Investigating the phenology and interactions of competitive plant species co-occurring with invasive Lantana camara in Indian Himalayan Region.

Invasive plant species are considered one of the significant drivers of habitat loss, leading to biodiversity loss. They have also been observed to alter the local ecology, resulting in a decline of native flora. The management of invasive species is widely recognised as one of the most severe challenges to biodiversity conservation. The International Union for Conservation of Nature (IUCN) considers Lantana camara, as one of the ten worst weeds. Over time, native and indigenous species may evolve to co-exist or compete with invasive species, reducing invader fitness. It is observed that species competition fluctuates throughout environmental gradients, life phases, and abundances. Hence, competition outcome is very context-dependent. To address this challenge, we conducted a comprehensive study in three phases: we identified native species coexisting with Lantana in their natural habitats in the Doon Valley (Phase I) and documented the phenotypic traits of selected coexisting species using the Landmark BBCH (Biologische Bun-desantalt, Bundessortenamt und Chemische Industrie) scale, revealing the phenological growth patterns of selected co-existing species (Phase II). This was followed by conducting pot (Phase IIIa) and field (Phase IIIb) experiments to study the interactions between them. Notably, Justicia adhatoda, Broussonetia papyrifera, Pongamia pinnata, Urtica dioica and Bauhinia variegata demonstrated promising results in both pot and field conditions. Furthermore, after the mechanical removal of Lantana and prior to the plantation in the field experiments, four native grass species were introduced using the seed ball method. Among these, Pennisetum pedicellatum and Sorghum halpense exhibited prompt regeneration and effectively colonised the field, densely covering the cleared area. The study provides a comprehensive management plan for the restoration of Lantana affected areas through competition using native species. This study utilizes phenological assessment for native plant selection using reclamation from native grasses and proposes a management plan for combating invasive Lantana.

Novel Anti-B-cell Maturation Antigen Alpha-Amanitin Antibody-drug Conjugate HDP-101 Shows Superior Activity to Belantamab Mafodotin and Enhanced Efficacy in Deletion 17p Myeloma Models.

B-cell maturation antigen (BCMA) plays a pathobiologic role in myeloma and is a validated target with five BCMA-specific therapeutics having been approved for relapsed/refractory disease. However, these drugs are not curative, and responses are inferior in patients with molecularly-defined high-risk disease, including those with deletion 17p (del17p) involving the tumor suppressor TP53, supporting the need for further drug development. Del17p has been associated with reduced copy number and gene expression of RNA polymerase II subunit alpha (POLR2A) in other tumor types. We therefore studied the possibility that HDP-101, an anti-BCMA antibody drug conjugate (ADC) with the POLR2A poison α-amanitin could be an attractive agent in myeloma, especially with del17p. HDP-101 reduced viability in myeloma cell lines representing different molecular disease subtypes, and overcame adhesion-mediated and both conventional and novel drug resistance. After confirming that del17p is associated with reduced POLR2A levels in publicly available myeloma patient databases, we engineered TP53 wild-type cells with a TP53 knockout (KO), POLR2A knockdown (KD), or both, the latter to mimic del17p. HDP-101 showed potent anti-myeloma activity against all tested cell lines, and exerted enhanced efficacy against POLR2A KD and dual TP53 KO/POLR2A KD cells. Mechanistic studies showed HDP-101 up-regulated the unfolded protein response, activated apoptosis, and induced immunogenic cell death. Notably, HDP-101 impacted CD138-positive but not-negative primary cells, showed potent efficacy against aldehyde dehydrogenase-positive clonogenic cells, and eradicated myeloma in an in vivo cell line-derived xenograft (CDX). Interestingly, in the CDX model, prior treatment with HDP-101 precluded subsequent engraftment on tumor cell line rechallenge in a manner that appeared to be dependent in part on natural killer cells and macrophages. Finally, HDP-101 was superior to the BCMA-targeted ADC belantamab mafodotin against cell lines and primary myeloma cells in vitro, and in an in vivo CDX. Together, the data support the rationale for translation of HDP-101 to the clinic, where it is now undergoing Phase I trials, and suggest that it could emerge as a more potent ADC for myeloma with especially interesting activity against the high-risk del17p myeloma subtype.

Common foods for boosting human immunity: A review.

