Clinical and histopathological spectrum of cranial small round cell tumors: An experience from a tertiary care center.

BACKGROUND: Small round cell tumors (SRCTs) are a group of malignant neoplasms with minimal or no differentiation, characterized by the presence of round cells with high nuclear-cytoplasmic ratio. Although SRCTs can occur in any part of the body, involvement of central nervous system (CNS) is uncommon. AIM: We aimed to study the clinicopathological spectrum of cranial SRCT diagnosed in our institute over a period of four years (2016-2019). MATERIAL AND METHODS: A retrospective review of medical records (2016-2019) with a morphological diagnosis of cranial SRCT was made. Both intra-axial and extra-axial tumors were included. A total of 60 cases were retrieved, and the clinical and histopathological features were studied. Special cytochemical staining and immunohistochemistry were performed, where needed. RESULTS: The mean age at presentation was 18.4 years (range, 1-60 years), with a male-to-female ratio of 2.5:1. The most common site was posterior fossa of brain (n = 28, 47%), followed by dorso-lumbar spine (n = 9, 15%). The most common type of tumor was medulloblastoma (n = 29, 48.3%), followed by Ewing sarcoma (ES)/peripheral primitive neuroectodermal tumor (pPNET) (n = 11, 18.3%), non-Hodgkin lymphoma (NHL) (n = 9, 15%), neuroblastoma (n = 3, 5%), and CNS embryonal tumor, NOS (n = 2, 3.3%). One case each of atypical teratoid rhabdoid tumor (ATRT), rhabdomyosarcoma, pineoblastoma, melanoma, rhabdomyosarcoma, and undifferentiated pleomorphic sarcoma was also documented. CONCLUSIONS: SRCTs have a variable age of presentation. Their incidence in CNS is low as compared to other organ systems. On light microscopy, the histopathology of these lesions is overlapping, posing a great diagnostic dilemma for the pathologist. The use of ancillary techniques like immunohistochemistry helps in arriving at the correct diagnosis. Treatment strategy and tumor prognosis also vary along the entire spectrum of SRCT, thus making exact characterization essential for proper management.

Effectiveness of herbal drug Terminalia Arjuna in chronic venous insufficiency - A prospective observational study.

INTRODUCTION: Chronic venous insufficiency (CVI) manifests in various clinical presentations ranging from asymptomatic but cosmetic problems to severe symptoms, such as lower limb edema, skin trophic changes, and ulceration. CVI substantially affects the quality of life and work productivity of the patients. Ayurveda, an ancient traditional medicine in India, evaluates the various pathological stages of CVI with a wide range of pathological conditions such as Siragranthi (venous abnormalities), Raktavaritavata (disorders of vata occluded by rakta ∼ blood), ApanaVaigunya (vitiated apanavayu), Arsha (hemorrhoids), VataRakta (rheumatism due to rakta), Kushtha (integumentary disease) and Dushta Vrana (putrefied wound) depending upon the presentations of the patient. Ayurvedic texts mention Terminalia arjuna as a potential herb for treating various conditions related to the circulatory system. The drug is an effective anti-inflammatory, anti-oxidant, and anti-hypertensive and has a definite role in improving cardiovascular hemodynamics and wound healing. These attributes suggest that the potential of Terminalia arjuna needs to be explored as a promising venoactive drug. METHODS: This prospective observational study included 25 patients (31 limbs) with CVI who were treated with Tab Terminalia arjuna (Bark extract of Terminalia arjuna in a dose of 500 mg, given twice a day) and were observed on two visits on day 30 and day 90. Follow-up was carried out for three months to evaluate post-treatment complications or adverse effects. The clinical outcome assessment was done using Venous Clinical Severity Score (VCSS), and clinical grading was performed using clinical classification (C0 - C6) of CEAP (Clinical-Etiology-Anatomy-Pathophysiology) classification. RESULTS: The median VCSS score (of both limbs) during the third visit was comparatively lower than the first, with a statistically significant improvement at 0.05 level. Further, there was a substantial positive improvement in the clinical classification of CEAP among the patients in pre and post treatment phase. CONCLUSION: The prospective observational study shows that Tab Terminalia arjuna is safe and effective in CVI, reducing the symptoms like pain, edema, inflammation, pigmentation, induration and also expediting ulcer healing.

