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Illegal activities associated with deforestation for the lumber and furniture industries pose significant threats to plant and animal biodiversity, as well as natural resources. Accurate identification of wood sources is vital, yet traditional laboratory techniques often fall short in precisely determining the chemical composition of samples for classification. This study aims to leverage ATR-FTIR spectroscopy alongside machine learning algorithms to construct a robust model for discerning the geographical origins of wood samples from India. By systematically comparing various machine learning classifiers, we address the limitations of subjective visual interpretation and evaluate their accuracy using wood spectral data. Logistic regression emerges as the most effective classifier for distinguishing Eucalyptus (75 % accuracy), Dalbergia (68 % accuracy), and Populus (81.5 % accuracy) species. Through a methodology encompassing data pre-processing, classifier selection, and performance evaluation, this research offers promising tools for combating challenges posed by illegal wood trafficking and transportation. The outcomes hold significant potential for enhancing wildlife crime prevention efforts by facilitating the tracing illicit timber sources, apprehension of perpetrators, and implementation of preventive measures.

Although sustainability issues disproportionately affect disenfranchised populations, justice considerations are often left to social scientists in sustainability research. As early-career researchers pursuing doctoral degrees in diverse disciplines focusing on sustainability, we reflect on the unintentional exclusion of justice in sustainability research. Building on our individual and collective research experiences, we propose a critical multifaceted-disciplinary perspective, advocating for the holistic creation of interdisciplinary academic teams involving scholars from diverse racial, social, cultural, and economic contexts. By embracing multifaceted-disciplinarity, we can step toward establishing and nurturing spaces that enrich justice considerations in sustainability science, forming a more comprehensive understanding of sustainability predicaments and building sustainable and humane futures for all.

The measles virus is an RNA virus belonging to the Paramyxoviridae family. It leads to an acute communicable illness that primarily involves the respiratory tract. Vaccination has significantly reduced the overall incidence and mortality worldwide; however, outbreaks still occur globally each year due to several factors. The SARS-CoV-2 pandemic has been a major hurdle since 2020. Despite the World Health Organization's goal to eradicate measles by 2023, there has been an increase in measles incidence in India, with 61,562 cases in 2022. Vaccination is a crucial preventive measure, and coverage needs to be increased through education, advocacy, and outreach to isolated communities.

Aims: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 virus) affects vital organs and causes vascular injury. There are concerns that this injury may have long-term consequences on the cardiovascular system after recovery from COVID-19. We investigated the incidence and predictors of new-onset hypertension at 1-year follow-up post-COVID-19 disease. Methods: In this prospective observational study, 393 patients hospitalised and diagnosed with COVID-19 disease at a tertiary cardiac care hospital during 27th March 2021 to 27th May 2021. Eligible 248 patients whose baseline characteristics, laboratory findings, treatment and outcome data were received systematically. Patients were followed up at 1 year of COVID-19 disease recovery. Results: We found that 32.3% of the population had new onset of hypertension at 1 year follow-up post-COVID-19 disease recovery. More hypertensive patients had severe computed tomography (CT) score severity (28.7 vs 14.9%; P 0.02). More number of patients in the hypertensive group were treated with steroids (73.8% vs 39%; p

Background: Hepatotoxicity is the foremost issue for clinicians and the primary reason for pharmaceutical product recalls. A biomarker is a measurable and quantifiable attribute used to evaluate the efficacy of a treatment or to diagnose a disease. There are various biomarkers which are used for the detection of liver disease and the intent of liver damage. Objective: This review aims to investigate the current state of hepatotoxicity biomarkers and their utility in clinical settings. Using hepatic biomarkers, the presence of liver injury, its severity, prognosis, causative agent, and type of hepatotoxicity can all be determined. Methods: Relevant published articles up to 2022 were systematically retrieved from MEDLINE/PubMed, SCOPUS, EMBASE, and WOS databases using keywords such as drug toxicity, hepatotoxicity biomarkers, biochemical parameters, and nonalcoholic fatty liver disease. Results: In clinical trials and everyday practice, biomarkers of drug-induced liver injury are essential for spotting the most severe cases of hepatotoxicity. Hence, developing novel biomarker approaches to enhance hepatotoxicity diagnosis will increase specificity and/or identify the person at risk. Importantly, early clinical studies on patients with liver illness have proved that some biomarkers such as aminotransferase, bilirubin, albumin, and bile acids are even therapeutically beneficial. Conclusions: By assessing the unique signs of liver injury, health care professionals can rapidly and accurately detect liver damage and evaluate its severity. These measures contribute to ensuring prompt and effective medical intervention, hence reducing the risk of long-term liver damage and other major health concerns.