We are frequently exposed to potentially harmful microbes of various types on a daily basis. Our immune system is an amazing collection of unique organs and cells that defends us from hazardous germs as well as certain diseases. It plays a crucial role in protecting the body against external invaders, including bacteria, viruses, and parasites. Maintaining a healthy immune system requires consuming a balanced diet that provides a variety of macro- and micronutrients. By consuming sufficient amounts of water, minerals such as zinc and magnesium, micronutrients, herbs, and foods rich in vitamins C, D, and E, and adopting a healthy lifestyle, one can enhance their health and immunity, and prevent infections. This article provides a comprehensive review of the scientific literature on common foods known for their potential to boost human immunity. The review begins by discussing the various components of the immune system and their functions. It then delves into the current understanding of how nutrition can influence immune response, highlighting the importance of a well-balanced diet in supporting optimal immune function. The article presents an extensive analysis of a range of common foods that have been studied for their immune-boosting properties. These foods include fruits, vegetables, whole grains, and animal-based foods. Each food category is explored in terms of its specific nutrients and bioactive compounds that contribute to immune support. Foods such as milk, eggs, fruits, leafy greens, and spices like onion, garlic, and turmeric contain beneficial compounds that can enhance the immune system's function, activate and inhibit immune cells, and interfere with multiple pathways that eventually lead to improved immune responses and defense. The available literature on the issue was accessed via online resources and evaluated thoroughly as a methodology for preparing this manuscript.

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.

Advancing Sowing Time and Conservation Tillage - The Climate-Resilient Approach to Enhance the Productivity and Profitability of Wheat.

Field experiments consisting of two sowing time (early and timely), two tillage options (conventional tillage and conservation tillage) and ten genotypes were conducted with the aim to maximize the wheat productivity and profitability. The early sowing (second fortnight of October) produced 16.0% higher grain yield compared to timely sowing (mid-November) in northern Indian Plains. However, no significant yield differences were observed between conventional tillage (CT) and conservation tillage (CST) practices. Among genotypes, the better yielders were PBW 723, BISA 927 and HD 2967. The interaction of sowing time and genotype had a significant (p < 0.05) effect on wheat yield. However, the interaction of genotype and tillage did not produce any significant response on wheat yield. The experiments conducted at farmer's fields also demonstrated similar performance of wheat under CT and CST systems but CST offered the savings of more than Rs. 3500 (US $ 47) along with 125 kg ha- 1 lesser CO2 emissions over CT due to reduction in fuel consumption associated with tillage and seed bed operations. At farmers field also, early sown wheat yielded 5.5% higher over wheat sown in November. The results of present studies show that early sowing of high yielding wheat genotypes under CST practice enhanced the productivity and profitability of wheat under rice-wheat cropping system along with lesser noxious impact on the environment. Amidst climate vagary and its menace on the agriculture, the adoption of climate-resilient management practices such as advancing the sowing time and conservation tillage can improve the productivity of long duration wheat cultivars in sub-tropical humid conditions besides lesser deleterious consequences on the environment.

Efficient Herbicide Delivery through a Conjugate Gel Formulation for the Mortality of Broad Leaf Weeds.

Carfentrazone-ethyl is embedded in guar gum to prepare a polymer-herbicide conjugate gel formulation for a sustained release of the active ingredient (a.i.). The sprayable gel formulation was optimized at 0.5% (w/v) concentration. Strong interactions of the prepared composition of the polymer-herbicide conjugate system are shown through spectroscopic techniques, depicting the peak broadening of hydrophilic -OH bonds in the herbicide at 1743 cm-1, shifting to 1730 cm-1 in the polymer-herbicide sample. There is a broadening and shifting of the peak at 329 nm for the n → π* transition at 335 nm in the polymer-herbicide conjugate system in UV spectra. Differential scanning calorimetric measurements show a lowering of endothermic melting peaks to 242 and 303 °C in the polymer-herbicide conjugate. X-ray diffraction studies showed a sharp diffraction peak of the pure polymer at a 2θ of ∼20.3°, while broadening and shifting of the peak position to a 2θ of ∼20.8° were observed after adding the herbicide. Diffusion of the active ingredient in the polymer-herbicide conjugate resulted in much greater coverage (most of the weed leaf stomata (>95%)) than conventional spraying. The efficacy of both the polymer-herbicide formulation and herbicide at different doses in weed nurseries showed significantly higher weed mortality in Anagallis arvensis (95.4%), Chenopodium album (∼97%), and Ageratum conyzoides (93.16%) treated with the polymer-herbicide formulation @ 20 g a.i. ha-1. Narrow SPAD readings range of A. arvensis (0.1-30.6) and that of C. album (0-5) were observed in the polymer-herbicide formulation @ 20 g a.i. ha-1 was at par with the conventional formulation @ 30 g a.i. ha-1. Less regeneration in a weed nursery of A. arvensis (27%), C. album (77%), and A. conyzoides (49%) treated with gel formulations @ 20 g a.i. ha-1 was observed, which was significantly lower than those in conventional herbicides.