Next-generation hybrid technologies for the treatment of pharmaceutical industry effluents.

Water and industries are intangible units of the globe that are always set to meet the population's demand. The global population depends on one-third of freshwater increasing the demand. The increase in population along with urbanization has polluted the fresh water resources. The pharmaceutical industry is marked as an emerging contaminant of water pollution. The most common type of pharmaceutical drugs that are detected in the environment includes antibiotics, analgesics, NSAIDs, and pain-relieving drugs. These drugs alter the food chain of the organisms causing chaos mainly in the marine ecosystem. Pharmaceutical drugs are found only in shallow amounts (ng/mg) they have a huge impact on the living system. The consumption of water contaminated with pharmaceutical ingredients can disrupt reproduction, hormonal imbalance, cancer, and respiratory problems. Various methods are used to remove these chemicals from the environment. In this review, we mainly focused on the emerging hybrid technologies and their significance in the effective treatment of pharmaceutical wastewater. This review paper primarily elaborates on the merits and demerits of existing conventional technologies helpful in developing integrated technologies for the modern era of pharmaceutical effluent treatment. This review paper further in detail discusses the various strategies of eco-friendly bioremediation techniques namely biostimulation, bioaugmentation, bacterial degradation, mycoremediation, phytoremediation, and others for the ultimate removal of pharmaceutical contaminants in wastewater. The review makes clear that targeted and hybrid solutions are what the world will require in the future to get rid of these pharmacological prints.

Equilibrium Optimization Algorithm with Deep Learning Enabled Prostate Cancer Detection on MRI Images.

The enlargement of the prostate gland in the reproductive system of males is considered a form of prostate cancer (PrC). The survival rate is considerably improved with earlier diagnosis of cancer; thus, timely intervention should be administered. In this study, a new automatic approach combining several deep learning (DL) techniques was introduced to detect PrC from MRI and ultrasound (US) images. Furthermore, the presented method describes why a certain decision was made given the input MRI or US images. Many pretrained custom-developed layers were added to the pretrained model and employed in the dataset. The study presents an Equilibrium Optimization Algorithm with Deep Learning-based Prostate Cancer Detection and Classification (EOADL-PCDC) technique on MRIs. The main goal of the EOADL-PCDC method lies in the detection and classification of PrC. To achieve this, the EOADL-PCDC technique applies image preprocessing to improve the image quality. In addition, the EOADL-PCDC technique follows the CapsNet (capsule network) model for the feature extraction model. The EOA is based on hyperparameter tuning used to increase the efficiency of CapsNet. The EOADL-PCDC algorithm makes use of the stacked bidirectional long short-term memory (SBiLSTM) model for prostate cancer classification. A comprehensive set of simulations of the EOADL-PCDC algorithm was tested on the benchmark MRI dataset. The experimental outcome revealed the superior performance of the EOADL-PCDC approach over existing methods in terms of different metrics.

Bioinspired Garra Rufa Optimization-Assisted Deep Learning Model for Object Classification on Pedestrian Walkways.

Object detection in pedestrian walkways is a crucial area of research that is widely used to improve the safety of pedestrians. It is not only challenging but also a tedious process to manually examine the labeling of abnormal actions, owing to its broad applications in video surveillance systems and the larger number of videos captured. Thus, an automatic surveillance system that identifies the anomalies has become indispensable for computer vision (CV) researcher workers. The recent advancements in deep learning (DL) algorithms have attracted wide attention for CV processes such as object detection and object classification based on supervised learning that requires labels. The current research study designs the bioinspired Garra rufa optimization-assisted deep learning model for object classification (BGRODL-OC) technique on pedestrian walkways. The objective of the BGRODL-OC technique is to recognize the presence of pedestrians and objects in the surveillance video. To achieve this goal, the BGRODL-OC technique primarily applies the GhostNet feature extractors to produce a set of feature vectors. In addition to this, the BGRODL-OC technique makes use of the GRO algorithm for hyperparameter tuning process. Finally, the object classification is performed via the attention-based long short-term memory (ALSTM) network. A wide range of experimental analysis was conducted to validate the superior performance of the BGRODL-OC technique. The experimental values established the superior performance of the BGRODL-OC algorithm over other existing approaches.

