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"Kumar, Ranjit"
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Healthcare big data analytics : computational optimization and cohesive approaches
Highlighting how optimized big data applications can be used for patient monitoring and clinical diagnosis, this book also explores the challenges, opportunities and future research directions, discussing the stages of data collection and pre-processing, as well as the associated challenges and issues in data handling and setup.
Book
Molecular signatures in prion disease: altered death receptor pathways in a mouse model
Background
Prion diseases are transmissible and fatal neurodegenerative diseases characterized by accumulation of misfolded prion protein isoform (PrP
Sc
), astrocytosis, microgliosis, spongiosis, and neurodegeneration. Elevated levels of cell membrane associated PrP
Sc
protein and inflammatory cytokines hint towards the activation of death receptor (DR) pathway/s in prion diseases. Activation of DRs regulate, either cell survival or apoptosis, autophagy and necroptosis based on the adaptors they interact. Very little is known about the DR pathways activation in prion disease. DR3 and DR5 that are expressed in normal mouse brain were never studied in prion disease, so also their ligands and any DR adaptors. This research gap is notable and investigated in the present study.
Methods
C57BL/6J mice were infected with Rocky Mountain Laboratory scrapie mouse prion strain. The progression of prion disease was examined by observing morphological and behavioural abnormalities. The levels of PrP isoforms and GFAP were measured as the marker of PrP
Sc
accumulation and astrocytosis respectively using antibody-based techniques that detect proteins on blot and brain section. The levels of DRs, their glycosylation and ectodomain shedding, and associated factors warrant their examination at protein level, hence western blot analysis was employed in this study.
Results
Prion-infected mice developed motor deficits and neuropathology like PrP
Sc
accumulation and astrocytosis similar to other prion diseases. Results from this research show higher expression of all DR ligands, TNFR1, Fas and p75NTR but decreased levels DR3 and DR5. The levels of DR adaptor proteins like TRADD and TRAF2 (primarily regulate pro-survival pathways) are reduced. FADD, which primarily regulate cell death, its level remains unchanged. RIPK1, which regulate pro-survival, apoptosis and necroptosis, its expression and proteolysis (inhibits necroptosis but activates apoptosis) are increased.
Conclusions
The findings from the present study provide evidence towards the involvement of DR3, DR5, DR6, TL1A, TRAIL, TRADD, TRAF2, FADD and RIPK1 for the first time in prion diseases. The knowledge obtained from this research discuss the possible impacts of these 16 differentially expressed DR factors on our understanding towards the multifaceted neuropathology of prion diseases and towards future explorations into potential targeted therapeutic interventions for prion disease specific neuropathology.
Journal Article
COVID-19 and prices of pulses in Major markets of India: Impact of nationwide lockdown
The COVID-19 pandemic has impacted almost all the sectors including agriculture in the country. The present paper investigates the impact of COVID-19 induced lockdown on both wholesale and retail prices of major pulses in India. The daily wholesale and retail price data on five major pulses namely Lentil, Moong, Arhar, Urad and Gram are collected for five major markets in India namely Delhi, Mumbai, Kolkata, Chennai and Hyderabad during the period January, 2019 to September, 2020 from Ministry of Consumer Affairs, Food & Public Distribution, Government of India. The Government of India declared nationwide lockdown since March, 24, to May, 31, 2020 in different phases in order to restrict the spread of the infection due to COVID-19. To see the impact of lockdown on price and price volatility, time series model namely Autoregressive integrated moving average (ARIMA) model with error following Generalized autoregressive conditional heteroscedastic (GARCH) model incorporating exogenous variable as lockdown dummy in both mean as well variance equations. It is observed that in almost all the markets, lockdown has significant impact on price of the pulses whereas in few cases, it has significant impact on price volatility.
Journal Article
Arsenic exposure in Indo Gangetic plains of Bihar causing increased cancer risk
Reportedly, 300 million people worldwide are affected by the consumption of arsenic contaminated groundwater. India prominently figures amongst them and the state of Bihar has shown an upsurge in cases affected by arsenic poisoning. Escalated arsenic content in blood, leaves 1 in every 100 human being highly vulnerable to being affected by the disease. Uncontrolled intake may lead to skin, kidney, liver, bladder, or lung related cancer but even indirect forms of cancer are showing up on a regular basis with abnormal arsenic levels as the probable cause. But despite the apparent relation, the etiology has not been understood clearly. Blood samples of 2000 confirmed cancer patients were collected from pathology department of our institute. For cross-sectional design, 200 blood samples of subjects free from cancer from arsenic free pockets of Patna urban agglomeration, were collected. Blood arsenic levels in carcinoma patients as compared to sarcomas, lymphomas and leukemia were found to be higher. The geospatial map correlates the blood arsenic with cancer types and the demographic area of Gangetic plains. Most of the cancer patients with high blood arsenic concentration were from the districts near the river Ganges. The raised blood arsenic concentration in the 2000 cancer patients strongly correlates the relationship of arsenic with cancer especially the carcinoma type which is more vulnerable. The average arsenic concentration in blood of the cancer patients in the Gangetic plains denotes the significant role of arsenic which is present in endemic proportions. Thus, the study significantly correlates and advocates a strong relation of the deleterious element with the disease. It also underlines the need to address the problem by deciphering the root cause of the elevated cancer incidences in the Gangetic basin of Bihar and its association with arsenic poisoning.
