The Kaleidoscope of Polyautoimmunity: An Odyssey of Diagnostic Dilemmas.

Diagnostic accuracy is of the utmost importance, both in the clinical setting and for research purposes. Mixed connective tissue disease (MCTD), rheumatoid arthritis (RA), Sjogren's syndrome (SS), and overlap syndrome (OS) frequently exhibit symptoms that mimic those of other conditions. Unfortunately, there is no singular definitive test for diagnosing these connective tissue diseases (CTDs), necessitating the reliance on expert opinions. Further complicating the matter, these diseases have overlapping clinical and serological features, and some individuals with one autoimmune disease may develop additional autoimmune disorders, either concurrently or at a later stage of their ailment. Autoimmune diseases (ADs) may manifest as a single AD or, concurrently with other ADs, a condition named polyautoimmunity (polyA). Polyautoimmunity refers to the presence of numerous autoimmune disorders in a single patient. Multiple autoimmune syndrome (MAS) is a condition that occurs when three or more autoimmune illnesses coexist. Moreover, the coexistence of two or more ADs with classification criteria is named "overt polyA," whereas the presence of autoantibodies not related to the index AD, without criteria fulfillment, is termed "latent polyA." Furthermore, both conditions can exist simultaneously within an individual patient. This case report's findings underscore that patients exhibiting both latent and overt polyautoimmunity tend to group, exhibiting distinct clinical and immunological characteristics. Additionally, CTDs not only have overlapping features amongst their various subclasses but also tend to mimic other conditions due to an underlying chronic inflammatory state. This case study also attempts to highlight the diagnostic dilemmas faced in such situations.

Development of Predictive Models Based on Biochemical Parameters to Triage COVID-19 Patients: A Study Conducted in a Tertiary Care Hospital.

Background The COVID-19 disease continues to cause severe mortality and morbidity. Biochemical parameters are being used to predict the severity of the infection. This study aims to predict disease severity and mortality to help reduce mortality through timely intervention in a cost-effective way. Methods A total of 324 COVID-19 cases admitted at our hospital (All India Institute of Medical Sciences, Patna, BR, India) between June 2020 to December 2020 (phase 1: 190 patients) and April 2021 to May 2021 (phase 2: 134 patients) were recruited for this study. Statistical analysis was done using SPSS Statistics version 23 (IBM Corp., Armonk, NY, USA) and model prediction using Python (The Python Software Foundation, Wilmington, DE, USA). Results There were significant differences in biochemical parameters at the time of admission among COVID-19 patients between phases 1 and 2, ICU and non-ICU admissions, and expired and discharged patients. The receiver operating characteristic (ROC) curves predicted mortality solely based on biochemical parameters. Using multiple logistic regression in Python, a total of four models (two each) were developed to predict ICU admission and mortality. A total of 92 out of 96 patients were placed into the correct management category by our model. This model would have allowed us to preserve 17 of the 21 patients we lost. Conclusions We developed predictive models for admission (ICU or non-ICU) and mortality based on biochemical parameters at the time of admission. A predictive model with a significant predictive capability for IL-6 and procalcitonin values using normal biochemical parameters was proposed. Both can be used as machine learning tools to prognosticate the severity of COVID-19 infections. This study is probably the first of its kind to propose triage for admission in the ICU or non-ICU at the medical emergency department during the first presentation for the necessary optimal treatment of COVID-19 based on a predictive model.

Thickness-Dependent Magnetic Breakdown in ZrSiSe Nanoplates.

We report a study of thickness-dependent interband and intraband magnetic breakdown by thermoelectric quantum oscillations in ZrSiSe nanoplates. Under high magnetic fields of up to 30 T, quantum oscillations arising from degenerated hole pockets were observed in thick ZrSiSe nanoplates. However, when decreasing the thickness, plentiful multifrequency quantum oscillations originating from hole and electron pockets are captured. These multiple frequencies can be explained by the emergent interband magnetic breakdown enclosing individual hole and electron pockets and intraband magnetic breakdown within spin-orbit coupling (SOC) induced saddle-shaped electron pockets, resulting in the enhanced contribution to thermal transport in thin ZrSiSe nanoplates. These experimental frequencies agree well with theoretical calculations of the intriguing tunneling processes. Our results introduce a new member of magnetic breakdown to the field and open up a dimension for modulating magnetic breakdown, which holds fundamental significance for both low-dimensional topological materials and the physics of magnetic breakdown.