Human IL-17A protein production is controlled through a PIP5K1α-dependent translational checkpoint.

The cytokine interleukin-17 (IL-17) is secreted by T helper 17 (TH17) cells and is beneficial for microbial control; however, it also causes inflammation and pathological tissue remodeling in autoimmunity. Hence, TH17 cell differentiation and IL-17 production must be tightly regulated, but, to date, this has been defined only in terms of transcriptional control. Phosphatidylinositols are second messengers produced during T cell activation that transduce signals from the T cell receptor (TCR) and costimulatory receptors at the plasma membrane. Here, we found that phosphatidylinositol 4,5-bisphosphate (PIP2) was enriched in the nuclei of human TH17 cells, which depended on the kinase PIP5K1α, and that inhibition of PIP5K1α impaired IL-17A production. In contrast, nuclear PIP2 enrichment was not observed in TH1 or TH2 cells, and these cells did not require PIP5K1α for cytokine production. In T cells from people with multiple sclerosis, IL-17 production elicited by myelin basic protein was blocked by PIP5K1α inhibition. IL-17 protein was affected without altering either the abundance or stability of IL17A mRNA in TH17 cells. Instead, analysis of PIP5K1α-associating proteins revealed that PIP5K1α interacted with ARS2, a nuclear cap-binding complex scaffold protein, to facilitate its binding to IL17A mRNA and subsequent IL-17A protein production. These findings highlight a transcription-independent, translation-dependent mechanism for regulating IL-17A protein production that might be relevant to other cytokines.

Linear and nonlinear characterization of a broadband integrated Si-rich silicon nitride racetrack ring resonator for on-chip applications.

We demonstrate the linear and nonlinear characterization of a plasma-enhanced chemical vapor deposited silicon-rich silicon nitride (SRSN) racetrack ring resonator for on-chip applications within the telecommunication wavelength range. The SRSN waveguide parameters are optimized by employing the refractive index profile measured by ellipsometry to achieve flat dispersion in the telecom band. Furthermore, we measure the thermo-optic coefficient of the micro-resonator by analyzing the temperature-dependent transmission spectra and assess it to be 3.2825×10-5 ∘ C -1. Additionally, we study power-dependent transmission spectra to investigate the effect of local heating and nonlinear absorption. The power-dependent transmission spectra exhibit a blueshifting of the resonance peak in the visible and near-IR regions, which indicates the presence of nonlinear losses in that range. The power-dependent transmission spectra almost remain unchanged in the telecom band, revealing the absence of nonlinear losses and excellent thermal stability in that wavelength range. Our experimental results reveal that the SRSN-based structure can be employed potentially to realize linear and nonlinear applications in the telecom band.

Diagnostic algorithm for pathological evaluation of gliomas in a resource-constrained setting.

