Tumor cell phagocytosis (cannibalism) in lung cancer: possible biomarker for tumor immune escape and prognosis.

BACKGROUND: Tumor cell phagocytosis (cannibalism) is rarely seen in lung carcinomas. Little is known about its underlying cellular pathogenesis and associated significance as tumor immune escape mechanism. METHODOLOGY: The cases of lung cancer diagnosed at department of Pathology, VPCI over 13-year period, 2007-2020 (n = 350) were retrospectively reviewed. The cases displaying cannibalism were correlated with their tumor morphology, coexisting inflammation, patient age at presentation, sex, stage/grade, and smoking status. RESULTS: Cannibalism was identified in 10/350 (2.86%) cases of lung cancer. 9/10 (90%) were males and 1/10 (10%) was female. These patients ranged from 48-71 years of age and presented with history of chest pain, anorexia and weight loss. History of smoking was seen in 9/10 (90%) cases while 10% were non-smokers. Mass lesions were seen on CT scan and CT-guided fine needle aspiration cytology (FNAC) was performed. Cytopathology revealed squamous cell carcinoma (5/10, 50%), adenocarcinoma-3/10 (30%), adenosquamous carcinoma (1/10, 10%), and non small cell lung carcinoma (1/10, 10%). No association with small cell carcinoma was seen in our study. Background inflammation and infiltration of acute on chronic inflammatory infiltrate were seen in 6/10 or 60% cases. CONCLUSION: Lung cancers rarely show cannibalism, a tumor immune escape mechanism, even in advanced stage. This phenomenon correlates with squamous cell and adenocarcinoma morphology, tumor associated inflammatory infiltrate, and smoking status. It may be considered as a possible biomarker for tumor immune escape and poor prognosis.

Nitrogen dioxide as proxy indicator of air pollution from fossil fuel burning in New Delhi during lockdown phases of COVID-19 pandemic period: impact on weather as revealed by Sentinel-5 precursor (5p) spectrometer sensor.

There has been a long-lasting impact of the lockdown imposed due to COVID-19 on several fronts. One such front is climate which has seen several implications. The consequences of climate change owing to this lockdown need to be explored taking into consideration various climatic indicators. Further impact on a local and global level would help the policymakers in drafting effective rules for handling challenges of climate change. For in-depth understanding, a temporal study is being conducted in a phased manner in the New Delhi region taking NO2 concentration and utilizing statistical methods to elaborate the quality of air during the lockdown and compared with a pre-lockdown period. In situ mean values of the NO2 concentration were taken for four different dates, viz. 4th February, 4th March, 4th April, and 25th April 2020. These concentrations were then compared with the Sentinel (5p) data across 36 locations in New Delhi which are found to be promising. The results indicated that the air quality has been improved maximum in Eastern Delhi and the NO2 concentrations were reduced by one-fourth than the pre-lockdown period, and thus, reduced activities due to lockdown have had a significant impact. The result also indicates the preciseness of Sentinel (5p) for NO2 concentrations.

Subtyping of advanced lung cancer based on PD-L1 expression, tumor histopathology and mutation burden (EGFR and KRAS): a study from North India.

Immune checkpoint inhibitor (PD-L1) therapy of advanced non-small-cell lung cancer (NSCLC) has variable outcomes. Tumor subtypes based on PD-L1 expression, histopathology, mutation burden is required for patient stratification and formulation of treatment guidelines. Lung cancers (n=57) diagnosed at Pathology department, VPCI (2018-2021) were retrospectively analyzed. PD-L1(SP263) expressed by tumor cells [low (<1%), medium (1-49%), high (≥50%)] was correlated with histopathology, microenvironment, EGFR, KRAS expression. Patients were categorized into high and low risk based on their: i) gender: males (n=47, 30-89 years), females (n=10, 45-80 years); ii) smoking history: males 26/47 (45.61%), females 1/10 (10%); iii) tumor subtyping: squamous cell carcinoma 15/57 (26.32%), adenocarcinoma 6/57 (17.54%), NSCLC-undifferentiated 24/57 (42.10%), adenosquamous carcinoma 5/57 (8.77 %), carcinosarcoma 4/57 (7.02%), small cell carcinoma 1/57 (1.75%); iv) inflammatory tumor microenvironment/TILs 44/57 (77.1%); iv) PD-L1 positivity-31/57 (54.3%); v) concomitant EGFR/KRAS positivity. PD-L1positive cases showed squamous/undifferentiated histopathology, concomitant EGFR+ (9/20, 45%) and KRAS+ (8/15, 53.3%), smoking+ (21/31,67.74%).PD-L1 negative cases (26/57, 45.6%), were EGFR+ (2/14, 14.28%) and KRAS+ (6/19, 31.5%). The high-risk lung cancer subtypes show squamous/undifferentiated histopathology, inflammatory microenvironment, male preponderance, smoking history, higher concomitant PD-L1, KRAS and EGFR positivity. Lung cancer subtyping can predict clinical response/resistance of patients prior to initiation of PD-L1 inhibitor therapies and can be used to guide therapy.

