Murine Typhus Presenting with Acute Psychosis and Disseminated Intravascular Coagulation: A Case Report

Murine typhus is an endemic infectious disease caused by Rickettsia typhi and is transmitted by fleas. It typically causes a mild illness with symptoms of fever, rash, headache, chills, and non-specific gastrointestinal complaints. However, there have been no reported cases in the literature of murine typhus infection causing symptoms of acute psychosis and disseminated intravascular coagulation (DIC). A 30-year-old female with a history of gastric bypass and chronic pain syndrome presented to the emergency department with altered mental state and fever. She developed vivid visual hallucinations, DIC, and hypoxia with pulmonary opacities, ultimately requiring intubation. Magnetic resonance imaging (MRI) showed leptomeningeal enhancement with unremarkable cerebrospinal fluid (CSF) studies. Serum murine typhus serology came back positive. Doxycycline therapy was initiated, which resulted in complete patient recovery. This case shows that murine typhus infection may present with acute psychosis and can mimic DIC, leading to diagnostic confusion. MRI sequences may show leptomeningeal enhancement, which has never been reported before in patients with typhus. Early neurological imaging using advanced MRI sequences for patients presenting with altered sensorium, visual hallucinations, and symptoms similar to thrombotic thrombocytopenic purpura (TTP) may help with early diagnosis, decreased hospital stay, and better prognosis

MicroRNA and cDNA-Microarray as Potential Targets against Abiotic Stress Response in Plants: Advances and Prospects

Abiotic stresses, such as temperature (heat and cold), salinity, and drought negatively affect plant productivity; hence, the molecular responses of abiotic stresses need to be investigated. Numerous molecular and genetic engineering studies have made substantial contributions and revealed that abiotic stresses are the key factors associated with production losses in plants. In response to abiotic stresses, altered expression patterns of miRNAs have been reported, and, as a result, cDNA-microarray and microRNA (miRNA) have been used to identify genes and their expression patterns against environmental adversities in plants. MicroRNA plays a significant role in environmental stresses, plant growth and development, and regulation of various biological and metabolic activities. MicroRNAs have been studied for over a decade to identify those susceptible to environmental stimuli, characterize expression patterns, and recognize their involvement in stress responses and tolerance. Recent findings have been reported that plants assign miRNAs as critical post-transcriptional regulators of gene expression in a sequence-specific manner to adapt to multiple abiotic stresses during their growth and developmental cycle. In this study, we reviewed the current status and described the application of cDNA-microarray and miRNA to understand the abiotic stress responses and different approaches used in plants to survive against different stresses. Despite the accessibility to suitable miRNAs, there is a lack of simple ways to identify miRNA and the application of cDNA-microarray. The elucidation of miRNA responses to abiotic stresses may lead to developing technologies for the early detection of plant environmental stressors. The miRNAs and cDNA-microarrays are powerful tools to enhance abiotic stress tolerance in plants through multiple advanced sequencing and bioinformatics techniques, including miRNA-regulated network, miRNA target prediction, miRNA identification, expression profile, features (disease or stress, biomarkers) association, tools based on machine learning algorithms, NGS, and tools specific for plants. Such technologies were established to identify miRNA and their target gene network prediction, emphasizing current achievements, impediments, and future perspectives. Furthermore, there is also a need to identify and classify new functional genes that may play a role in stress resistance, since many plant genes constitute an unexplained fraction

The Transcriptional Landscape and Hub Genes Associated with Physiological Responses to Drought Stress in Pinus tabuliformis

Drought stress has an extensive impact on regulating various physiological, metabolic, and molecular responses. In the present study, the Pinus tabuliformis transcriptome was studied to evaluate the drought-responsive genes using RNA- Sequencing approache. The results depicted that photosynthetic rate and H2O conductance started to decline under drought but recovered 24 h after re-watering; however, the intercellular CO2 concentration (Ci) increased with the onset of drought. We identified 84 drought-responsive transcription factors, 62 protein kinases, 17 transcriptional regulators, and 10 network hub genes. Additionally, we observed the expression patterns of several important gene families, including 2192 genes positively expressed in all 48 samples, and 40 genes were commonly co-expressed in all drought and recovery stages compared with the control samples. The drought-responsive transcriptome was conserved mainly between P. tabuliformis and A. thaliana, as 70% (6163) genes had a homologous in arabidopsis, out of which 52% homologous (3178 genes corresponding to 2086 genes in Arabidopsis) were also drought response genes in arabidopsis. The collaborative network exhibited 10 core hub genes integrating with ABA-dependent and independent pathways closely conserved with the ABA signaling pathway in the transcription factors module. PtNCED3 from the ABA family genes had shown significantly different expression patterns under control, mild, prolonged drought, and recovery stages. We found the expression pattern was considerably increased with the prolonged drought condition. PtNCED3 highly expressed in all drought-tested samples; more interestingly, expression pattern was higher under mild and prolonged drought. PtNCED3 is reported as one of the important regulating enzymes in ABA synthesis. The continuous accumulation of ABA in leaves increased resistance against drought was due to accumulation of PtNCED3 under drought stress in the pine needles