Introduction: Gliomas are the most common primary intracranial tumors. The current World Health Organization (WHO) classification of central nervous system tumors recommends integrated histo-molecular diagnosis of gliomas. However, molecular testing is not available in even most of the advanced centers of our country, and histopathology aided with immunohistochemistry (IHC) is still widely used for diagnosis. Immunohistochemical markers such as iso-citrate dehydrogenase1 (IDH1) and Alpha Thalassemia/Mental Retardation Syndrome X-linked (ATRX) can be reliably used for the correct diagnosis, prognosis, and treatment of gliomas. Aim: We aimed to develop a diagnostic algorithm by integrating morphology, IDH1, and ATRX status of gliomas seen in our institute for 1 year. Settings and Design: Analytical cross-sectional study. Materials and Methods: This study included 60 histopathologically confirmed cases of astrocytic (n = 51) and oligodendroglial tumors (n = 9). Clinical, radiological, and histopathological features were noted and tumor grades assigned according to the WHO recommendations. IDH1 and ATRX mutation status was evaluated using IHC. The tumors were divided into three molecular groups on the basis of their IDH1 and ATRX mutation status: (1) Group 1: IDH1 negative and ATRX positive, (2) Group 2: IDH1 positive and ATRX positive, (3) Group 3: IDH1 positive and ATRX negative. Results: The mean age of presentation was 45.0 ± 15.8 years with a male-to-female ratio of 2:1. Seizures, headache, and hemiparesis were the most common modes of presentation. The tumor subtypes studied were glioblastoma (n = 32), anaplastic astrocytoma (n = 7), diffuse astrocytoma (n = 6), oligodendroglioma (n = 6), pilocytic astrocytoma (n = 6), and anaplastic oligodendroglioma (n = 3). IDH1 mutation was present in 26 cases including anaplastic astrocytoma (n = 7), diffuse astrocytoma (n = 6), oligodendroglioma (n = 5), secondary glioblastoma (n = 5), and anaplastic oligodendroglioma (n = 3). ATRX mutation, i. e., loss of ATRX was observed in 17 cases including diffuse astrocytoma (n = 5), anaplastic astocytoma (n = 5), anaplastic oligodendroglioma (n = 3), oligodendroglioma (n = 3), and secondary glioblastoma (n = 1). All six cases of pilocytic astrocytoma were negative for IDH1 and ATRX mutation. There were 34 patients in Group 1 (IDH1- and ATRX +), nine cases in Group 2 (IDH1 + and ATRX +), and 17 patients in Group 3 (IDH1 + and ATRX-). Conclusion: Diagnosis of gliomas should be based on a detailed clinicoradiological and histopathological assessment, followed by genotypic characterization. Evaluation for IDH1and ATRX status has both diagnostic and prognostic value as it helps in differentiating gliomas from reactive gliosis, primary glioblastoma from secondary glioblastoma, and pilocytic astrocytoma (WHO grade I) from diffuse astrocytoma (WHO grade II). Tumors with IDH1 mutations have a better outcome than those with wild-type IDH. IHC can serve as a useful surrogate to conventional molecular tests in resource-constrained settings. By devising an algorithm based on morphological and IHC features, we were able to stratify gliomas into three prognostic subgroups.

Ayurvedic management of neurological deficits post COVID-19 vaccination - A report of two cases.

The world witnessed much research fund allocation on the COVID-19 outbreak's epidemiology, pathology, impact on lifestyles, social behaviours and treatment possibilities. The highly contagious nature of the disease compelled scientific communities and related organisations to hasten vaccine development and supplies. Well-timed international collaborations resulted in quicker development of varied forms of vaccines against COVID-19. Prospective observational studies and systematic reviews on vaccine trials reported their safety and efficacies. Nevertheless, post-marketing surveillance is quintessential to ascertain such safety and efficacy claims. There have been scattered reports lately of several adverse temporal events, such as haematological, immunological and neurological untoward occurrences following COVID-19 inoculation. There is a growing piece of evidence of the impact of COVID vaccination on patients with neurological-neuroimmunological disorders. Here two unrelated cases of neurological deficits post-COVID vaccination are reported. One was an incidence of Acute Disseminated Encephalomyelitis, while the other was an acute exacerbation of Multiple Sclerosis following vaccination. Ayurvedic treatments were effective in either of these conditions. Case series and case reports shall judiciously add information to vaccine safety data and acknowledge the necessity of clinician approval, based on detailed individualised assessments before mass vaccination.

Safety evaluation of horizontal curves on two lane rural highways using machine learning algorithms: A priority-based study for sight distance improvements.