Lipoprotein Ratios: Correlation With Glycated Hemoglobin (HbA1c) Among Thyroid Disorders' Patients.

INTRODUCTION: Thyroid disorders and diabetes mellitus are prevalent conditions in the modern era. Moreover, glycated hemoglobin (HbA1c) is the established (prognostic as well as diagnostic) marker for long-term glycemic control, whereas the lipid profile is the marker for cardiovascular risks. The association of hypothyroidism with dyslipidemia is also a well-established fact. The current study explores a correlation between thyroid profile, glycemic status, and various lipoprotein indices. OBJECTIVE: To look for an association between thyroid profile, glycemic status, and various lipoprotein indices. METHODOLOGY: The cross-sectional study conducted at AIIMS Gorakhpur included a total of 108 subjects, with 37 normal subjects (Group I) and 71 patients) with T2DM (Type-2 diabetes mellitus) (Group II). Baseline characteristics of the two groups were compared for age, sex, presence of hypertension, fasting blood glucose, and body mass index (BMI). Blood samples were collected from the patients. The sera were analyzed for HbA1c and lipid profile, which included total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). Serum samples were also used to estimate the thyroid stimulating hormone (TSH) and triiodothyronine (fT3). The association between thyroid profile, glycemic status, and various lipoprotein indices was calculated. STATISTICAL ANALYSIS:  Kolmogorov-Smirnov test for normality of the data. Spearmann correlation was used for nonparametric data. RESULTS: There were significantly higher levels of total cholesterol, triglycerides, and LDL-C levels in T2DM subjects than in non-diabetic subjects. There was also a significant positive correlation observed between TSH and TC among the normal control group (ρ =0.348, P=0.04). Similarly, significant positive correlations were found for TG (ρ =0.354, P=0.04) and LDL-C (ρ =0.431, P=0.03) among non-diabetic subjects. Among patients with T2DM, TSH was significantly correlated positively with TG (ρ =0.530, P=0.006) and LDL-C (ρ =0.443, P=0.03). Similarly, in the same group, among lipid ratios, TG/HDL-C (ρ =0.311, P=0.04) and LDL-C/HDL-C (ρ =0.227, P=0.05) were significantly correlated to TSH. Furthermore, there were significant positive correlations between TSH and HbA1c (ρ =0.301, P=0.04). fT3 was found to have a strong negative correlation with HbA1c among patients with T2DM (ρ =-0.454, P=0.02). CONCLUSION: Thyroid disorders exert significant effects on glycemic control and lipid metabolism, which may impact HbA1c levels and lipid profile parameters.

Lipoprotein Ratios: Correlations With Glycated Hemoglobin Among Type 2 Diabetes Mellitus Patients.

Introduction Diabetes mellitus (DM) has become a common disorder in India, and can be even considered as an epidemic in most developing countries. It usually adds a big burden on the economy through its macro and microvascular complications which often require hospitalisation. Glycated hemoglobin (HbA1c) is considered a well-established test to track long-term glycemic control, and hence can be used for both diagnosis and prognosis of disease. On the other hand, lipid profile is a significant marker of cardiovascular risks. Objective To investigate the clinical relevance of lipid profile and correlate with glycemic control in type 2 DM patients. Methodology This observational study used laboratory results (HbA1c and lipid profile) of 140 patients who attended various out-patient departments (OPD) of All India Institute of Medical Sciences (AIIMS), Gorakhpur. On the advice of clinicians, for routine follow-up, blood samples were collected from the patients (aged 20-50 years, 84 males, and 56 females, with a history of more than three years of type 2 DM). The sera were analyzed for HbA1c and lipid profile [which included triglycerides (TG), total cholesterol (TC), low density lipoprotein cholesterol (LDL-C) and high density lipoprotein cholesterol (HDL-C)]. Based on HbA1c levels the study subjects were divided into three groups, namely group I (HbA1c <7%, n=14), group II (HbA1c7%-8.5%, n=91), and group III (HbA1c >8.5%, n=35). Correlation studies between HbA1c and parameters of lipid profile were explored in the study. Data generated were checked for normality and correlation studies were accordingly done. Results Elevated levels of HbA1c were associated with a notable parallel increase in LDL-C levels (P<0.05), TG, and TC. There was no notable correlation observed between HbA1c and HDL-C levels. However, as HbA1c levels increased, the TG/HDL-C and LDL-C/HDL-C ratios displayed a gradual rise (P<0.05). Conclusion LDL-C and the LDL-C/HDL-C ratio serve as valuable tools for evaluating and mitigating cardiovascular disease risk and are correlated to glycemic control among individuals with type 2 DM.