Journal Article
Efficacy and safety of 225Ac-DOTATATE targeted alpha therapy in metastatic paragangliomas: a pilot study
Purpose
In this study, we aim to evaluate the efficacy and safety of
225
AC-DOTATATE targeted alpha therapy in advanced-stage paragangliomas (PGLs).
Methods
Nine (6 males and 3 females) consecutive patients with histologically proven PGLs were treated with
225
Ac-DOTATATE targeted alpha therapy (TAT) and concomitant radiosensitizer, capecitabine, at 8-weekly intervals up to a cumulative activity of ~ 74 MBq. The primary endpoint included evaluating therapy response and disease control rate (DCR) using the RECIST 1.1 criteria. Additional secondary endpoints comprised clinical response assessment using EORTC QLQ-H&N35 questionnaire, Karnofsky Performance Scale (KPS), Eastern Cooperative Oncology Group performance status (ECOG), analgesic score (AS), dose alterations of anti-hypertensive drugs (anti-HTN), and the safety and side-effect profile evaluation as per CTCAE criteria version 5.0.
Results
Following
225
Ac-DOTATATE treatment, morphological response revealed partial response in 50%, stable disease in 37.5%, and disease progression in 12.5%, with a DCR of 87.5%. Similarly, the symptomatic response was remarkable, and anti-HTN drugs were stopped in 25% and reduced in 37.5%. Another significant finding in our study revealed a morphologic DCR of 66.6% (2/3) in patients who failed previous lutetium-177 peptide receptor radionuclide therapy (
177
Lu-PRRT). Regarding the KPS, ECOG, and AS performance scores, a notable improvement was observed post-
225
Ac-DOTATATE treatment. The QLQ-H&N35 symptom scores evaluated in seven H&N PGL patients showed significant improvement in all aspects. No improvement in sexual function was noted (
P
= 0.3559). Despite the significant reduction in the analgesic score post-treatment (
P
= 0.0031), the QLQ-H&N35 revealed only marginal significance concerning the intake of pain killers (
P
= 0.1723). No grade III/IV hematological, renal, and hepatological toxicities were noted.
Conclusion
The evidence from this study suggests
225
Ac-DOTATATE therapy is effective and safe in the treatment of advanced-stage PGLs and also reports a clear benefit even in patient’s refractory to the previous
177
Lu-PRRT.
Journal Article
Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India
Price forecasting of perishable crop like vegetables has importance implications to the farmers, traders as well as consumers. Timely and accurate forecast of the price helps the farmers switch between the alternative nearby markets to sale their produce and getting good prices. The farmers can use the information to make choices around the timing of marketing. For forecasting price of agricultural commodities, several statistical models have been applied in past but those models have their own limitations in terms of assumptions. In recent times, Machine Learning (ML) techniques have been much successful in modeling time series data. Though, numerous empirical studies have shown that ML approaches outperform time series models in forecasting time series, but their application in forecasting vegetables prices in India is scared. In the present investigation, an attempt has been made to explore efficient ML algorithms e.g. Generalized Neural Network (GRNN), Support Vector Regression (SVR), Random Forest (RF) and Gradient Boosting Machine (GBM) for forecasting wholesale price of Brinjal in seventeen major markets of Odisha, India. An empirical comparison of the predictive accuracies of different models with that of the usual stochastic model i.e. Autoregressive integrated moving average (ARIMA) model is carried out and it is observed that ML techniques particularly GRNN performs better in most of the cases. The superiority of the models is established by means of Model Confidence Set (MCS), and other accuracy measures such as Mean Error (ME), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Prediction Error (MAPE). To this end, Diebold-Mariano test is performed to test for the significant differences in predictive accuracy of different models. Among the machine learning techniques, GRNN performs better in all the seventeen markets as compared to other techniques. RF performs at par with GRNN in four markets. The accuracies of other techniques such as SVR, GBM and ARIMA are not up to the mark.