OBJECTIVE: Current Road geometric design processes disregard stochastic aspects, resulting in inadequate traffic safety considerations. In addition, the primary sources of crash data are obtained from police department, insurance agency and hospitals, where detailed investigation is not carried out from a transportation perspective. So, the data obtained from these sources may or may not be reliable. The main objective of this study is to account uncertainties using reliability as a tool that considers slow down the vehicle while they maneuver the curve and to develop thresholds of reliability index associated with sight distance based on operating speed design consistent measure as a surrogate safety measure rather than using crash data. METHODOLOGY: This study proposes a thresholds of reliability index associated with sight distance for various operating speed ranges based on design consistent measure. In addition, established the relationship between the consistency levels, geometric characteristics and vehicle characteristics. In this study, classical topography survey was carried out on the field using total station. The data collected comprises speed data and geometric data of 18 horizontal curves (Lane based analysis was carried out). A total of 3042 free flowing vehicle speeds have been extracted from the video graphic survey and used in the analysis. RESULTS: The threshold values of reliability indices associated with sight distance is higher as the operating speed increases for the section to be a consistent design section. The results from the Binary Logit Model show that the consistency level is significantly affected by deflection angle and operating speed. Deflection angle was negatively correlated with the in-consistency level, and the operating speed was positively correlated with in-consistency level. CONCLUSIONS: From Binary Logit Model (BLM) results, we can conclude that an increase in the value of deflection angle will significantly decrease the probability of In-Consistent level, which indicates uncertainties that cause drivers to change the path of vehicle or rate of deceleration of the vehicle while maneuvering the curve will be decreased. Whereas, increase in the operating speed will significantly increase the probability of In-Consistency level.

Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model.

COVID-19 was first identified in December 2019 at Wuhan, China. At present, the outbreak of COVID-19 pandemic has resulted in severe consequences on both economic and social infrastructures of the developed and developing countries. Several studies have been conducted and ongoing still to design efficient models for diagnosis and treatment of COVID-19 patients. The traditional diagnostic models that use reverse transcription-polymerase chain reaction (rt-qPCR) is a costly and time-consuming process. So, automated COVID-19 diagnosis using Deep Learning (DL) models becomes essential. The primary intention of this study is to design an effective model for diagnosis and classification of COVID-19. This research work introduces an automated COVID-19 diagnosis process using Convolutional Neural Network (CNN) with a fusion-based feature extraction model, called FM-CNN. FM-CNN model has three major phases namely, pre-processing, feature extraction, and classification. Initially, Wiener Filtering (WF)-based preprocessing is employed to discard the noise that exists in input chest X-Ray (CXR) images. Then, the pre-processed images undergo fusion-based feature extraction model which is a combination of Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRM), and Local Binary Patterns (LBP). In order to determine the optimal subset of features, Particle Swarm Optimization (PSO) algorithm is employed. At last, CNN is deployed as a classifier to identify the existence of binary and multiple classes of CXR images. In order to validate the proficiency of the proposed FM-CNN model in terms of its diagnostic performance, extension experimentation was carried out upon CXR dataset. As per the results attained from simulation, FM-CNN model classified multiple classes with the maximum sensitivity of 97.22%, specificity of 98.29%, accuracy of 98.06%, and F-measure of 97.93%.

Isolation, Production, and Characterization of Serum Immunoglobulin M (IgM) of Indian Major Carp Cirrhinus mrigal (MRIGAL).

A method for the isolation of immunoglobulin M (IgM) in Indian major carp Cirrhinus mrigal (mrigal) serum to produce polyclonal antibodies is described in the present study. The purified immunoglobulins (IgM) were isolated from the serum of mrigal (Cirrhinus mrigal) by the bovine serum albumin (BSA)-CL affinity column purification method, and the IgM was used to produce a polyclonal rabbit anti-mrigal IgM antiserum. The IgM preparations were employed in the characterization of mrigal serum immunoglobulin. Reduced mrigal IgM on sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) was shown to consist of two subunits, compatible with heavy and light chains. A single heavy chain at approximately 90 kDa and variant of light chain 30 kDa were found. The dominant form of nonreduced IgM had a MW of approximately 900 kDa, suggesting a tetrameric structure based on estimated molecular weights, the relative protein content, and the reactivity with anti-mrigal IgM antisera, was obtained. The antisera were characterized as to specificity and reactivity by means of the enzyme linked immuno-sorbent assay (ELISA) and western blotting method. The information on the structure and character of immunoglobulin of fishes is essential in health management. The study described here investigates the possibility of using the serological techniques to assess the reactivity of antibody with the anti-mrigal IgM antisera.