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.

Syringic acid, a novel thyroid hormone receptor-ß agonist, ameliorates propylthiouracil-induced thyroid toxicity in rats.

The aim of this study was to evaluate the potential of syringic acid (SA) against propylthiouracil (PTU)-induced hypothyroidism in rats. SA at a prestandardized dose, 50 mg/kg/day, was orally administered to PTU-induced hypothyroid rats for 30 days, and alterations in the levels of serum triiodothyronine (T3 ), thyroxine (T4 ), thyrotropin (TSH), alanine transaminase (ALT), and aspartate transaminase (AST); tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6); total cholesterol (CHOL) and triglycerides (TG); hepatic lipid peroxidation (LPO) and antioxidants (superoxide dismutase, catalase, glutathione peroxidase, and glutathione content), as well as histological changes in liver and thyroid were examined. The molecular interactions of the ligand, SA, with thyroid-related protein targets, such as human thyroid hormone receptor ß (hTRß), and thyroid peroxidase (TPO) protein, were studied using molecular docking. Whereas in hypothyroid animals, T4 , T3 , and antioxidants were decreased, there was an increase in TSH, TNF-α, IL-6, ALT, AST, and hepatic LPO; administration of SA in PTU-induced animals reversed all these indices to near normal levels. SA also improved the histological features of liver and thyroid gland. Our study clearly demonstrates SA as a novel thyroid agonist for augmenting the thyroid functions in rats. Molecular docking analysis reveals that SA possesses good binding affinity toward both the targets, hTRß and TPO. Through this approach, for the first time we provide the evidence for SA as a novel thyroid agonist and suggest a receptor-mediated mechanism for its thyroid stimulatory potential.

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.

Targeting phosphatidylinositol 3 kinase-ß and -δ for Bruton tyrosine kinase resistance in diffuse large B-cell lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma; 40% of patients relapse after a complete response or are refractory to therapy. To survive, the activated B-cell (ABC) subtype of DLBCL relies upon B-cell receptor signaling, which can be modulated by the activity of Bruton tyrosine kinase (BTK). Targeting BTK with ibrutinib, an inhibitor, provides a therapeutic approach for this subtype of DLBCL. However, non-Hodgkin lymphoma is often resistant to ibrutinib or acquires resistance soon after exposure. We explored how this resistance develops. We generated 3 isogenic ibrutinib-resistant DLBCL cell lines and investigated the deregulated pathways known to be associated with tumorigenic properties. Reduced levels of BTK and enhanced phosphatidylinositol 3-kinase (PI3K)/AKT signaling were hallmarks of these ibrutinib-resistant cells. Upregulation of PI3K-ß expression was demonstrated to drive resistance in ibrutinib-resistant cells, and resistance was reversed by the blocking activity of PI3K-ß/δ. Treatment with the selective PI3K-ß/δ dual inhibitor KA2237 reduced both tumorigenic properties and survival-based PI3K/AKT/mTOR signaling of these ibrutinib-resistant cells. In addition, combining KA2237 with currently available chemotherapeutic agents synergistically inhibited metabolic growth. This study elucidates the compensatory upregulated PI3K/AKT axis that emerges in ibrutinib-resistant cells.

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.

Modelling Agriculture, Forestry and Other Land Use (AFOLU) in response to climate change scenarios for the SAARC nations.

Agriculture and forestry are the two major land use classes providing sustenance to the human population. With the pace of development, these two land use classes continue to change over time. Land use change is a dynamic process under the influence of multiple drivers including climate change. Therefore, tracing the trajectory of the changes is challenging. The artificial neural network (ANN) has successfully been applied for tracing such a dynamic process to capture nonlinear responses. We test the application of the multilayer perceptron neural network (MLP-NN) to project the future Agriculture, Forestry and Other Land Use (AFOLU) for the year 2050 for the South Asian Association for Regional Cooperation (SAARC) nations which is a geopolitical union of Afghanistan, Bangladesh, Bhutan, India, Nepal, Maldives, Pakistan and Sri Lanka. The Intergovernmental Panel on Climate Change (IPCC) and Food and Agriculture Organization (FAO) use much frequently the term 'AFOLU' in their policy documents. Hence, we restricted our land use classification scheme as AFOLU for assessing the influence of climate change scenarios of the IPCC fifth assessment report (RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5). Agricultural land would increase in all the SAARC nations, with the highest increase in Pakistan and Maldives; moderate increase in Afghanistan, India and Nepal; and the least increase in Bangladesh, Bhutan and Sri Lanka. The forestry land use will witness a decreasing trend under all scenarios in all of the SAARC nations with varying levels of changes. The study is expected to assist planners and policymakers to develop nations' specific strategy to proportionate land use classes to meet various needs on a sustainable basis.