Model-based ensembles: Lessons learned from retrospective analysis of COVID-19 infection forecasts across 10 countries.

Mathematical models of different types and data intensities are highly used by researchers, epidemiologists, and national authorities to explore the inherently unpredictable progression of COVID-19, including the effects of different non-pharmaceutical interventions. Regardless of model complexity, forecasts of future COVID-19 infections, deaths and hospitalization are associated with large uncertainties, and critically depend on the quality of the training data, and in particular how well the recorded national or regional numbers of infections, deaths and recoveries reflect the the actual situation. In turn, this depends on, e.g., local test and abatement strategies, treatment capacities and available technologies. Other influencing factors including temperature and humidity, which are suggested by several authors to affect the spread of COVID-19 in some countries, are generally only considered by the most complex models and further serve to inflate the uncertainty. Here we use comparative and retrospective analyses to illuminate the aggregated effect of these systematic biases on ensemble-based model forecasts. We compare the actual progression of active infections across ten of the most affected countries in the world until late November 2020 with "re-forecasts" produced by two of the most commonly used model types: (i) a compartment-type, susceptible-infected-removed (SIR) model; and (ii) a statistical (Holt-Winters) time series model. We specifically examine the sensitivity of the model parameters, estimated systematically from different subsets of the data and thereby different time windows, to illustrate the associated implications for short- to medium-term forecasting and for probabilistic projections based on (single) model ensembles as inspired by, e.g., weather forecasting and climate research. Our findings portray considerable variations in forecasting skill in between the ten countries and demonstrate that individual model predictions are highly sensitive to parameter assumptions. Significant skill is generally only confirmed for short-term forecasts (up to a few weeks) with some variation across locations and periods.

Exploring the knowledge, attitude, and practice of health-care professionals on coronavirus (COVID-19) pandemic infection.

BACKGROUND: To prevent the rapid spread of infectious COVID-19 in India, many steps have been taken. Adherence to the control measures depends on the knowledge, attitude, and practices (KAPs) toward COVID-19 disease in health-care professionals. The present study was conducted among health-care professionals in Jaipur. The objective was to evaluate the KAPs about COVID-19 among health-care professionals. MATERIALS AND METHODS: A total of 385 participants took part in the study. A self-designed questionnaire was filled by the participants online (Google Form). The knowledge of the participants was assessed using 12 questions. Attitude and practices of the participants were assessed by two questions for each. RESULTS: Among the study completers, 52.20% were male, 71.42% of the participants were aged above 30 years, and 59.22% were nurse. The knowledge score was approximately 90%. The majority of the participants had a strong conviction that India can overcome this infectious disease in the near future. Multiple regression analysis found that good knowledge score of COVID-19 was associated with lower negative attitudes and less risky practices (P < 0.001). CONCLUSION: Participants of the study were knowledgeable, held positive attitudes, and had adequate practices for COVID-19.

Highlighting the compound risk of COVID-19 and environmental pollutants using geospatial technology.