Evaluation of feed utilization, immune response and disease resistance in striped catfish, Pangasianodon hypophthalmus (Sauvage 1878) fed with a novel Aeromonas hydrophila biofilm vaccine.

Striped catfish, Pangasianodon hypophthalmus was immunized with Biofilm (BF) and Free cell (FC) of Aeromonas hydrophila vaccine at 1010 CFU g-1 for 20 days and monitored for growth parameters, immune responses and disease resistance up to 60 day post vaccination (dpv). Pangasius catfish in the BF vaccinated group had considerably higher growth and feed utilization than the FC vaccinated and unvaccinated groups (p < 0.05). Biofilm vaccinated group showed a significant increase (p < 0.05) in the mean weight gain (46.91 ± 0.59) than the FC (35.94 ± 0.21) and unvaccinated group (34.92 ± 0.35). The vaccinated fishes were challenged with A. hydrophila at 107 CFU/ml. Significant higher relative percentage survival (RPS) was recorded with BF (84.21 ± 1.49%) compared to that with FC (33.33 ± 1.21%). Polyclonal antibody-based ELISA was used to quantify the antibody titre. BF vaccinated group showed significantly higher antibody titer compared to other treatments (p < 0.05). Moreover, higher haematological parameters recorded in the present study were differentially stimulated by the oral administration of A. hydrophila biofilm vaccine. The mean total protein, albumin, and globulin levels of the BF vaccine groups were significantly higher (p < 0.05) than the mean total protein, albumin, and globulin contents of the unvaccinated group. Furthermore, biochemical stress parameters (SGPT, SGOT) in the vaccinated groups showed an incremental trend in the early days of the experimental period. However, the values were significantly lower (p < 0.05) in the biofilm group on 20 dpv onwards indicating improved health condition. Vaccinated BF fishes showed gut associated lymphoid tissues (GALT) within the laminar propria of mid gut. But in FC group fishes showed less aggregation of lymphoid cells. The unvaccinated control fish had no lymphoid cell aggregation in their intestines. The findings of the current research suggested that biofilm vaccine has the capability to be one of the potential oral vaccines in striped catfish against A. hydrophila infection.

Nonlinear spectral broadening of a dual-carrier electro-optic frequency comb in a graphene oxide cladded silicon-rich nitride hybrid waveguide.

We demonstrate a detailed theoretical analysis describing the generation of an electro-optic comb (EOC) in the near-IR range through discrete phase and amplitude modulation driven by radio frequency (RF) signal generators. Furthermore, the generated EOC spectra suffer nonlinear spectral broadening while propagating through a hybrid Si-rich nitride (SRN) waveguide structure integrated with two-dimensional (2D) layered graphene oxide (GO) films. We perform a detailed analysis to investigate the influence of GO layers, pump wavelength detuning, and other waveguide parameters on the evolution of comb spectra propagating through the hybrid waveguide structure. Owing to the strong modal overlapping between the SRN waveguides and the highly nonlinear GO films, the nonlinearity of the system is enhanced effectively, and broadband comb spectra have been achieved in the near-IR range. Furthermore, we investigate the spectral coherence of the generated comb spectra under different input conditions. The results exhibit strong potential to generate a tunable frequency comb with high spectral coherence in the near-IR range by employing the hybrid waveguide structure.

Anti-GPVI Fab reveals distinct roles for GPVI signaling in the first platelet layer and subsequent layers during microfluidic clotting on collagen with or without tissue factor.