A Deep Learning-Based Privacy-Preserving Model for Smart Healthcare in Internet of Medical Things Using Fog Computing

With the emergence of COVID-19, smart healthcare, the Internet of Medical Things, and big data-driven medical applications have become even more important. The biomedical data produced is highly confidential and private. Unfortunately, conventional health systems cannot support such a colossal amount of biomedical data. Hence, data is typically stored and shared through the cloud. The shared data is then used for different purposes, such as research and discovery of unprecedented facts. Typically, biomedical data appear in textual form (e.g., test reports, prescriptions, and diagnosis). Unfortunately, such data is prone to several security threats and attacks, for example, privacy and confidentiality breach. Although significant progress has been made on securing biomedical data, most existing approaches yield long delays and cannot accommodate real-time responses. This paper proposes a novel fog-enabled privacy-preserving model called [Formula: see text] sanitizer, which uses deep learning to improve the healthcare system. The proposed model is based on a Convolutional Neural Network with Bidirectional-LSTM and effectively performs Medical Entity Recognition. The experimental results show that [Formula: see text] sanitizer outperforms the state-of-the-art models with 91.14% recall, 92.63% in precision, and 92% F1-score. The sanitization model shows 28.77% improved utility preservation as compared to the state-of-the-art

In Vitro Release Studies of Diclofenac Potassium Tablet from Pure and Blended Mixture of Hydrophilic and Hydrophobic Polymers

The purpose of the present study was to evaluate the effect of pure and blended mixtures, with different compositions of hydroxy propyl methyl cellulose (HPMC) and carnauba wax (CW) on the release of diclofenac potassium from matrix tablets. Fifteen different matrix tablet formulations were prepared by direct compression process by using Carver Hydraulic laboratory press having 13 mm flat dies set at constant pressure. The paddle dissolution apparatus II (Curio DL 2020) was used to assess the dissolution of drug in phosphate buffer, pH 7.4 for 8 h. The release data was fitted to different release models. Zoom stereo micrography was done to evaluated the release mechanism of drug from polymers. The interaction of polymer mixture and different ratios of drug in polymer mixture was determined by Fourier transform infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC). The type and content of polymer in the matrix system influenced the release characteristics. Higher polymeric content in the matrix decreased the drug release rate because of increased tortuosity and decreased porosity. Retardation of drug release from pure carnauba was higher as compared to that with pure HPMC matrices. The polymers blends controlled drug release pattern effectively. The drug released showed better linearity with Higuchi release kinetics. The Korsmeyer equation revealed n value ranged from 0.388-0.627 or non - Fickian transport mechanism of drug release was predominant. The FTIR and DSC suggested that there were no chemical interaction between drug and polymers.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Metabolomic analyses provide insights into the preharvest rind disorder in Satsuma Owari Mandarin

Citrus fruit’s appearance is the primary criterion used to assess its quality for the fresh market, hence the rind’s condition is a crucial quality trait. Pre-harvest rind disorder is one of the major physiological problems in mandarins. The disorder occurs right before harvest following rain events in some Mandarin varieties. Despite the economic damage caused by this kind of disorder, very limited information is available about the molecular mechanisms underlying the occurrence of this disorder. In the present study, we evaluated the primary metabolites, antioxidants, and hormones associated with the pre-harvest rind disorder in Mandarins. The study was carried out using ten-year-old ‘Owari’ Satsuma mandarin trees grafted on ‘Carrizo’ rootstock and grown in a commercial orchard in San Joaquin Valley, California, USA. Samples were collected from healthy tissue of healthy fruit (HF_HT), healthy tissue of damaged fruit (DF_HT), and damaged tissue of damaged fruit (DF_DT). Damaged fruit (DF_HT and DF_DT) showed lower cellulose concentrations than healthy fruit tissues (HF_HT), however, had similar contents of pectin and hemicellulose. The antioxidant activities showed no significant difference in all paired comparisons between samples as expressed in the malondialdehyde (MDA) content. However, DF_DT had a higher H2O2 content compared to HF_HT, but DF_HT had a similar content to that of HF_HT. Furthermore, peroxidase (POD) and polyphenol oxidase (PPO) activities were increased in DF_DT compared to HF_HT (P = 0.0294) and DF_HT (P = 0.0044), respectively. Targeted metabolomics analysis revealed that a total of 76 metabolites were identified in Satsuma rind tissues, and the relative concentrations of 43 metabolites were significantly different across studied samples. The hormonal analysis showed the involvement of jasmonate O-methyltransferase, jasmonic acid-amido synthetase JAR1-like, and JA-isoleucine may key role in causing the rind disorder in mandarins. In addition, the damaged fruit tissues have a higher level of jasmonic acid (JA), 12-oxo-phytodienoic acid, and JA-isoleucine than undamaged tissue

