that incorporates questions from Patient-Reported Outcomes Measurement Information System-Global Health (PROMIS-GH) to monitor patients' physical, mental, and social health and to assess patients' needs for extra support, and 2) modified Patient Health Questionnaire 2+9 (PHQ 2+9) tool to screen for depression and suicide risk. This tool was used to triage patients in need of further assessment by the clinical team, including consideration for referrals to cancer supportive services. The two-tiered distress screening tool was delivered to patients at Stanford Cancer Center and 5 affiliate sites for inperson clinic visits starting September 2019. The Wellness Reflection was launched starting June 2020 for video visits. Result(s): From October 1, 2019 to May 14, 2021, 147,165 questionnaires representing 46,341 patients were collected. The overall Wellness Reflection questionnaire response rate was 55%;the video visit questionnaire response rate was ~70%, higher than the in-person visit response rate of ~50%. About 6% of questionnaires analyzed showed a request for more support, most commonly emotional and physical/symptom support, and this was consistent during the COVID-19 pandemic. 18.6% of questionnaire responses screened positive for further assessment, and those that triggered reflexive suicidality screening had a 17% positive PHQ 2+9 rate. Following assessment, 13,631 referrals to cancer supportive services were generated. Conclusion(s): The adoption of commonly available patient-reported outcome tools, including PROMIS-GH and PHQ 2+9, as a large-scale distress screening method for both in-person and tele-health clinic visits during the COVID-19 pandemic is feasible at a large academic cancer center and network sites. The volumes of positive screens and referrals were lower than expected despite broader implementation of distress screening and amplified symptoms of distress due to the pandemic.

Impact of the Pandemic on Mental Health Among the Youth of India

The restrictions due to the pandemic have impacted mental health worldwide. The pandemic instigated health and financial pressure on everyone. The research aims to analyse the impact of the lockdown and pandemic on the mental health of the youth in India, understand the reasons behind it and try to examine whether it should be continued after the lockdown. The study also seeks to understand whether the government response to the distress created mentally by the coronavirus pandemic has been adequate or not. This study is based on primary data collected from the youth of tier-one cities in India, using web-based surveys, focus group discussions, and semi-structured interviews. Through the research, variables that impact mental health have been identified, which include sleep patterns, generalized anxiety, screen time, social media usage, life satisfaction, work pressure, the environment at home, work satisfaction, sense of productivity, physical activities, and interpersonal conflicts. There has been an inadequate promotion of the initiatives undertaken by the government, leading to unawareness of these schemes. The research observed, an increased work pressure, screen time, and generalized anxiety, and a decrease in work satisfaction, productivity, sleep time, and personal space. This research is essential to understand the ‘how’ and ‘why’ of mental health deterioration and to come up with methods to help curb these problems in the long run. The research, however, has shortcomings including small sample size, and an under-representation of the marginalized income group. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Developing and Supporting Mathematics Teacher Educators Through Virtual Collaborations

This chapter focuses on a group of mathematics teacher educators across the state of Texas that utilized collaboration before and during a global pandemic in order to examine and apply equity issues in their own instruction and delivery of their mathematics methods courses. The authors will highlight and share how the use of collaboration that initiated with the focus on rethinking equity practices in methods courses morphed into a supportive environment that helped the group of mathematics teacher educators through a difficult transition in education due to COVID-19. The goal of the chapter is to urge educators to use collaboration as an impetus for professional development and establishing communities of practice.