The extraordinary and unique properties of persistent luminescent (PerLum) nanostructures like storage of charge carriers, extended afterglow, and some other fascinating characteristics like no need for in-situ excitation, and rechargeable luminescence make such materials a primary candidate in the fields of bio-imaging and therapeutics. Apart from this, due to their extraordinary properties they have also found their place in the fields of anti-counterfeiting, latent fingerprinting (LPF), luminescent markings, photocatalysis, solid-state lighting devices, glow-in-dark toys, etc. Over the past few years, persistent luminescent nanoparticles (PLNPs) have been extensively used for targeted drug delivery, bio-imaging guided photodynamic and photo-thermal therapy, biosensing for cancer detection and subsequent treatment, latent fingerprinting, and anti-counterfeiting owing to their enhanced charge storage ability, in-vitro excitation, increased duration of time between excitation and emission, low tissue absorption, high signal-to-noise ratio, etc. In this review, we have focused on most of the key aspects related to PLNPs, including the different mechanisms leading to such phenomena, key fabrication techniques, properties of hosts and different activators, emission, and excitation characteristics, and important properties of trap states. This review article focuses on recent advances in cancer theranostics with the help of PLNPs. Recent advances in using PLNPs for anti-counterfeiting and latent fingerprinting are also discussed in this review.

Undoubtedly, throughout the past half-century, environmental quality has emerged as a significant obstacle to both economic and social endeavors. Recent local and international policy debates have focused on environmental deterioration and global warming, but how governments balance economic growth and environmental sustainability is still enigmatic. For this reason, we have examined the determinants of environmental quality in India from 1972 to 2021. More specifically, we have investigated whether the fiscal deficit is ‘curse’ or ‘haven’ for environmental quality (CO2) in India. Moreover, this study deliberated four other predictors, comprising technological development (TIN), fossil fuel consumption (FFC), urbanization (Ub), and human capital index (HCI). In order to attain this objective, a range of econometric estimation techniques are employed to ensure the validity and reliability of the outcomes. For instance, we have employed a battery of ARDL approaches, such as standard ARDL, nonlinear ARDL, and multiple threshold NARDL approaches. In light of our research findings, we will be focusing directly on the examination of the NARDL and MTNARDL outcomes. This is due to the empirical evidence indicating the existence of asymmetric effects resulting from FD on CO2 emissions in India. The NARDL approach reveals that the consequence of fiscal deterioration is more pronounced, and the influence of fiscal progress is mild in terms of CO2 emission growth. Further, the outcomes of the MTNARDL approach revealed that the size of the extremely low changes in FD is much higher than the extremely high changes in FD in both models. This implies that as the FD rises, CO2 ascends more significantly, and when the FD lowers, CO2 declines progressively. In a nutshell, FD has a long-run positive and asymmetric impact on CO2 in India; thus, we may conclude that FD is considered the ‘curse’ for CO2 in India. Furthermore, TIN, HCI, and Ub have detrimental effects on CO2, whereas FFC stimulates CO2 in India. This research work provides some important policy implications for environmentalists, economists and macroeconomic policymakers to promote a green and healthy environment.