Mechano-regulation by clathrin pit-formation and passive cholesterol-dependent tubules during de-adhesion.

Adherent cells ensure membrane homeostasis during de-adhesion by various mechanisms, including endocytosis. Although mechano-chemical feedbacks involved in this process have been studied, the step-by-step build-up and resolution of the mechanical changes by endocytosis are poorly understood. To investigate this, we studied the de-adhesion of HeLa cells using a combination of interference reflection microscopy, optical trapping and fluorescence experiments. We found that de-adhesion enhanced membrane height fluctuations of the basal membrane in the presence of an intact cortex. A reduction in the tether force was also noted at the apical side. However, membrane fluctuations reveal phases of an initial drop in effective tension followed by saturation. The area fractions of early (Rab5-labelled) and recycling (Rab4-labelled) endosomes, as well as transferrin-labelled pits close to the basal plasma membrane, also transiently increased. On blocking dynamin-dependent scission of endocytic pits, the regulation of fluctuations was not blocked, but knocking down AP2-dependent pit formation stopped the tension recovery. Interestingly, the regulation could not be suppressed by ATP or cholesterol depletion individually but was arrested by depleting both. The data strongly supports Clathrin and AP2-dependent pit-formation to be central to the reduction in fluctuations confirmed by super-resolution microscopy. Furthermore, we propose that cholesterol-dependent pits spontaneously regulate tension under ATP-depleted conditions.

Pre-analytical errors in coagulation testing: a case series.

OBJECTIVES: Prevention of pre-analytical issues in coagulation testing is of paramount importance for good laboratory performance. In addition to common issues like hemolysed, icteric, or lipemic samples, some specific pre-analytical errors of coagulation testing include clotted specimens, improper blood-to-anticoagulant ratio, contamination with other anticoagulants, etc. Prothrombin time (PT) and activated partial thromboplastin time (aPTT) are very commonly affected tests due to pre-analytical variables. The impact these parameters possess on surgical decision-making and various life-saving interventions are substantial therefore we cannot afford laxity and casual mistakes in carrying out these critical investigations at all. CASE PRESENTATION: In this case series, a total of 4 cases of unexpectedly deranged coagulation profiles have been described which were reported incorrectly due to the overall casual approach towards these critical investigations. We have also mentioned how the treating clinician and lab physician retrospectively accessed relevant information in the nick of time to bring back reassurance. CONCLUSIONS: Like every other critical investigation, analytical errors can occur in coagulation parameters due to various avoidable pre-analytical variables. The release of spurious results for coagulation parameters sets alarm bells ringing causing much agony to the treating doctor and patient. Only a disciplined and careful approach taken by hospital and lab staff towards each sample regardless of its criticality can negate these stressful errors to a large extent.

The Role of Indirect Immunofluorescence (IIF) and Line Immunoassay (LIA) in the Diagnosis of Autoimmune Diseases and Their Clinical Correlation: An Observational Study From a Tertiary Care Center in Bihar.