What is Emotional Pain? - A Review of Pathophysiology and Treatment Options

Pain is a dynamic process that involves multiple physiological systems for the perception and its outcomes. The basic pain process involves a complex neurological process. and biochemical changes. Emotional pain is an extended form of the already known pain where the stimuli are emotional of nature that involves abstract feelings, for example, losing a loved one. Like the pain, now there is a growing evidence that emotional pain also involves inflammatory process and the behavioral approach is directly linked with them. This association can help us modify the emotional pain by modulating the inflammatory processes that already known to us.   Other than therapeutic and known interventions, mindfulness, writing therapy, exposure based intervention and other actions can help us in just not treating the condition but can serve as exploration of other avenues of the pain physiology. Emotional Pain has always been the part of human history but one of the least discussed form of pain. Multiple neuropsychiatric studies can help further in evaluation of this process

A comprehensive review on Gossypium hirsutum resistance against cotton leaf curl virus

Cotton (Gossypium hirsutum L.) is a significant fiber crop. Being a major contributor to the textile industry requires continuous care and attention. Cotton is subjected to various biotic and abiotic constraints. Among these, biotic factors including cotton leaf curl virus (CLCuV) are dominant. CLCuV is a notorious disease of cotton and is acquired, carried, and transmitted by the whitefly (Bemisia tabaci). A cotton plant affected with CLCuV may show a wide range of symptoms such as yellowing of leaves, thickening of veins, upward or downward curling, formation of enations, and stunted growth. Though there are many efforts to protect the crop from CLCuV, long-term results are not yet obtained as CLCuV strains are capable of mutating and overcoming plant resistance. However, systemic-induced resistance using a gene-based approach remained effective until new virulent strains of CLCuV (like Cotton Leaf Curl Burewala Virus and others) came into existence. Disease control by biological means and the development of CLCuV-resistant cotton varieties are in progress. In this review, we first discussed in detail the evolution of cotton and CLCuV strains, the transmission mechanism of CLCuV, the genetic architecture of CLCuV vectors, and the use of pathogen and nonpathogen-based approaches to control CLCuD. Next, we delineate the uses of cutting-edge technologies like genome editing (with a special focus on CRISPR-Cas), next-generation technologies, and their application in cotton genomics and speed breeding to develop CLCuD resistant cotton germplasm in a short time. Finally, we delve into the current obstacles related to cotton genome editing and explore forthcoming pathways for enhancing precision in genome editing through the utilization of advanced genome editing technologies. These endeavors aim to enhance cotton’s resilience against CLCuD

Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017

Background: Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods: We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2\ub75th percentile and 100 as the 97\ub75th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator. Findings: The global median health-related SDG index in 2017 was 59\ub74 (IQR 35\ub74–67\ub73), ranging from a low of 11\ub76 (95% uncertainty interval 9\ub76–14\ub70) to a high of 84\ub79 (83\ub71–86\ub77). SDG index values in countries assessed at the subnational level varied substantially, particularly in China and India, although scores in Japan and the UK were more homogeneous. Indicators also varied by SDI quintile and sex, with males having worse outcomes than females for non-communicable disease (NCD) mortality, alcohol use, and smoking, among others. Most countries were projected to have a higher health-related SDG index in 2030 than in 2017, while country-level probabilities of attainment by 2030 varied widely by indicator. Under-5 mortality, neonatal mortality, maternal mortality ratio, and malaria indicators had the most countries with at least 95% probability of target attainment. Other indicators, including NCD mortality and suicide mortality, had no countries projected to meet corresponding SDG targets on the basis of projected mean values for 2030 but showed some probability of attainment by 2030. For some indicators, including child malnutrition, several infectious diseases, and most violence measures, the annualised rates of change required to meet SDG targets far exceeded the pace of progress achieved by any country in the recent past. We found that applying the mean global annualised rate of change to indicators without defined targets would equate to about 19% and 22% reductions in global smoking and alcohol consumption, respectively; a 47% decline in adolescent birth rates; and a more than 85% increase in health worker density per 1000 population by 2030. Interpretation: The GBD study offers a unique, robust platform for monitoring the health-related SDGs across demographic and geographic dimensions. Our findings underscore the importance of increased collection and analysis of disaggregated data and highlight where more deliberate design or targeting of interventions could accelerate progress in attaining the SDGs. Current projections show that many health-related SDG indicators, NCDs, NCD-related risks, and violence-related indicators will require a concerted shift away from what might have driven past gains—curative interventions in the case of NCDs—towards multisectoral, prevention-oriented policy action and investments to achieve SDG aims. Notably, several targets, if they are to be met by 2030, demand a pace of progress that no country has achieved in the recent past. The future is fundamentally uncertain, and no model can fully predict what breakthroughs or events might alter the course of the SDGs. What is clear is that our actions—or inaction—today will ultimately dictate how close the world, collectively, can get to leaving no one behind by 2030

Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017.

BACKGROUND: Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of 'leaving no one behind', it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990-2017, projected indicators to 2030, and analysed global attainment. METHODS: We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0-100, with 0 as the 2·5th percentile and 100 as the 97·5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator