Integrated clinical and metabolomic analysis of dengue infection shows molecular signatures associated with host-pathogen interaction in different phases of the disease.

PURPOSE: Dengue is a mosquito vector-borne disease caused by the dengue virus, which affects 125 million people globally. The disease causes considerable morbidity. The disease, based on symptoms, is classified into three characteristic phases, which can further lead to complications in the second phase. Molecular signatures that are associated with the three phases have not been well characterized. We performed an integrated clinical and metabolomic analysis of our patient cohort and compared it with omics data from the literature to identify signatures unique to the different phases. METHODS: The dengue patients are recruited by clinicians after standard-of-care diagnostic tests and evaluation of symptoms. Blood from the patients was collected. NS1 antigen, IgM, IgG antibodies, and cytokines in serum were analyzed using ELISA. Targeted metabolomics was performed using LC-MS triple quad. The results were compared with analyzed transcriptomic data from the GEO database and metabolomic data sets from the literature. RESULTS: The dengue patients displayed characteristic features of the disease, including elevated NS1 levels. TNF-α was found to be elevated in all three phases compared to healthy controls. The metabolic pathways were found to be deregulated compared to healthy controls only in phases I and II of dengue patients. The pathways represent viral replication and host response mediated pathways. The major pathways include nucleotide metabolism of various amino acids and fatty acids, biotin, etc. CONCLUSION: The results show elevated TNF-α and metabolites that are characteristic of viral infection and host response. IL10 and IFN-γ were not significant, consistent with the absence of any complications.

Evidence-based insecticide resistance in South American tomato leaf miner, Phthorimaea absoluta (Meyrick) under laboratory selection.

The South American tomato moth, Phthorimaea absoluta (Meyrick), is one of the key pests of tomato in India. Since its report in 2014, chemical control has been the main means of tackling this pest, both in the open field and protected cultivation. Despite regular insecticidal sprays, many outbreaks were reported from major tomato-growing regions of South India during 2019-2020. A study was conducted to investigate the effect of insecticide resistance on biology, biochemical enzymes, and gene expression in various P. absoluta field populations viz., Bangalore, Kolar, Madurai, Salem, and Anantapur to commonly used insecticides such as flubendiamide, cyantraniliprole, and indoxacarb. Increased levels of insecticide resistance ratios (RR) were recorded in P. absoluta populations of different locations. A significant increase in cytochrome P450 monooxygenase (CYP/MFO) and esterase levels was noticed in the resistant population compared to susceptible one. Through molecular studies, we identified four new CYP genes viz., CYP248f (flubendiamide), CYP272c, CYP724c (cyantraniliprole), and CYP648i (indoxacarb). The expression levels of these genes significantly increased as the folds of resistance increased from G1 to G20 (generation), indicating involvement of the identified genes in insecticide resistance development in P. absoluta. In addition, the resistant populations showed decreased fecundity, increased larval development period, and adult longevity, resulting in more crop damage. The information generated in the present study thus helps in understanding the development of insecticide resistance by P. absoluta and suggests the farmers and researchers to use insecticides wisely by adopting insecticide resistance management as a strategy under integrated pest management.

In Vitro and In Vivo Activities of a Bi-Aryl Oxazolidinone, RBx 11760, against Gram-Positive Bacteria.

RBx 11760, a bi-aryl oxazolidinone, was investigated for antibacterial activity against Gram-positive bacteria. The MIC90s of RBx 11760 and linezolid against Staphylococcus aureus were 2 and 4 mg/liter, against Staphylococcus epidermidis were 0.5 and 2 mg/liter, and against Enterococcus were 1 and 4 mg/liter, respectively. Similarly, against Streptococcus pneumoniae the MIC90s of RBx 11760 and linezolid were 0.5 and 2 mg/liter, respectively. In time-kill studies, RBx 11760, tedizolid, and linezolid exhibited bacteriostatic effect against all tested strains except S. pneumoniae RBx 11760 showed 2-log10 kill at 4× MIC while tedizolid and linezolid showed 2-log10 and 1.4-log10 kill at 16× MIC, respectively, against methicillin-resistant S. aureus (MRSA) H-29. Against S. pneumoniae 5051, RBx 11760 showed bactericidal activity, with 4.6-log10 kill at 4× MIC compared to 2.42-log10 and 1.95-log10 kill for tedizolid and linezolid, respectively, at 16× MIC. RBx 11760 showed postantibiotic effects (PAE) at 3 h at 4 mg/liter against MRSA H-29, and linezolid showed the same effect at 16 mg/liter. RBx 11760 inhibited biofilm production against methicillin-resistant S. epidermidis (MRSE) ATCC 35984 in a concentration-dependent manner. In a foreign-body model, linezolid and rifampin resulted in no advantage over stasis, while the same dose of RBx 11760 demonstrated a significant killing compared to the initial control against S. aureus (P < 0.05) and MRSE (P < 0.01). The difference in killing was statistically significant for the lower dose of RBx 11760 (P < 0.05) versus the higher dose of linezolid (P > 0.05 [not significant]) in a groin abscess model. In neutropenic mouse thigh infection, RBx 11760 showed stasis at 20 mg/kg of body weight, whereas tedizolid showed the same effect at 40 mg/kg. These data support RBx 11760 as a promising investigational candidate.