Background and aim The presence of distinct sets of autoantigens and autoantibodies bestow these autoimmune diseases (ADs) with specific immune profiles or fingerprints, which has cleared the diagnostic dilemma associated with these ADs. This study was planned to collate and compare the reporting of indirect immunofluorescence (IIF) with line immunoassay (LIA) and their clinical correlations. This study was conducted to investigate the association between the reporting of anti-nuclear antibody (ANA) screening by IIF and ANA profile reporting by LIA. Additionally, it aimed to explore the association of ANA pattern detection by IIF with the detection of autoantibodies against nuclear antigens by LIA and the association of autoantibody detection by LIA with clinical diagnosis. Methodology A total of 98 samples from patients suspected of having ADs were subjected to both IIF and LIA, and results were correlated with clinical diagnosis. Results In the homogenous pattern noted by IIF, the clustered antigens identified by LIA included dsDNA, Nucleosome, Histone, and Mi-2. In the speckled pattern, the identified antigens were SS-A/Ro52, P0, SS-A/Ro60, SS-B/La, and U1-snRNP. On the other hand, the nucleolar pattern revealed antigens AMA M2, PCNA, and CENP-B. The centromere pattern was mostly associated with CENP-B. The speckled pattern was found to be most commonly associated with systemic lupus erythematosus (SLE). The most common autoantibody found in total ANA profile-positive samples was anti-U1-snRNP followed by anti-SS-A/Ro60 and anti-SS-B/La, and all three were found to be associated with SLE. Conclusions SLE was the most common AD identified in our study samples, with the speckled pattern being the most common pattern on IIF and anti-U1-snRNP being the most common ANA identified by LIA. The fluorescence pattern of IIF predicts the presence of specific antibodies. LIA should be reserved for IIF-positive but dubious cases and whose signs and symptoms are nebulous and do not match the disease dictated by IIF.

Spin-Direction-Spin Coupling of Quasiguided Modes in Plasmonic Crystals.

We report an unusual spin-direction-spin coupling phenomenon of light using the leaky quasiguided modes of a waveguided plasmonic crystal. This is demonstrated as simultaneous input spin-dependent directional guiding of waves (spin-direction coupling) and wave-vector-dependent spin acquisition (direction-spin coupling) of the scattered light. These effects, manifested as the forward and the inverse spin Hall effect of light in the far field, and other accompanying spin-orbit interaction effects are observed and analyzed using a momentum (k) domain polarization Mueller matrix. Resonance-enabled enhancement of these effects is also demonstrated by utilizing the spectral Fano resonance of the hybridized modes. The fundamental origin and the unconventional manifestation of the spin-direction-spin coupling phenomenon from a relatively simple system, ability to probe and interpret the resulting spin-orbit phenomena in the far field through momentum-domain polarization analysis, and their regulated control in plasmonic-photonic crystals open up exciting avenues in spin-orbit-photonic research.

Enhanced remediation of polyaromatic hydrocarbon using agro-industrial waste for biofuel production and environmental pollution mitigation.

In the present study, attention has been paid to the development of economically feasible strategies for enhanced remediation of anthracene and its conversion into biofuels. The strategies developed (B1, B2, B3, and B4) include bagasse and lipid-producing strain Rhodotorula mucilagenosa IIPL32 synthesizing surface active metabolites. The results indicate the highest production of surface-active metabolites in strategies B2, B3, and B4 along with a maximum biodegradation rate. GC-MS analysis affirmed the conversion of anthracene into phthalic acid in all the strategies. Biofuel quality of the lipid produced by the strain showed higher cetane number and improved cold flow property indicating the efficiency of the developed strategies for the production of commercial grade biodiesel. Furthermore, the phytotoxicity study of the spent wash revealed that 50% and 75% diluted spent wash were non-toxic and can be employed for ferti-irrigation. Thus, the study signifies the development of an economically feasible process that can be commercially employed in biofuel industries.

Nondegenerate Integer Quantum Hall Effect from Topological Surface States in Ag2Te Nanoplates.

The quantum Hall effect is one of the exclusive properties displayed by Dirac Fermions in topological insulators, which propagates along the chiral edge state and gives rise to quantized electron transport. However, the quantum Hall effect formed by the nondegenerate Dirac surface states has been elusive so far. Here, we demonstrate the nondegenerate integer quantum Hall effect from the topological surface states in three-dimensional (3D) topological insulator ß-Ag2Te nanostructures. Surface-state dominant conductance renders quantum Hall conductance plateaus with a step of e2/h, along with typical thermopower behaviors of two-dimensional (2D) massless Dirac electrons. The 2D nature of the topological surface states is proven by the electrical and thermal transport responses under tilted magnetic fields. Moreover, the degeneracy of the surface states is removed by structure inversion asymmetry (SIA). The evidenced SIA-induced nondegenerate integer quantum Hall effect in low-symmetry ß-Ag2Te has implications for both fundamental study and the realization of topological magneto-electric effects.

The Drosophila Nab2 RNA binding protein inhibits m6A methylation and male-specific splicing of Sex lethal transcript in female neuronal tissue.