The collagen receptor glycoprotein VI (GPVI) drives strong platelet activation, however its role at later stages of clotting remains less clear. Controlled timing of addition of anti-human GPVI Fab (clone E12) with microfluidic venous whole blood flow over collagen (± lipidated tissue factor, TF) produced distinct effects on platelets, fibrin, P-selectin exposure, and phosphatidylserine (PS) exposure. On collagen alone, Fab present initially potently reduced platelet deposition on collagen, while Fab added 90 s after initial platelet deposition, stopped subsequent platelet accumulation (despite the absence of fibrin). With thrombin generation via TF, Fab added at either t = 0 or 90 s had no effect on platelet deposition. However, Fab added initially, but not at 90-s, blocked fibrin formation. Gly-Pro-Arg-Pro ablated fibrin formation without effect on platelet accumulation (regardless of Fab added at t = 0 or 90 s), indicating thrombin signaling can suffice over GPVI signaling. Still, Fab moderately reduced P-selectin exposure with thrombin present and fibrin absent. On collagen/TF, Fab present initially ablated PS exposure, but had no effect when added 30 to 90-s later. The thrombin generated via PS exposure had an important role in driving platelet deposition in the presence of Fab, since inhibition of PS via annexin V binding in the presence of Fab significantly inhibited platelet deposition. We conclude GPVI signaling in the first platelet layer on collagen dictates thrombin and fibrin production, but the role of GPVI at subsequent times after formation of the first monolayer is obscured by thrombin-induced signaling.

Chaotic Sparrow Search Algorithm with Deep Transfer Learning Enabled Breast Cancer Classification on Histopathological Images.

Breast cancer is the major cause behind the death of women worldwide and is responsible for several deaths each year. Even though there are several means to identify breast cancer, histopathological diagnosis is now considered the gold standard in the diagnosis of cancer. However, the difficulty of histopathological image and the rapid rise in workload render this process time-consuming, and the outcomes might be subjected to pathologists' subjectivity. Hence, the development of a precise and automatic histopathological image analysis method is essential for the field. Recently, the deep learning method for breast cancer pathological image classification has made significant progress, which has become mainstream in this field. This study introduces a novel chaotic sparrow search algorithm with a deep transfer learning-enabled breast cancer classification (CSSADTL-BCC) model on histopathological images. The presented CSSADTL-BCC model mainly focused on the recognition and classification of breast cancer. To accomplish this, the CSSADTL-BCC model primarily applies the Gaussian filtering (GF) approach to eradicate the occurrence of noise. In addition, a MixNet-based feature extraction model is employed to generate a useful set of feature vectors. Moreover, a stacked gated recurrent unit (SGRU) classification approach is exploited to allot class labels. Furthermore, CSSA is applied to optimally modify the hyperparameters involved in the SGRU model. None of the earlier works have utilized the hyperparameter-tuned SGRU model for breast cancer classification on HIs. The design of the CSSA for optimal hyperparameter tuning of the SGRU model demonstrates the novelty of the work. The performance validation of the CSSADTL-BCC model is tested by a benchmark dataset, and the results reported the superior execution of the CSSADTL-BCC model over recent state-of-the-art approaches.

Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images.

In recent times, COVID-19 infection gets increased exponentially with the existence of a restricted number of rapid testing kits. Several studies have reported the COVID-19 diagnosis model from chest X-ray images. But the diagnosis of COVID-19 patients from chest X-ray images is a tedious process as the bilateral modifications are considered an ill-posed problem. This paper presents a new metaheuristic-based fusion model for COVID-19 diagnosis using chest X-ray images. The proposed model comprises different preprocessing, feature extraction, and classification processes. Initially, the Weiner filtering (WF) technique is used for the preprocessing of images. Then, the fusion-based feature extraction process takes place by the incorporation of gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRM), and local binary patterns (LBP). Afterward, the salp swarm algorithm (SSA) selected the optimal feature subset. Finally, an artificial neural network (ANN) is applied as a classification process to classify infected and healthy patients. The proposed model's performance has been assessed using the Chest X-ray image dataset, and the results are examined under diverse aspects. The obtained results confirmed the presented model's superior performance over the state of art methods.