Nucleotide variation and identification of novel blast resistance alleles of Pib by allele mining strategy.

Pib is one of significant rice blast resistant genes, which provides resistance to wide range of isolates of rice blast pathogen, Magnaporthe oryzae. Identification and isolation of novel and beneficial alleles help in crop enhancement. Allele mining is one of the best strategies for dissecting the allelic variations at candidate gene and identification of novel alleles. Hence, in the present study, Pib was analyzed by allele mining strategy, and coding and non-coding (upstream and intron) regions were examined to identify novel Pib alleles. Allelic sequences comparison revealed that nucleotide polymorphisms at coding regions affected the amino acid sequences, while the polymorphism at upstream (non-coding) region affected the motifs arrangements. Pib alleles from resistant landraces, Sercher and Krengosa showed better resistance than Pib donor variety, might be due to acquired mutations, especially at LRR region. The evolutionary distance, Ka/Ks and phylogenetic analyzes also supported these results. Transcription factor binding motif analysis revealed that Pib (Sr) had a unique motif (DPBFCOREDCDC3), while five different motifs differentiated the resistance and susceptible Pib alleles. As the Pib is an inducible gene, the identified differential motifs helps to understand the Pib expression mechanism. The identified novel Pib resistant alleles, which showed high resistance to the rice blast, can be used directly in blast resistance breeding program as alternative Pib resistant sources.

Skull Base Metastasis and Krukenberg Tumor in a Pregnant Woman: An Unusual Presentation of Metastatic Gastric Cancer.

Gastric cancer is an infrequent cause of vomiting during pregnancy. It is often diagnosed at an advanced stage due to late presentation by patients, mistaking it for gestational symptoms. We report a 24-year-old pregnant woman with gastric cancer with skull base metastasis and Krukenberg tumor on initial diagnosis. She underwent medical termination of pregnancy and best supportive care before dying of her illness.

Radiological Parameters Predicting the Round Window Niche Visibility through Facial Recess Approach in Cochlear Implantation.

Aim: The aim was to study the radiological parameters using High Resolution Computed Tomography (HRCT) temporal bone to predict the Round Window Niche (RWN) visibility through the facial recess approach and to study radiological types of the round window niche. Materials and Methods: Prospective study was done in the patients underwent CI surgery from 2019 to 2021. HRCT radiological parameters of the patients and their intraoperative visualisation from video recordings were compared to predict the most feasible parameters to predict good visualisation of RWN. Results: Among 51 patients (34 males, 17 females) in 48 children round window membrane insertion was done and in three children cochleostomy was done and in two children partial canal wall drilling was done due to poor visualisation of RWN area. Multiple parameters to assess the visibility of the RWN were used. Facial recess width (4.2 mm), location of the mastoid segment of facial nerve (2 mm), external auditory canal to basal turn of cochlea angle (< 13.50) and the radiological types (tunnel shape and semi-circular shape) of the RWN by HRCT were found to be significant parameters in predicting a good visualisation of the RWN. Conclusion: HRCT parameters prepare the surgeon to face the possibility of a difficult surgery and plan to deal with difficult situations. This would eventually lead to better preparedness of surgeons for management of complications.

Light quality influences the virulence and physiological responses of Colletotrichum acutatum causing anthracnose in pepper plants.