The Drosophila polyadenosine RNA binding protein Nab2, which is orthologous to a human protein lost in a form of inherited intellectual disability, controls adult locomotion, axon projection, dendritic arborization, and memory through a largely undefined set of target RNAs. Here, we show a specific role for Nab2 in regulating splicing of ~150 exons/introns in the head transcriptome and focus on retention of a male-specific exon in the sex determination factor Sex-lethal (Sxl) that is enriched in female neurons. Previous studies have revealed that this splicing event is regulated in females by N6-methyladenosine (m6A) modification by the Mettl3 complex. At a molecular level, Nab2 associates with Sxl pre-mRNA in neurons and limits Sxl m6A methylation at specific sites. In parallel, reducing expression of the Mettl3, Mettl3 complex components, or the m6A reader Ythdc1 rescues mutant phenotypes in Nab2 flies. Overall, these data identify Nab2 as an inhibitor of m6A methylation and imply significant overlap between Nab2 and Mettl3 regulated RNAs in neuronal tissue.

A tropical geometric approach to exceptional points.

Non-Hermitian systems have been widely explored in platforms ranging from photonics to electric circuits. A defining feature of non-Hermitian systems is exceptional points (EPs), where both eigenvalues and eigenvectors coalesce. Tropical geometry is an emerging field of mathematics at the interface between algebraic geometry and polyhedral geometry, with diverse applications to science. Here, we introduce and develop a unified tropical geometric framework to characterize different facets of non-Hermitian systems. We illustrate the versatility of our approach using several examples and demonstrate that it can be used to select from a spectrum of higher-order EPs in gain and loss models, predict the skin effect in the non-Hermitian Su-Schrieffer-Heeger model, and extract universal properties in the presence of disorder in the Hatano-Nelson model. Our work puts forth a framework for studying non-Hermitian physics and unveils a connection of tropical geometry to this field.

Drug discovery through Covid-19 genome sequencing with siamese graph convolutional neural network.

After several waves of COVID-19 led to a massive loss of human life worldwide due to the changes in its variants and the vast explosion. Several researchers proposed neural network-based drug discovery techniques to fight against the pandemic; utilizing neural networks has limitations (Exponential time complexity, Non-Convergence, Mode Collapse, and Diminished Gradient). To overcome those difficulties, this paper proposed a hybrid architecture that will help to repurpose the most appropriate medicines for the treatment of COVID-19. A brief investigation of the sequences has been made to discover the gene density and noncoding proportion through the next gene sequencing. The paper tracks the exceptional locales in the virus DNA sequence as a Drug Target Region (DTR). Then the variable DNA neighborhood search is applied to this DTR to obtain the DNA interaction network to show how the genes are correlated. A drug database has been obtained based on the ontological property of the genomes with advanced D3Similarity so that all the chemical components of the drug database have been identified. Other methods obtained hydroxychloroquine as an effective drug which was rejected by WHO. However, The experimental results show that Remdesivir and Dexamethasone are the most effective drugs, with 97.41 and 97.93%, respectively.

A New Language-Independent Deep CNN for Scene Text Detection and Style Transfer in Social Media Images.

Due to the adverse effect of quality caused by different social media and arbitrary languages in natural scenes, detecting text from social media images and transferring its style is challenging. This paper presents a novel end-to-end model for text detection and text style transfer in social media images. The key notion of the proposed work is to find dominant information, such as fine details in the degraded images (social media images), and then restore the structure of character information. Therefore, we first introduce a novel idea of extracting gradients from the frequency domain of the input image to reduce the adverse effect of different social media, which outputs text candidate points. The text candidates are further connected into components and used for text detection via a UNet++ like network with an EfficientNet backbone (EffiUNet++). Then, to deal with the style transfer issue, we devise a generative model, which comprises a target encoder and style parameter networks (TESP-Net) to generate the target characters by leveraging the recognition results from the first stage. Specifically, a series of residual mapping and a position attention module are devised to improve the shape and structure of generated characters. The whole model is trained end-to-end so as to optimize the performance. Experiments on our social media dataset, benchmark datasets of natural scene text detection and text style transfer show that the proposed model outperforms the existing text detection and style transfer methods in multilingual and cross-language scenario.