An Ayurvedic personalized prophylactic protocol in COVID-19.

The current COVID-19 pandemic brought about by the SARS-CoV-2, a novel ß coronavirus is creating intense health havoc globally. Researchers suspect the situation to stay for long in the community, considering this virus's pathogenesis, high rate transmission and tendency to provoke uncontrolled immune response activation. Immune mechanisms are highly individualistic. We put forward a hypothetical model of prakruti (Ayurvedic body phenotyping character) based personalized prophylactic-therapeutic strategies aiming at a better immunomodulation and quicker resolution of host immune mechanisms. We propose this model in symptomatic, mild to moderate, COVID-19 diagnosed cases and in cases quarantined for high to low risk primary contact with a positive case. We also suggest a community level personalized Ayurvedic prophylactic-therapeutic strategy based on the DOTS model. Person-centered body purificatory measures (panchakarma procedures) like therapeutic purgation (virechana) and medicated enema (basti) are suggested in this hypothetical protocol with justification on evidence-based links between immune responses and prakruti along with specific jwara (fevers of varied origin as per Ayurvedic sciences) and COVID-19 symptomatology. The paper also appraises the importance of pitta dosha/ama dosha in the manifestation of inflammation driven destructive phase of immune responses along with its stage-wise intervention. This hypothetical model intends to open up discussions on significance of prakruti assessment as a predictive marker to screen people who are at risk of succumbing into deteriorating states if infected with COVID-19. It also intends to discuss the predictive personalized medicine measures based on prakruti in yielding individual host immune homeostasis which may positively reduce the chances of untoward events of an aggravated immune responsiveness and subsequent inflammation driven tissue destruction - the candidate causes for COVID-19 related casualties. Testing this model may give insight towards emphasizing personalized host immune coping mechanisms that may prove crucial in any infectious outbreaks in near future too.

Study of Relationship Between Iron Deficiency and Thyroid Function in Pregnant Females.

BACKGROUND: Iron is essential for the normal functioning of thyroid peroxidase and iron deficiency is very commonly encountered during pregnancy. Thyroid disorders and iron deficiency are associated with obstetrical and fetal complications. The aim of the study was to find out the relationship between iron deficiency and thyroid function in pregnant females during first trimester. METHODOLOGY:  The present cross-sectional observational study was conducted among first trimester pregnant females at the Department of Medicine, Obstetrics and Gynecology, and Biochemistry at Atal Bihari Vajpayee Institute of Medical Sciences and Dr. Ram Manohar Lohia Hospital, New Delhi. Hundred pregnant women were included in this study. On the basis of serum ferritin value, the study population was divided into two groups namely iron deficient and non-iron deficient. Serum thyroid-stimulating hormone (TSH), FT4, and anti-thyroid peroxidase (TPO) values were then compared between the two groups. RESULTS: In the present study, 68% of the females were iron deficient. In the Iron deficient group, serum TSH and anti-TPO levels were significantly higher as compared to that in the non-iron deficient group (35.29% vs. 6.25% and 22.06% vs. 3.13%; p=0.001 and 0.018, respectively). A positive correlation was obtained between FT4 and ferritin with correlation coefficient of 0.907 and p-value of 0.0001. Serum TSH levels and serum anti-TPO levels were inversely correlated with ferritin. Univariate logistic regression analysis revealed that iron deficiency was associated with an increased risk of subclinical hypothyroidism with odds ratio (OR) 8.182 (95% CI: 1.798-37.234, p=0.007) and raised anti-TPO with OR 8.77 (95% CI: 1.105-69.681; p=0.040). CONCLUSIONS: Iron deficiency is frequent during the first trimester of pregnancy and is associated with an increased risk of subclinical hypothyroidism and thyroid autoimmunity.