AIMS: To explore the effects of light quality on the physiology and pathogenicity of Colletotrichum acutatum, we analysed the morphological traits, melanin production and virulence of the pathogen under different light wavelengths. METHODS AND RESULTS: The influence of light wavelength on the mycelial growth and conidial germination of C. acutatum was investigated using red, green, blue and white light sources. Red and green light reduced the mycelial growth in comparison with blue and white light, and dark conditions. The least percentage of conidial germination was observed under blue light, while the germination rate among white, red and green light, as well as in the dark, was insignificant. In comparison with its influence on mycelial growth and conidial germination, light wavelength significantly affected the pathogen's virulence towards hot pepper fruits. The highest disease severity was observed under blue light, which was at least a twofold increase compared with the disease severity under other light conditions. To elucidate the effect of light on the disparity in virulence, scytalone was assayed by HPLC, and scd1 gene expression was examined with real-time PCR. The highest and lowest scytalone production was observed in the cultures incubated under blue (10.9 mAU) and green light (1.5 mAU), respectively. Higher scd1 gene expression (~ 40-fold increase) was observed in cultures incubated under blue and white light in comparison with those incubated in the dark. CONCLUSIONS: This study revealed that light affects the growth, colonial morphology and virulence of C. acutatum. The pathogen needs light for its active melanin production and also to attain higher virulence. SIGNIFICANCE AND IMPACT OF THE STUDY: This is the first report on the effect of light quality on the virulence of C. acutatum. The findings of this study will broaden our knowledge of the influence of light on physiological responses of fungal pathogens.

Deep learning techniques in liver tumour diagnosis using CT and MR imaging - A systematic review.

Deep learning has become a thriving force in the computer aided diagnosis of liver cancer, as it solves extremely complicated challenges with high accuracy over time and facilitates medical experts in their diagnostic and treatment procedures. This paper presents a comprehensive systematic review on deep learning techniques applied for various applications pertaining to liver images, challenges faced by the clinicians in liver tumour diagnosis and how deep learning bridges the gap between clinical practice and technological solutions with an in-depth summary of 113 articles. Since, deep learning is an emerging revolutionary technology, recent state-of-the-art research implemented on liver images are reviewed with more focus on classification, segmentation and clinical applications in the management of liver diseases. Additionally, similar review articles in literature are reviewed and compared. The review is concluded by presenting the contemporary trends and unaddressed research issues in the field of liver tumour diagnosis, offering directions for future research in this field.

Advance on the biology, behaviour ecology and management of the coffee white stem borer, Xylotrechus quadripes Chevrolat, 1863 (Coleoptera: Cerambycidae).

The coffee white stem borer, Xylotrechus quadripes Chevrolat, 1863 (Coleoptera: Cerambycidae) - here removed from the synonymy with X. javanicus (Laporte & Gory, 1841) - is the most notorious pest in Arabica coffee plantations in many southeast Asian countries. It can cause damage up to 80% in various gardens. The borer is reported on 16 different host plants other than coffee. The severity of the pest was more commonly recorded on the Arabica coffee than on other species. More pest intensity on the coffee may be due to its innate evolutionary relation compared to other host plants. Studies revealed that the borer is more specific and attracted to the volatile of coffee plants but it is still needs a strong supporting data. Some of the behavioural and ecological-adaptations of borers leads to avoid predation and chemical-pesticides reaching them. Hence, no single method gives perfect control of this pest; therefore, harmonic use of different tools such as cultural, mechanical, physical, bio-control and chemical methods are the best way to combat this pest. Though the pest is economically important, the information on chemical and ecological behaviour, host plant resistance and recent advancements in the pest management are scanty. The present article is an endeavour to shed a light on biology, behaviour, host selection and management of X. quadripes with multiple instances, that will give a new avenue for the researchers to work on the least concerned fields to develop strong management practice and alert against future pest outbreak.

Diagnostic challenges in cerebral tuberculoma presenting with seizures in pregnancy.

Tuberculoma is an uncommon presentation of tuberculosis and is found in regions with a high prevalence of tuberculosis. This is rarely diagnosed during pregnancy. The presentation can mimic other etiologies such as eclampsia or cerebral venous sinus thrombosis so the diagnosis can be challenging, particularly when presenting with seizures in pregnancy. Described here is a woman in her first pregnancy who presented with seizures mimicking eclampsia and was suspected to have a brain tumour on neuroimaging. She was diagnosed to have a intracerebral tuberculoma on histopathological examination following surgical decompression after delivery.

In vitro and in vivo activities of the novel Ketolide RBx 14255 against Clostridium difficile.

The MIC(90) of RBx 14255, a novel ketolide, against Clostridium difficile was 4 µg/ml (MIC range, 0.125 to 8 µg/ml), and this drug was found to be more potent than comparator drugs. An in vitro time-kill kinetics study of RBx 14255 showed time-dependent bacterial killing for C. difficile. Furthermore, in the hamster model of C. difficile infection, RBx 14255 demonstrated greater efficacy than metronidazole and vancomycin, making it a promising candidate for C. difficile treatment.