PPIGCF: A Protein-Protein Interaction-Based Gene Correlation Filter for Optimal Gene Selection.

Biological data at the omics level are highly complex, requiring powerful computational approaches to identifying significant intrinsic characteristics to further search for informative markers involved in the studied phenotype. In this paper, we propose a novel dimension reduction technique, protein-protein interaction-based gene correlation filtration (PPIGCF), which builds on gene ontology (GO) and protein-protein interaction (PPI) structures to analyze microarray gene expression data. PPIGCF first extracts the gene symbols with their expression from the experimental dataset, and then, classifies them based on GO biological process (BP) and cellular component (CC) annotations. Every classification group inherits all the information on its CCs, corresponding to the BPs, to establish a PPI network. Then, the gene correlation filter (regarding gene rank and the proposed correlation coefficient) is computed on every network and eradicates a few weakly correlated genes connected with their corresponding networks. PPIGCF finds the information content (IC) of the other genes related to the PPI network and takes only the genes with the highest IC values. The satisfactory results of PPIGCF are used to prioritize significant genes. We performed a comparison with current methods to demonstrate our technique's efficiency. From the experiment, it can be concluded that PPIGCF needs fewer genes to reach reasonable accuracy (~99%) for cancer classification. This paper reduces the computational complexity and enhances the time complexity of biomarker discovery from datasets.

Non-Hermitian topological phases: principles and prospects.

The synergy between non-Hermitian concepts and topological ideas have led to very fruitful activity in the recent years. Their interplay has resulted in a wide variety of new non-Hermitian topological phenomena being discovered. In this review, we present the key principles underpinning the topological features of non-Hermitian phases. Using paradigmatic models-Hatano-Nelson, non-Hermitian Su-Schrieffer-Heeger and non-Hermitian Chern insulator-we illustrate the central features of non-Hermitian topological systems, including exceptional points, complex energy gaps and non-Hermitian symmetry classification. We discuss the non-Hermitian skin effect and the notion of the generalized Brillouin zone, which allows restoring the bulk-boundary correspondence. Using concrete examples, we examine the role of disorder, describe the Floquet engineering, present the linear response framework, and analyze the Hall transport properties of non-Hermitian topological systems. We also survey the rapidly growing experimental advances in this field. Finally, we end by highlighting possible directions which, in our view, may be promising for explorations in the near future.

The second wave of COVID-19 wreaked havoc: A look at clinical and laboratory parameters of survivors and non-survivors admitted to Intensive Care Unit, a single-centered retrospective study.

Background: The second wave of COVID-19 was disastrous and claimed many lives in India and abroad. The most challenging task was to provide the required treatment as per the patient's condition, within a limited span of time. The lack of prognostic predictors at the time of admission led to failure in prioritizing the patient's need for intensive care. Aim: This study was conducted to find out the clinical and laboratory parameters at the time of admission to ICU as predictors of outcomes in COVID-19 patients, which can help in judicious utilization of the available resources for better patient care. Subjects and Methods: Study comprises of 161 ICU admitted patients. Study of clinical traits, comorbidities, test results, and demographic variables were carried out among survivors and non-survivor. Result: Maximum death were patients of age group 21-30 years and male gender. Mortality in hypertensives, diabetics, and patients with sepsis were found to be statistically significant. Patients who developed ARDS and pneumonia or needed ventilation died invariably. High levels of laboratory parameters like IL-6, LDH, PT, INR, aPTT, ferritin, WBC count, and D-dimer were significantly associated with poor outcomes and at a particular cutoff had optimum sensitivity and specificity to predict mortality in ICU admitted COVID-19 patients. At the same time, low lymphocyte count and PaO2/FiO2 ratio was significantly associated with bad prognosis (P < 0.05). Conclusion: This paper will help in prioritizing patients in ICU who need special attention especially at the time of meager supply of resources.

An insight into omics analysis and metabolic pathway engineering of lignin-degrading enzymes for enhanced lignin valorization.