Journal Article
Population dynamic consequences of fearful prey in a spatiotemporal predator-prey system
Fear can influence the overall population size of an ecosystem and an important drive for change in nature. It evokes a vast array of responses spanning the physiology, morphology, ontogeny and the behavior of scared organisms. To explore the effect of fear and its dynamic consequences, we have formulated a predator-prey model with the cost of fear in prey reproduction term. Spatial movement of species in one and two dimensions have been considered for the better understanding of the model system dynamics. Stability analysis, Hopf-bifurcation, direction and stability of bifurcating periodic solutions have been studied. Conditions for Turing pattern formation have been established through diffusion-driven instability. The existence of both supercritical and subcritical Hopf-bifurcations have been investigated by numerical simulations. Various Turing patterns are presented and found that the change in the level of fear and diffusion coefficients alter these structures significantly. Holes and holes-stripes mixed type of ecologically realistic patterns are observed for small values of fear and relative increase in the level of fear may reduce the overall population size.
Journal Article
Nystromformer based cross-modality transformer for visible-infrared person re-identification
Person re-identification (Re-ID) aims to accurately match individuals across different camera views, a critical task for surveillance and security applications, often under varying conditions such as illumination, pose, and background. Traditional Re-ID systems operate solely in the visible spectrum, which limits their effectiveness under varying lighting conditions and at night. To overcome these limitations, leveraging the visible-infrared (VIS-IR) domain becomes essential, as infrared imaging provides reliable information in low-light and night-time environments. However, the integration of VIS (visible) and IR (infrared) modalities introduces significant cross-modality discrepancies, posing a major challenge for feature alignment and fusion. To address this, we propose NiCTRAM: a Nyströmformer-based Cross-Modality Transformer designed for robust VIS-IR person re-identification. Our framework begins by extracting hierarchical features from both RGB and IR images through a shared convolutional neural network (CNN) backbone, ensuring the preservation of modality-specific characteristics. These features are then processed by parallel Nyströmformer encoders, which efficiently capture long-range dependencies in linear time using lightweight self-attention mechanisms. To bridge the modality gap, a cross-attention fusion block is introduced, where RGB and IR features interact and integrate second-order covariance statistics to model higher-order correlations. The fused features are subsequently refined through projection layers and optimized for re-identification using a classification head. Extensive experiments on benchmark VIS-IR person Re-ID datasets demonstrate that NiCTRAM outperforms existing methods, achieving state-of-the-art accuracy and robustness by effectively addressing the cross-modality challenges inherent in VIS-IR Re-ID. The proposed NiCTRAM model achieves significant improvements over the current SOTA in VIS-IR ReID. On the
SYSU-MM01
dataset, it surpasses the SOTA by
4.21% in Rank-1 accuracy
and
2.79% in mAP
for all-search single-shot mode, with similar gains in multi-shot settings. Additionally, NiCTRAM outperforms existing methods on
RegDB
and
LLCM
, achieving up to
5.90% higher Rank-1 accuracy
and
5.83% higher mAP
in Thermal-to-Visible mode. We will make the code and the model available at
https://github.com/Ranjitkm2007/NiCTRAM
Journal Article
Estimating health and economic burden of PM10 pollution in Agra, India using AirQ+ and VSL approaches
Air pollution affects both the environment and the economy. This study aimed to analyse the annual and seasonal concentrations of respirable suspended particulate matter (PM
10
) and evaluate the monetary loss from health risk caused by PM
10
in Agra, India, for the year 2022. PM
10
levels were monitored using ground-based methods. Health impacts were estimated via the AirQ+ model. Seasonal trends were analyzed using Mann-Kendall and Sen’s slope tests, and economic costs were calculated using the Value of a Statistical Life (VSL) approach. Post-monsoon had the highest seasonal concentration, followed by winter, summer, and monsoon. Proportions of attributable exposure to PM
10
were estimated to be 41.91% for post-neonatal all-cause mortality, 65.6% for bronchitis in children, 78.4% for chronic bronchitis, 65.65% for lung cancer, 79.2% for respiratory diseases, and 55.38% for ischemic heart disease (IHD) in adults. We observed significant (
p
< 0.05) decreasing trends in summer and an increasing trend in post-monsoon. The annual economic burden was estimated at US$ 95.56 per person, US$ 202.58 million for Agra, and extrapolated US$ 135.40 billion for India. The expense amount is approximately 4% of the gross domestic product (GDP), 46% of all tax revenues, and around 130% of the healthcare budget of India. The study underscores the urgent need for pollution control to safeguard health and ease the heavy economic burden locally and nationally.
Journal Article