Synthesis and antibacterial activity of a novel series of acylides active against community acquired respiratory pathogens.

A novel series of acylides 4 were designed to overcome antibacterial resistance and evaluated for in vitro and in vivo activity. This series of acylides was designed from clarithromycin by changing the substitution on the desosamine nitrogen, followed by conversion to 3-O-acyl and 11,12-carbamate. These compounds showed significantly potent antibacterial activity against not only Gram-positive pathogens, including macrolide-lincosamide-streptogramin B (MLS(B))-resistant and efflux-resistant strains, but also Gram-negative pathogens such as Haemophilus influenzae. These acylides also showed better activity against telithromycin resistant Streptococcus pneumoniae strains.

The molecular diversity and evolution of Rice tungro bacilliform virus from Indian perspective.

Rice tungro disease is caused by a combination of two viruses: Rice tungro spherical virus and Rice tungro bacilliform virus (RTBV). This study was performed with the objective to decipher the molecular variability and evolution of RTBV isolates present in the tungro-affected states of Indian subcontinent. Phylogenetic analysis based on ORF-I, ORF-II, and ORF-IV sequences showed distinct divergence of Indian RTBV isolates into two groups; one consisted isolates from Hyderabad (Andhra Pradesh), Cuttack (Orissa), and Puducherry and another from West Bengal, Chinsura West Bengal, and Kanyakumari (Tamil Nadu). The results obtained from phylogenetic analysis were further supported with the single nucleotide polymorphisms (SNPs), insertion and deletion (INDELs) and evolutionary distance analysis. In addition, sequence difference count matrix revealed a maximum of 56 (ORF-I), 13 (ORF-II) and 73 (ORF-IV) nucleotides differences among all the Indian RTBV isolates taken in this study. However, at the protein level these differences were not significant as revealed by K (a)/K (s) ratio calculation. Sequence identity at nucleotide and amino acid level was 92-100 % (ORF-I), 96-100 % (ORF-II), 94-100 % (ORF-IV) and 86-100 % (ORF-I), 98-100 % (ORF-II) and 95-100 % (ORF-IV), respectively, among Indian isolates of RTBV. The divergence of RTBV isolates into two independent clusters of Indian and non-Indian was shown with the help of the data obtained from phylogeny, SNPs, and INDELs, evolutionary distance analysis, and conserved motifs analysis. The important role of ORF-I and ORF-IV in RTBV diversification and adaptation to different rice growing regions is also discussed.

Genetic variation of coat protein gene among the isolates of Rice tungro spherical virus from tungro-endemic states of the India.

Rice tungro disease, one of the major constraints to rice production in South and Southeast Asia, is caused by a combination of two viruses: Rice tungro spherical virus (RTSV) and Rice tungro bacilliform virus (RTBV). The present study was undertaken to determine the genetic variation of RTSV population present in tungro endemic states of Indian subcontinent. Phylogenetic analysis based on coat protein sequences showed distinct divergence of Indian RTSV isolates into two groups; one consisted isolates from Hyderabad (Andhra Pradesh), Cuttack (Orissa), and Puducherry and another from West Bengal, Coimbatore (Tamil Nadu), and Kanyakumari (Tamil Nadu). The results obtained from phylogenetic study were further supported with the SNPs (single nucleotide polymorphism), INDELs (insertion and deletion) and evolutionary distance analysis. In addition, sequence difference count matrix revealed 2-68 nucleotides differences among all the Indian RTSV isolates taken in this study. However, at the protein level these differences were not significant as revealed by Ka/Ks ratio calculation. Sequence identity at nucleotide and amino acid level was 92-100% and 97-100%, respectively, among Indian isolates of RTSV. Understanding of the population structure of RTSV from tungro endemic regions of India would potentially provide insights into the molecular diversification of this virus.

An Automated Deep Learning Model for the Cerebellum Segmentation from Fetal Brain Images.