The development of sustainable biorefineries and bioeconomy has been the mandate of most of the governments with major focus on restricting the climate change concerns and finding new strategies to maintain the global food supply chain. Xylooligosaccharides (XOS) are short-chain oligomers which due to their excellent prebiotic potential in the nutraceutical sector has attracted intense research focus in the recent years. The agro-industrial crop and food waste can be utilized for the production of XOS which are derived from hemicellulose fraction (xylan) of the lignocellulosic materials. The extraction of xylan, is traditionally achieved by acidic and alkaline pretreatments which, however, have limited industrial applications. The inclusion of cutting-edge and environmentally beneficial pretreatment methods and technologies such as deep eutectic solvents and green catalysts are preferred. Moreover, the extraction of xylans from biomass using combinatorial pretreatment approaches may help in economizing the whole bioprocess. The current review outlines the factors involved in the xylan extraction and depolymerization processes from different lignocellulosic biomass and the subsequent enzymatic hydrolysis for XOS production. The different types of oligosaccharides and their prebiotic potential for the growth of healthy gut bacteria have also been explained. The introduction of modern molecular technologies has also made it possible to identify enzymes and microorganisms with the desired characteristics for usage in XOS industrial production processes.

The design of an efficient and scalable Wireless Sensor Network (WSN) to accommodate the fluctuations in topology, node-mobility, node-density, and network-size is an exigent task. A meticulous investigation is necessitated to implement the sensor nodes in an appropriate topology at optimum node-mobility in ZigBee incorporated WSN networks to offer optimum Quality of Services (QoS). Subsequently, an attempt to compute the comprehensive recital of a non-beacon mode based 802.15.4/ZigBee integrated WSN network is demonstrated. An analytical model is designed using parameters such as back-off number, retransmission limit, and back-off exponent considering the impact of node-mobility which has not reported earlier. Further, the investigation is carried out experimentally by evaluating the different QoS metrics, for instance, throughput, network load, bit error rate (BER), received power, signal to noise ratio (SNR) and end-to-end delay for diverse node-density and network-size of a mobile Wireless Sensor Network. Based on the measurements obtained, the authors recommend implementing the mobile sensor nodes in a cluster-tree fashion to afford the best possible QoS services.

Background: Various inotropic agents/vasopressors combinations are used in patients of cardiogenic shock. We performed this study to observe hemodynamic effects of various inotrope/vasopressor combinations in patients with NSTEMI cardiogenic shock (CS) at tertiary cardiac centre Methods and materials: Of 3832 NSTEMI, we studied 59 consecutive such patients with CS who hadn't undergone revascularization in the first 24 h in a prospective, open label, observational study. Group 1 comprised of background Dopamine with Noradrenaline titration(N = 38), Group 2 had background Dobutamine and Noradrenaline titration(N = 15) and Group 3 comprised of triple combination of Dopamine, Noradrenaline & Adrenaline(N = 6). Results: The mean change in hemodynamic parameters between these groups from baseline to 24 h showed no statistical difference. Cardiac output(CO), mean arterial pressure(MAP), central venous pressure(CVP) and cardiac power output(CPO) in group 2 were favorable at 6 and 24 h compared to baseline but mean change was insignificant as compared to others. In group 3, the increase in MAP was significant. IABP use did not change CO, CPO or SVR in any group except lower dosages of Dobutamine (49%) in IABP group. Lower in-hospital mortality in group 2 compared to others (P = 0.004) may be reflective of sicker patients in group 1 and 3. Conclusion: The mean changes in hemodynamic parameters were not significant between all groups. All regimes of inotropes when selected as per clinical indication in CS with ACS resulted in similar hemodynamic effects. The mortality difference may not truly be reflective of regimes rather reflect sicker patients in the higher mortality group.

We report an investigation into the application of the logistic regression classifier for estimation of sex in facial images. We used 2000 images, 1000 each of both sexes from a publicly available database and automatically detected facial landmarks and derived some morphometric facial indices. These indices were used as predictors for the classification. As the traditional manual extraction of facial landmarks is time consuming, automatic detection of the landmarks improves the efficiency. The logistic regression classification is also compared with two other classification methods, the likelihood-ratio (LR) based method where the features of a face are evaluated in terms of the probability distribution of these features in both the sexes, and the Convolutional Neural Networks (CNN) methods. While is former is desirable from the viewpoint of interpretability and to assess the strength of evidence, the latter is sophisticated. We report an AUC of 0.94 with true positive (TP) rate of 88.4% for males and 87.9% for females for logistic regression-based classification. This performance is better than the likelihood ratio classifier with TP rate of 79.6% for males and 82.2% for females. The overall performance of logistics regression is slightly less than the CNN classifier that has 89.3% TP rate for males and 92.6% for females. We have extended these models to a CCTV image database, more representative of the forensic scenario and found the logistic regression performing better than the CNN method on an average for 8 different types of cameras. We conclude that as a trade-off between simplicity and sophistication, the logistic regression classifier can be used for a two-class problem like classification of sex from facial morphometric indices, and that the likelihood ratio approach can assess the strength of the classification, in conformance with the requirements of evidence interpretation.