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.

Application of National Aerosol Facility (NAF) in Designing of a Ventilation System for Isolation Rooms to Minimize Interpersonal Exposure of Sneezing/Coughing.

With the outbreak of pandemic COVID-19, protection of public and health workers has become a national priority. In this regard, it is desirable to study the coughing- and sneezing-generated pathogen aerosols, its dispersion and transportation in isolation rooms, clinics, confined spaces and other general public places to evolve efficient ventilation system along with suitable protective measures to limit the spread of the virus. The present paper describes the overall experimental scheme supported with computational fluid dynamics evaluation to address this problem for evolution of optimal ventilation system using the National Aerosol Facility at IIT Kanpur set up in collaboration with BARC Trombay. The outcome of this study is aimed to evolve a national standard for optimum isolation rooms that would provide adequate protection to health workers.

Ubiquitin-activating enzyme inhibition induces an unfolded protein response and overcomes drug resistance in myeloma.

Three proteasome inhibitors have garnered regulatory approvals in various multiple myeloma settings; but drug resistance is an emerging challenge, prompting interest in blocking upstream components of the ubiquitin-proteasome pathway. One such attractive target is the E1 ubiquitin-activating enzyme (UAE); we therefore evaluated the activity of TAK-243, a novel and specific UAE inhibitor. TAK-243 potently suppressed myeloma cell line growth, induced apoptosis, and activated caspases while decreasing the abundance of ubiquitin-protein conjugates. This was accompanied by stabilization of many short-lived proteins, including p53, myeloid cell leukemia 1 (MCL-1), and c-MYC, and activation of the activating transcription factor 6 (ATF-6), inositol-requiring enzyme 1 (IRE-1), and protein kinase RNA-like endoplasmic reticulum (ER) kinase (PERK) arms of the ER stress response pathway, as well as oxidative stress. UAE inhibition showed comparable activity against otherwise isogenic cell lines with wild-type (WT) or deleted p53 despite induction of TP53 signaling in WT cells. Notably, TAK-243 overcame resistance to conventional drugs and novel agents in cell-line models, including bortezomib and carfilzomib resistance, and showed activity against primary cells from relapsed/refractory myeloma patients. In addition, TAK-243 showed strong synergy with a number of antimyeloma agents, including doxorubicin, melphalan, and panobinostat as measured by low combination indices. Finally, TAK-243 was active against a number of in vivo myeloma models in association with activation of ER stress. Taken together, the data support the conclusion that UAE inhibition could be an attractive strategy to move forward to the clinic for patients with relapsed and/or refractory multiple myeloma.

Protein targeting chimeric molecules specific for bromodomain and extra-terminal motif family proteins are active against pre-clinical models of multiple myeloma.

Bromodomain and extraterminal (BET) domain containing protein (BRD)-4 modulates the expression of oncogenes such as c-myc, and is a promising therapeutic target in diverse cancer types. We performed pre-clinical studies in myeloma models with bi-functional protein-targeting chimeric molecules (PROTACs) which target BRD4 and other BET family members for ubiquitination and proteasomal degradation. PROTACs potently reduced the viability of myeloma cell lines in a time-dependent and concentration-dependent manner associated with G0/G1 arrest, reduced levels of CDKs 4 and 6, increased p21 levels, and induction of apoptosis. These agents specifically decreased cellular levels of downstream BRD4 targets, including c-MYC and N-MYC, and a Cereblon-targeting PROTAC showed downstream effects similar to those of an immunomodulatory agent. Notably, PROTACs overcame bortezomib, dexamethasone, lenalidomide, and pomalidomide resistance, and their activity was maintained in otherwise isogenic myeloma cells with wild-type or deleted TP53. Combination studies showed synergistic interactions with dexamethasone, BH3 mimetics, and Akt pathway inhibitors. BET-specific PROTACs induced a rapid loss of viability of primary cells from myeloma patients, and delayed growth of MM1.S-based xenografts. Our data demonstrate that BET degraders have promising activity against pre-clinical models of multiple myeloma, and support their translation to the clinic for patients with relapsed and/or refractory disease.

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).