Cerebellum measures taken from routinely obtained ultrasound (US) images have been frequently employed to determine gestational age and identify developing central nervous system's anatomical abnormalities. Standardized cerebellar assessments from large-scale clinical datasets are required to investigate correlations between the growing cerebellum and postnatal neurodevelopmental results. These studies could uncover structural abnormalities that could be employed as indicators to forecast neurodevelopmental and growth consequences. To achieve this, higher-throughput, precise, and impartial measures must be used to replace the existing human, semiautomatic, and advanced algorithms, which seem to be time-consuming and inaccurate. In this article, we presented an innovative deep learning (DL) technique for automatic fetal cerebellum segmentation from 2-dimensional (2D) US brain images. We present ReU-Net, a semantic segmentation network tailored to the anatomy of the fetal cerebellum. Moreover, we use U-Net as a foundation models with the incorporation of residual blocks and Wiener filter over the last 2 layers to segregate the cerebellum (c) from the noisy US data. 590 images for training and 150 images for testing were taken; also, we employed a 5-fold cross-assessment method. Our ReU-Net scored 91%, 92%, 25.42, 98%, 92%, and 94% for Dice Score Coefficient (DSC), F1-score, Hausdorff Distance (HD), accuracy, recall, and precision, correspondingly. The suggested method outperforms the other U-Net predicated techniques by a quantitatively significant margin (p 0.001). Our presented approach can be used to allow high bandwidth imaging techniques in medical study fetal US images as well as biometric evaluation on a broader scale in fetal US images.

Implementation of Machine Learning Mechanism for Recognising Prostate Cancer through Photoacoustic Signal.

Biological tissues may be studied using photoacoustic (PA) spectroscopy, which can yield a wealth of physical and chemical data. However, it is really challenging to directly analyse these tissues because of a lot of data. Data mining techniques can get around this issue. In order to diagnose prostate cancer via PA spectrum assessment, this work describes the machine learning (ML) technique implementation, such as supervised classification and unsupervised hierarchical clustering. The collected PA signals were preprocessed using Pwelch method, and the features are extracted using two methods such as hierarchical cluster and correlation assessment. The extracted features are classified using four ML-methods, namely, Support Vector Machine (SVM), Naïve Bayes (NB), decision tree C4.5, and Linear Discriminant Analysis (LDA). Furthermore, as these components alter throughout the progression of prostate cancer, this study focuses on the composition and distribution of collagen, lipids, and haemoglobin. In diseased tissues compared to normal tissues, there is a stronger correlation between the various chemical components ultrasonic power spectra, suggesting that the microstructural dispersion in tumour tissues has been more uniform. The accuracy of several classifiers used in cancer tissue diagnosis was greater than 94% for all four methods, which is effective than that of benchmark medical methods. Thus, the method shows significant promise for the noninvasive, early detection of severe prostate cancer.

A Modified Deep Learning Framework for Arrhythmia Disease Analysis in Medical Imaging Using Electrocardiogram Signal.

Arrhythmias are anomalies in the heartbeat rhythm that occur occasionally in people's lives. These arrhythmias can lead to potentially deadly consequences, putting your life in jeopardy. As a result, arrhythmia identification and classification are an important aspect of cardiac diagnostics. An electrocardiogram (ECG), a recording collecting the heart's pumping activity, is regarded the guideline for catching these abnormal episodes. Nevertheless, because the ECG contains so much data, extracting the crucial data from imagery evaluation becomes extremely difficult. As a result, it is vital to create an effective system for analyzing ECG's massive amount of data. The ECG image from ECG signal is processed by some image processing techniques. To optimize the identification and categorization process, this research presents a hybrid deep learning-based technique. This paper contributes in two ways. Automating noise reduction and extraction of features, 1D ECG data are first converted into 2D pictures. Then, based on experimental evidence, a hybrid model called CNNLSTM is presented, which combines CNN and LSTM models. We conducted a comprehensive research using the broadly used MIT_BIH arrhythmia dataset to assess the efficacy of the proposed CNN-LSTM technique. The results reveal that the proposed method has a 99.10 percent accuracy rate. Furthermore, the proposed model has an average sensitivity of 98.35 percent and a specificity of 98.38 percent. These outcomes are superior to those produced using other procedures, and they will significantly reduce the amount of involvement necessary by physicians.

A Novel Deep Learning-Based Black Fungus Disease Identification Using Modified Hybrid Learning Methodology.