In the Questioned Document, the examination of ink is crucial scientific evidence to discern the difference between genuine and forged documents. The document examination which is examined by physical and chemical analysis of ink involves the matching of the source of writing material used to fabricate the document of ink. In this study, fifty black ink samples (25 ball-point and 25 gel-point pens) are used. These ink samples are analyzed with Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy conjoined with chemometric method i.e. Principal Component Analysis (PCA) and PLS-Discriminant Analysis (PLS-DA). The spectroscopic discrimination is determined by the absence or presence of particular peaks or bands in the fingerprint region of black ink samples. The first-order derivative graphs are used for spectral characterization of ink samples. PCA reduces the dimensionality of large datasets into small number of independent Principal Components and increases interpretability but at the same time minimizes the data information loss. The utilization of the PLS-DA algorithm gives 100 % accuracy to differentiate two classes of black ink samples. The ROC curve value of the PLS-Discrimination Analysis model is 1 which means 100 % sensitivity and specificity. This non-destructive study is an analytical approach that helped to maintain the spectral library based on spectroscopic results of all fifty samples. The adequacy of the PLS-Discrimination Analysis model is checked by running a blind test of ten unknown samples.

Non-small cell lung cancer (NSCLC) has a poor prognosis. Targeted therapy and immunotherapy in recent years has significantly improved NSCLC patient outcome. In this study, we employed cell-by-cell immune and cancer marker profiling of the primary tumor cells to investigate possible signatures that might predict the presence or absence of circulating tumor cells (CTCs). We performed a comprehensive study on 10 NSCLC patient tissue samples with paired blood samples. The solid tissue biopsy samples were dissociated into single cells by non-enzymatic tissue homogenization and stained with a total 25 immune, cancer markers and DNA content dye and analyzed with high-parameter flow cytometry. CTCs were isolated and analyzed from the paired peripheral blood. We investigated a total of 74 biomarkers for their correlation with CTC number. Strong correlations were observed between CTC number and the frequency of immune checkpoint marker expressing lymphocytes (CTLA-4, LAG3, TIM3, PD-1), within the CD103+CD4+ T lymphocyte subset. CTC number is also correlated with the frequency of PD-L1 expressing cancer cells and cancer cell DNA content. In contrast, CTC number inversely correlated to the frequency of CD44+E-cadherin− cancer cells. Unsupervised clustering analysis based on the biomarker analysis separated the CTC negative patients from the CTC positive patients. Profiling multiple immune and cancer markers on cancer samples with multi-parametric flow cytometry allowed us to obtain protein expression information at the single cell level. Clustering analysis of the proteomic data revealed a signature driven by checkpoint marker expression on CD103+CD4+ T cells that could potentially be predictive of CTCs and targets of therapy.

For the identification of decomposed dead bodies and skeletal remains, estimation of stature is of utmost importance. In estimating stature from the percutaneous bone lengths or skeletal remains, regression analysis has been the method of choice. Technically, as the regression line is just the best fit to the data, the estimate of stature differs from the actual stature, and this difference is reported in terms of the root mean square error (RMSE). The estimate using regression analysis is probabilistic in nature and there is a confidence interval about the prediction. This interval is mostly not reported in various studies, neither are the probable errors in estimation. In the likelihood ratio approach, absolute “matching” or “identification” is not possible and therefore, only the quantitative strength of evidence (strength of association in the present context) is given.The sample for the present study comprised of 344 young adults (172 females and 172 males) residing in the Shimla city of Himachal Pradesh State of north India. The data have been generated from anthropometric measurements of seven percutaneous bone lengths besides stature. The present study reports the regression equations for stature estimation as traditionally calculated, but additionally, it signifies the determination of the confidence intervals of the regression line as well as the confidence intervals of the prediction of stature. The study also shows that for the likelihood ratio estimation, the regression analysis needs to be flipped, i.e. instead of using regression of stature versus bone length; the regression of bone length versus stature should be used.Practical application of finding the likelihood ratio of a recovered bone to be associated with two missing persons of known stature has also been discussed in the current study. This approach is further extended to a general scenario, where the likelihood of a person of known stature is evaluated with reference to any other person chosen at random from the relevant population. The likelihood ratios arrived at, have been found to be generally low, meaning that the strength of association of stature and percutaneous bone length is not very high, and combining likelihood ratios from different methods of personal identification may be needed.