The new COVID-19 coronavirus disease has emerged as a global threat and not just to human health but also the global economy. Due to the pandemic, most countries affected have therefore imposed periods of full or partial lockdowns to restrict community transmission. This has had the welcome but unexpected side effect that existing levels of atmospheric pollutants, particularly in cities, have temporarily declined. As found by several authors, air quality can inherently exacerbate the risks linked to respiratory diseases, including COVID-19. In this study, we explore patterns of air pollution for ten of the most affected countries in the world, in the context of the 2020 development of the COVID-19 pandemic. We find that the concentrations of some of the principal atmospheric pollutants were temporarily reduced during the extensive lockdowns in the spring. Secondly, we show that the seasonality of the atmospheric pollutants is not significantly affected by these temporary changes, indicating that observed variations in COVID-19 conditions are likely to be linked to air quality. On this background, we confirm that air pollution may be a good predictor for the local and national severity of COVID-19 infections.

Adjunctive effects of acupressure therapy on pain and quality of life in patients with knee osteoarthritis: an interventional study / 针灸推拿医学（英文版）

Objective: To investigate the effect of acupressure therapy as an adjunctive therapy to pharmacological treatment on pain and health-related quality of life (QOL) among knee osteoarthritis (KOA) patients.Methods: One hundred KOA patients were recruited from the orthopedic out-patient clinic of the institute. The patients were allocated randomly (flipping-coin simple randomization method) into an intervention group (n=50) and a control group (n=50). Patients in the intervention group received acupressure therapy along with pharmacological treatment. Patients in the control group did not receive acupressure therapy but continued their pharmacological treatment. Changes in pain (visual analog scale, VAS) and QOL (short-form 36-item health survey, SF-36) scores at baseline (A0), during training session (A1), follow-up at the 3rd month after training (A2) and follow-up at the 6th month after training session (A3) were collected and examined. Multiple regression analysis was used to check the relationship between pain and SF-36 domains. Results: The VAS score of participants in the intervention group decreased at A3 (P=0.001). Scores of physical functioning (PF), role limitations due to emotional problems (RE) and mental health (MH) of SF-36 in the intervention group improved more as compared with the control group. Patients in the intervention group with improvement in VAS (pain) score showed greater changes in mean scores of all domains of SF-36 from baseline (all P?0.05). Except bodily pain (BP), the other domains of SF-36 were negatively correlated with pain score. Conclusion: Acupressure therapy with pharmacological treatment can improve health-related QOL and pain among KOA patients.

Acupressure as an Adjunct to Pharmacological Treatment for Depression, Anxiety, and Stress in Patients with Knee Osteoarthritis.

BACKGROUND: Osteoarthritis is the most common type of arthritis. Depression, anxiety, and stress are associated with knee osteoarthritis. OBJECTIVES: The aim of the study was to check the effectiveness of acupressure therapy with pharmacological treatment on pain, depression, anxiety, and stress in patients with knee osteoarthritis and to assess the effect of pain improvement on psychological health. METHODS: Eligible 212 patients with knee osteoarthritis were divided into two groups (intervention and control group). The intervention group (n = 106) received acupressure therapy in combination with pharmacological treatment, whereas the control group (n = 106) continued pharmacological treatment only. Pain and psychological symptoms were measured using the visual analog scale and Depression Anxiety Stress Scale-21. Pearson's correlation was used to check the effect of pain improvement on psychological health. RESULTS: Patients of both groups reported severe pain initially. On analyzing the results after completion of the study, it was found that patients in the intervention group scored better on the pain scale (p < 0.001) and DASS-21 (p ≤ 0.0001). However, it may be noted that reduction in the DASS-21 score was not found to be significant for the control group (p = 0.08). Pearson's correlation coefficients value ranged from 0.231 to 0.412 for DASS-21 (p < 0.05). CONCLUSIONS: On analysis, it can be concluded that acupressure can be used as add-on therapy in combination with conventional treatment (pharmacological treatment), which may assist in pain reduction. The reduction in pain directly contributes to improvement in the physiological wellness among patients with knee osteoarthritis.

Prediction of the COVID-19 Pandemic for the Top 15 Affected Countries: Advanced Autoregressive Integrated Moving Average (ARIMA) Model.