Currently, countries across the world are suffering from a prominent viral infection called COVID-19. Most countries are still facing several issues due to this disease, which has resulted in several fatalities. The first COVID-19 wave caused devastation across the world owing to its virulence and led to a massive loss in human lives, impacting the country's economy drastically. A dangerous disease called mucormycosis was discovered worldwide during the second COVID-19 wave, in 2021, which lasted from April to July. The mucormycosis disease is commonly known as "black fungus," which belongs to the fungus family Mucorales. It is usually a rare disease, but the level of destruction caused by the disease is vast and unpredictable. This disease mainly targets people already suffering from other diseases and consuming heavy medication to counter the disease they are suffering from. This is because of the reduction in antibodies in the affected people. Therefore, the patient's body does not have the ability to act against fungus-oriented infections. This black fungus is more commonly identified in patients with coronavirus disease in certain country. The condition frequently manifests on skin, but it can also harm organs such as eyes and brain. This study intends to design a modified neural network logic for an artificial intelligence (AI) strategy with learning principles, called a hybrid learning-based neural network classifier (HLNNC). The proposed method is based on well-known techniques such as convolutional neural network (CNN) and support vector machine (SVM). This article discusses a dataset containing several eye photographs of patients with and without black fungus infection. These images were collected from the real-time records of people afflicted with COVID followed by the black fungus. This proposed HLNNC scheme identifies the black fungus disease based on the following image processing procedures: image acquisition, preprocessing, feature extraction, and classification; these procedures were performed considering the dataset training and testing principles with proper performance analysis. The results of the procedure are provided in a graphical format with the precise specification, and the efficacy of the proposed method is established.

Automated Recognition of Cancer Tissues through Deep Learning Framework from the Photoacoustic Specimen.

The fast advancement of biomedical research technology has expanded and enhanced the spectrum of diagnostic instruments. Various research groups have found optical imaging, ultrasonic imaging, and magnetic resonance imaging to create multifunctional devices that are critical for biomedical activities. Multispectral photoacoustic imaging that integrates the ideas of optical and ultrasonic technologies is one of the most essential instruments. At the same time, early cancer identification is becoming increasingly important in order to minimize fatality. Deep learning (DL) techniques have recently advanced to the point where they can be used to diagnose and classify cancer using biological images. This paper describes a hybrid optimization method that combines in-depth transfer learning-based cancer detection with multispectral photoacoustic imaging. The goal of the PS-ACO-RNN approach is to use ultrasound images to detect and classify the presence of cancer. Bilateral filtration (BF) is often used as a noise removal approach in image processing. In addition, lightweight LEDNet models are used to separate the biological images. A feature extractor with particle swarm with ant colony optimization (PS-ACO) paradigm can also be used. Finally, biological images assign appropriate class labels using a recurrent neural network (RNN) model. The effectiveness of the PS-ACO-RNN technique is verified using a benchmark database, and test results show that the PS-ACO-RNN approach works better than current approaches.

Energetic Glaucoma Segmentation and Classification Strategies Using Depth Optimized Machine Learning Strategies.

Glaucoma is a major threatening cause, in which it affects the optical nerve to lead to a permanent blindness to individuals. The major causes of Glaucoma are high pressure to eyes, family history, irregular sleeping habits, and so on. These kinds of causes lead to Glaucoma easily, and the effect of such disease leads to heavy damage to the internal optic nervous system and the affected person will get permanent blindness within few months. The major problem with this disease is that it is incurable; however, the affection stages can be reduced and the same level of effect as that for the long period can be maintained but this is possible only in the earlier stages of identification. This Glaucoma causes structural effect to the eye ball and it is complex to estimate the cause during regular diagnosis. In medical terms, the Cup to Disc Ratio (CDR) is minimized to the Glaucoma patients suddenly and leads to harmful damage to one's eye in severe manner. The general way to identify the Glaucoma is to take Optical Coherence Tomography (OCT) test, in which it captures the uncovered portion of eye ball (backside) and it is an efficient way to visualize diverse portions of eyes with optical nerve visibility shown clearly. The OCT images are mainly used to identify the diseases like Glaucoma with proper and robust accuracy levels. In this work, a new methodology is introduced to identify the Glaucoma in earlier stages, called Depth Optimized Machine Learning Strategy (DOMLS), in which it adapts the new optimization logic called Modified K-Means Optimization Logic (MkMOL) to provide best accuracy in results, and the proposed approach assures the accuracy level of more than 96.2% with least error rate of 0.002%. This paper focuses on the identification of early stage of Glaucoma and provides an efficient solution to people in case of effect by such disease using OCT images. The exact position pointed out is handled by using Region of Interest- (ROI-) based optical region selection, in which it is easy to point the optical cup (OC) and optical disc (OD). The proposed algorithm of DOMLS proves the accuracy levels in estimation of Glaucoma and the practical proofs are shown in the Result and Discussions section in a clear manner.