Estimation of sex is an essential parameter which helps in establishing the biological profile of the deceased in forensic and archaeological examinations. There are two types of methods useful in the estimation of sex –morphological and morphometric. While morphological analysis is subjective and involves visual inspection of the dimorphic traits, morphometric analysis involves statistical analysis and comparison of the measurements to develop a probabilistic estimation of sex. Most studies estimate sex using Discriminant Function Analysis (DFA) that has many restrictive conditions, however, the logistic regression does not have these restrictions and is suitable for a two class problem like estimation of sex.In the present investigation, an attempt has been made to estimate sex by developing logistic regression equations using a cross-sectional sample of 344 young adults (172 females and 172 males). Eight anthropometric measurements were taken on the participants with standard procedures. Hand breadth shows the highest sexual dimorphism and is the best classifier of sex, followed by foot length and hand length. A combined regression model using these three variables has also been proposed that can correctly classify sex with an accuracy of 90.1 % and 91.3 % for males and females, respectively. The likelihood ratio is calculated from the normal probability density curve constructed from the mean and standard deviation for various predictors obtained for the population. It has been concluded that the likelihood ratios corroborate the classification well and the likelihood ratio with its threshold at '1′ can be used as a classifier when there are only two classes. The performance of the likelihood ratio classifier in the present study has been found to be equal to the logistic regression classifier.

Regio- and stereoselective 1,3-dipolar cycloadditions of C-(3-indolyl)-N-phenylnitrone (10) were carried out with different mono-substituted, disubstituted and cyclic dipolarophiles under mono-mode microwave irradiation to obtain substituted 3-(indol-3′-yl)-N-phenyl-isoxazolidines (16–22). Reactions of nitrone (10) with allenic esters under similar conditions afforded, via a domino process, bis-indole derivatives (23a–c) along with compounds 24 and 25. Similarly, reactions of C-(3-pyridyl)-N-phenylnitrone (26) with mono-substituted, disubstituted and cyclic dipolarophiles were carried out in refluxing dry toluene to obtain substituted 3-(3′-pyridyl)-N-phenylisoxazolidines (27–34). Some of the compounds (16f, 18b, 23a, 23c, 27c and 29f) display significant cytotoxicity against a number of human cancer cell lines.

Endomyocardial fibrosis is a progressive disease of unknown origin affecting children and young adults. It involves inflow portion of right and/or left ventricle and apex. It may be associated with thrombus. Literature regarding right ventricular endomyocardial fibrosis with thrombus is scarce. Here we report a rare case of right ventricular endomyocardial fibrosis presenting as ventricular tachycardia and echocardiographic evidence of apical thrombus. Interestingly there was no pulmonary involvement or evidence of deep venous thrombosis. This case also underscores the importance of urgent echocardiography in diagnosis of obscure cases of ventricular tachycardia.

Monoclonal gammopathy can accompany diverse conditions and is usually benign. It should be distinguished from monoclonal gammopathy of undetermined significance (MGUS) which can rarely turn malignant. Visceral leishmaniasis has only rarely been associated with monoclonal gammopathy. We describe the case of a 55-year-old male who had monoclonal gammopathy associated with visceral leishmanisais, which reversed with stibogluconate therapy. Keywords: visceral leishmaniasis, monoclonal gammopathy

Monoclonal gammopathy can accompany diverse conditions and is usually benign. It should be distinguished from monoclonal gammopathy of undetermined significance (MGUS) which can rarely turn malignant. Visceral leishmaniasis has only rarely been associated with monoclonal gammopathy. We describe the case of a 55-year-old male who had monoclonal gammopathy associated with visceral leishmanisais, which reversed with stibogluconate therapy.