BACKGROUND: The coronavirus disease (COVID-19) pandemic has affected more than 200 countries and has infected more than 2,800,000 people as of April 24, 2020. It was first identified in Wuhan City in China in December 2019. OBJECTIVE: The aim of this study is to identify the top 15 countries with spatial mapping of the confirmed cases. A comparison was done between the identified top 15 countries for confirmed cases, deaths, and recoveries, and an advanced autoregressive integrated moving average (ARIMA) model was used for predicting the COVID-19 disease spread trajectories for the next 2 months. METHODS: The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. The spatial map is useful to identify the intensity of COVID-19 infections in the top 15 countries and the continents. The recent reported data for confirmed cases, deaths, and recoveries for the last 3 months was represented and compared between the top 15 infected countries. The advanced ARIMA model was used for predicting future data based on time series data. The ARIMA model provides a weight to past values and error values to correct the model prediction, so it is better than other basic regression and exponential methods. The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. RESULTS: The top 15 countries with a high number of confirmed cases were stratified to include the data in a mathematical model. The identified top 15 countries with cumulative cases, deaths, and recoveries from COVID-19 were compared. The United States, the United Kingdom, Turkey, China, and Russia saw a relatively fast spread of the disease. There was a fast recovery ratio in China, Switzerland, Germany, Iran, and Brazil, and a slow recovery ratio in the United States, the United Kingdom, the Netherlands, Russia, and Italy. There was a high death rate ratio in Italy and the United Kingdom and a lower death rate ratio in Russia, Turkey, China, and the United States. The ARIMA model was used to predict estimated confirmed cases, deaths, and recoveries for the top 15 countries from April 24 to July 7, 2020. Its value is represented with 95%, 80%, and 70% confidence interval values. The validation of the ARIMA model was done using the Akaike information criterion value; its values were about 20, 14, and 16 for cumulative confirmed cases, deaths, and recoveries of COVID-19, respectively, which represents acceptable results. CONCLUSIONS: The observed predicted values showed that the confirmed cases, deaths, and recoveries will double in all the observed countries except China, Switzerland, and Germany. It was also observed that the death and recovery rates were rose faster when compared to confirmed cases over the next 2 months. The associated mortality rate will be much higher in the United States, Spain, and Italy followed by France, Germany, and the United Kingdom. The forecast analysis of the COVID-19 dynamics showed a different angle for the whole world, and it looks scarier than imagined, but recovery numbers start looking promising by July 7, 2020.

A Machine Condition Monitoring Framework Using Compressed Signal Processing.

The vibration monitoring of ball bearings of a rotating machinery is a crucial aspect for smooth functioning and sustainability of plants. The wireless vibration monitoring using conventional Nyquist sampling techniques is costly in terms of power consumption, as it generates lots of data that need to be processed. To overcome this issue, compressive sensing (CS) can be employed, which directly acquires the signal in compressed form and hence reduces power consumption. The compressive measurements so generated can easily be transmitted to the base station and the original signal can be recovered there using CS reconstruction algorithms to diagnose the faults. However, the CS reconstruction is very costly in terms of computational time and power. Hence, this conventional CS framework is not suitable for diagnosing the machinery faults in real time. In this paper, a bearing condition monitoring framework is presented based on compressed signal processing (CSP). The CSP is a newer research area of CS, in which inference problems are solved without reconstructing the original signal back from compressive measurements. By omitting the reconstruction efforts, the proposed method significantly improves the time and power cost. This leads to faster processing of compressive measurements for solving the required inference problems for machinery condition monitoring. This gives a way to diagnose the machinery faults in real-time. A comparison of proposed scheme with the conventional method shows that the proposed scheme lowers the computational efforts while simultaneously achieving the comparable fault classification accuracy.

Short-Term Statistical Forecasts of COVID-19 Infections in India.

COVID-19 cases in India have been steadily increasing since January 30, 2020 and have led to a government-imposed lockdown across the country to curtail community transmission with significant impacts on societal systems. Forecasts using mathematical-epidemiological models have played and continue to play an important role in assessing the probability of COVID-19 infection under specific conditions and are urgently needed to prepare health systems for coping with this pandemic. In many instances, however, access to dedicated and updated information, in particular at regional administrative levels, is surprisingly scarce considering its evident importance and provides a hindrance for the implementation of sustainable coping strategies. Here we demonstrate the performance of an easily transferable statistical model based on the classic Holt-Winters method as means of providing COVID-19 forecasts for India at different administrative levels. Based on daily time series of accumulated infections, active infections and deaths, we use our statistical model to provide 48-days forecasts (28 September to 15 November 2020) of these quantities in India, assuming little or no change in national coping strategies. Using these results alongside a complementary SIR model, we find that one-third of the Indian population could eventually be infected by COVID-19, and that a complete recovery from COVID-19 will happen only after an estimated 450 days from January 2020. Further, our SIR model suggests that the pandemic is likely to peak in India during the first week of November 2020.