Lignin, a highly heterogeneous polymer of lignocellulosic biomass, is intricately associated with cellulose and hemicellulose, responsible for its strength and rigidity. Lignin decomposition is carried out through certain enzymes derived from microorganisms to promote the hydrolysis of lignin. Analyzing multi-omics data helps to emphasize the probable value of fungal-produced enzymes to degrade the lignocellulosic material, which provides them an advantage in their ecological niches. This review focuses on lignin biodegrading microorganisms and associated ligninolytic enzymes, including lignin peroxidase, manganese peroxidase, versatile peroxidase, laccase, and dye-decolorizing peroxidase. Further, enzymatic catalysis, lignin biodegradation mechanisms, vital factors responsible for lignin modification and degradation, and the design and selection of practical metabolic pathways are also discussed. Highlights were made on metabolic pathway engineering, different aspects of omics analyses, and its scope and applications to ligninase enzymes. Finally, the advantages and essential steps of successfully applying metabolic engineering and its path forward have been addressed.

Variation in biochemical parameters in COVID-19 patients admitted at a tertiary care dedicated COVID hospital: A prospective study.

Introduction: COVID-19 infection has a myriad of presentation. Rural India and other developing nations are relatively resource poor, not having access to modern specialized investigations. In this study, we tried to evaluate only biochemical parameters in predicting the severity of the infection. The aim of this study was to find a cost-effective means to predict the clinical course at the time of admission and thereby to reduce mortality and, if possible, morbidity by timely intervention. Materials and Methods: All COVID-19-positive cases admitted at our hospital from March 21 to December 31, 2020, were recruited in this study. The same acted as sham control at recovery. Results: We observed a significant difference in biochemical parameters at the time of admission and discharge, between mild/moderate disease and severe disease. We found slightly deranged liver function tests at admission, which becomes normal at the time of discharge. Urea, C-reactive protein (CRP), procalcitonin, lactate dehydrogenase, and ferritin concentrations in severe/critical patients were significantly higher than that in the mild/moderate group. Receiver operating characteristic curves were plotted to predict the severity on the basis of biochemical parameters independently, of the patients based on these values. Conclusion: We proposed cutoff values of certain biochemical parameters, which will help in judging the severity of the infection at admission. We developed a predictive model with a significant predictive capability for CRP and ferritin values, using normal available biochemical parameters, routinely done in resource-poor centers. Clinicians working in resource-poor situations will be benefitted by having an idea of the severity of the disease. Timely intervention will reduce mortality and severe morbidity.

Résumé Introduction: L'infection au COVID19 a une myriade de présentations. L'Inde rurale et d'autres pays en développement sont relativement pauvres en ressources, non avoir accès aux enquêtes spécialisées modernes. Dans cette étude, nous avons essayé d'évaluer uniquement les paramètres biochimiques pour prédire la gravité de l'infection. Le but de cette étude était de trouver un moyen rentable de prédire l'évolution clinique au moment de l'admission et ainsi de réduire la mortalité et, si possible, la morbidité par une intervention rapide. Matériels et méthodes: Tous les cas positifs au COVID19 admis à notre hospitalisés du 21 mars au 31 décembre 2020, ont été recrutés dans cette étude. La même chose a agi comme un contrôle factice lors de la récupération. Résultats: Nous avons observé une différence significative dans les paramètres biochimiques au moment de l'admission et de la sortie, entre une maladie légère/modérée et une maladie grave. Nous avons trouvé des tests de la fonction hépatique légèrement dérangés à l'admission, qui deviennent normaux au moment de la sortie. Urée, protéine Créactive (CRP, les concentrations de procalcitonine, de lactate déshydrogénase et de ferritine chez les patients sévères/critiques étaient significativement plus élevées que chez les patients légers/modérés groupe. Les courbes caractéristiques de fonctionnement du récepteur ont été tracées pour prédire la gravité sur la base de paramètres biochimiques indépendamment, deles patients en fonction de ces valeurs. Conclusion: Nous avons proposé des valeurs seuils de certains paramètres biochimiques, qui permettront de juger de la gravité de l'infection à l'admission. Nous avons développé un modèle prédictif avec une capacité prédictive significative pour les valeurs de CRP et de ferritine, en utilisant les paramètres biochimiques normaux disponibles, systématiquement effectués dans les centres pauvres en ressources. Les cliniciens travaillant dans des situations où les ressources sont limitées bénéficier d'avoir une idée de la gravité de la maladie. Une intervention rapide réduira la mortalité et la morbidité grave. Mots-clés: COVID19, ferritine, lactate déshydrogénase, urée.