Scrutinizing predator journals in pharmacology and calculating their predatory rate.

BACKGROUND: As the list of predatory journals is burgeoning, the researchers should have knowledge of calculating the predatory rate (PR) for the journals, in which they aim to publish their work and self-guard them from publishing in bogus journals. AIM AND OBJECTIVES: Our aim is to find out the predatory rate for various Pharmacology journals. MATERIALS AND METHODS: Here, we have examined the recently updated list (in 2017) of standalone predatory journals created and maintained by Beall, pertinent to all auspices of pharmacology including pharmacy, pharmaceutical, and pharmacognosy. The PR of various journals was calculated. RESULTS: Of 131 journals, pertinent to the pharmacology field, 45.03% of them had the PR between 0.72 and 0.84. 98.5% of journals were classified as predatory, whereas only 2 (1.53%) journals were classified in the category of predatory practice. CONCLUSION: It should be an eye-opener to the researchers, and they should deliberately select the journals to get real recognition of their work.

Modeling of Electric Demand for Sustainable Energy and Management in India Using Spatio-Temporal DMSP-OLS Night-Time Data.

Changes in the pattern of electric power consumption in India have influenced energy utilization processes and socio-economic development to greater extent during the last few decades. Assessment of spatial distribution of electricity consumption is, thus, essential for projecting availability of energy resource and planning its infrastructure. This paper makes an attempt to model the future electricity demand for sustainable energy and its management in India. The nighttime light database provides a good approximation of availability of energy. We utilized defense meteorological satellite program-operational line-scan system (DMSP-OLS) nighttime satellite data, electricity consumption (1993-2013), gross domestic product (GDP) and population growth to construct the model. We also attempted to examine the sensitiveness of electricity consumption to GDP and population growth. The results revealed that the calibrated DMSP and model has provided realistic information on the electric demand with respect to GDP and population, with a better accuracy of r 2 = 0.91. The electric demand was found to be more sensitive to GDP (r = 0.96) than population growth (r = 0.76) as envisaged through correlation analysis. Hence, the model proved to be useful tool in predicting electric demand for its sustainable use and management.

Vigorous Appraisal of Fluoride on Industrial Proponent in Thermal Power Plant over Anthropoid Biosphere Using F(-) Ion-Selective Electrode.

This study was conducted to analyze the impact of fluoride in the anthropogenic condition in an industrial region promoting and affecting the health of the workers. Fluoride is toxic to humans in high concentrations, such as can occur in persons working in fluoride-containing mineral industries like aluminum industries. When workers are exposed to fluoride-containing minerals, they can suffer from a variety of health problems, such as dental disease. This paper presents the relationship of different clinical conditions correlated against the fluoride level. Contributing clinical aspects, such as morbidity, dysentery, overcrowding, and skin disease, are also studied to assess the consequences of fluoride upon consistent exposure. The relationship between pH and hardness of water with fluoride was measured, and then spatial maps were generated. The investigations resulted in a conclusion that hardness of water had a more pronounced impact on the level of fluoride concentration as compared with pH. Water with more hardness contains more fluoride concentration (25 mg/ml) as compared with soft water (4 mg/ml). This paper also revealed the concentration of fluoride content in the bodies of aluminum plant workers, which varied from 0.06 to 0.17 mg/L of blood serum in the case of pot room workers and 0.01 to 0.04 mg/L in the case of non-pot room workers. In fingernails, it varied from 0.09 to 3.77 mg/L and 0.39 to 1.15 mg/L in the case of pot room and non-pot room workers, respectively. In urine, it varied from 0.53 to 9.50 mg/L in pot room workers and 0.29 to 1.80 mg/L in non-pot room workers. This paper concluded that water was safe for drinking purposes if it has a low hardness (60-140 mg/ml) and pH (7.1-7.4).