Disease X and COVID-19: turning lessons from India and the world into policy recommendations.

Disease X is caused by pathogen X, an unknown infectious agent that can potentially trigger an epidemic or pandemic. Pathogen X might be any pathogen, including bacteria, viruses, parasites, fungi, and prions. WHO uses the term 'Disease X' for any new emerging disease caused by an unknown pathogen X. Disease X stands for any possible future pandemic in WHO's shortlist of high-priority diseases. This review looks at the manifestations of the recent COVID-19 epidemic as the first Disease X to evaluate what has happened and to learn from what went wrong in India and worldwide. To this end, a summary is presented of response measures by governments, often lacking flows of information, discrepancies in the views of experts and decisions of policymakers, and undesirable variations in individual and collective behavior and their consequences. The elements of combating Disease X in a world with considerable inequalities in relevant knowledge, expertise, information, quality of governance, and financial possibilities are discussed. Based on this, recommendations are given for an innovative global pandemic preparedness system.

An explainable ensemble approach for advanced brain tumor classification applying Dual-GAN mechanism and feature extraction techniques over highly imbalanced data.

Brain tumors are one of the leading diseases imposing a huge morbidity rate across the world every year. Classifying brain tumors accurately plays a crucial role in clinical diagnosis and improves the overall healthcare process. ML techniques have shown promise in accurately classifying brain tumors based on medical imaging data such as MRI scans. These techniques aid in detecting and planning treatment early, improving patient outcomes. However, medical image datasets are frequently affected by a significant class imbalance, especially when benign tumors outnumber malignant tumors in number. This study presents an explainable ensemble-based pipeline for brain tumor classification that integrates a Dual-GAN mechanism with feature extraction techniques, specifically designed for highly imbalanced data. This Dual-GAN mechanism facilitates the generation of synthetic minority class samples, addressing the class imbalance issue without compromising the original quality of the data. Additionally, the integration of different feature extraction methods facilitates capturing precise and informative features. This study proposes a novel deep ensemble feature extraction (DeepEFE) framework that surpasses other benchmark ML and deep learning models with an accuracy of 98.15%. This study focuses on achieving high classification accuracy while prioritizing stable performance. By incorporating Grad-CAM, it enhances the transparency and interpretability of the overall classification process. This research identifies the most relevant and contributing parts of the input images toward accurate outcomes enhancing the reliability of the proposed pipeline. The significantly improved Precision, Sensitivity and F1-Score demonstrate the effectiveness of the proposed mechanism in handling class imbalance and improving the overall accuracy. Furthermore, the integration of explainability enhances the transparency of the classification process to establish a reliable model for brain tumor classification, encouraging their adoption in clinical practice promoting trust in decision-making processes.

Identification of Putative Biomarkers in Cerebral Palsy: A Meta-Analysis and Meta-Regression.

BACKGROUND: Cerebral palsy (CP) is a neurological disorder that impairs motor abilities. Identifying maternal biomarker derangements can facilitate further evaluation for early diagnosis, potentially leading to improved clinical outcomes. This study investigates the association between maternal biomarker derangements and CP development during the antenatal period. METHODS: A systematic search was conducted in MEDLINE, EMBASE, and Cochrane databases, following MOOSE guidelines. Data on participants exceeding biomarker thresholds (95th and 5th percentiles) were extracted for combined odds ratio estimation. Geometric mean differences, reported as multiples of the median (MoMs), were used to analyze changes in marker levels. Trimesterwise subgroup analysis and metaregression assessed the impact of variables on outcomes. RESULTS: Five observational studies (1552 cases, 484,985 controls) revealed lower maternal pregnancy-associated plasma protein A levels were associated with CP (pooled odds ratio [OR] = 1.60, 95% confidence interval [CI] = 1.22 to 2.09; I = 0%), with a -0.04 MoM geometric mean difference. Lower maternal beta-human chorionic gonadotropin (HCG) levels in first and second trimesters indicated a pooled OR = 1.18 (95% CI = 0.85 to 1.63; I = 57%). Sensitivity analysis showed an OR = 1.40 (95% CI = 1.08 to 1.82; I = 0%), with a -0.07 MoM geometric mean difference. Metaregression identified primigravida status as negatively influencing beta-HCG levels. Elevated nuchal translucency values and CP presented a pooled OR = 1.06 (95% CI = 0.77 to 1.44; I = 0%). CONCLUSION: Lower maternal pregnancy-associated plasma protein A levels during the first trimester and lower beta-HCG levels in the first and second trimesters are associated with CP development in children. Future research should validate the predictive utility of these biomarkers and explore novel ones through large-scale cohort studies.

Exploring the role of macromolecular crowding and TNFR1 in cell volume control.

The excessive cosolute densities in the intracellular fluid create a physicochemical condition called macromolecular crowding (MMC). Intracellular MMC entropically maintains the biochemical thermodynamic equilibria by favouring associative reactions while hindering transport processes. Rapid cell volume shrinkage during extracellular hypertonicity elevates the MMC and disrupts the equilibria, potentially ushering cell death. Consequently, cells actively counter the hypertonic stress through regulatory volume increase (RVI) and restore the MMC homeostasis. Here, we establish fluorescence anisotropy of EGFP as a reliable tool for studying cellular MMC and explore the spatiotemporal dynamics of MMC during cell volume instabilities under multiple conditions. Our studies reveal that the actin cytoskeleton enforces spatially varying MMC levels inside adhered cells. Within cell populations, MMC is uncorrelated with nuclear DNA content but anti-correlated with the cell spread area. Although different cell lines have statistically similar MMC distributions, their responses to extracellular hypertonicity vary. The intensity of the extracellular hypertonicity determines a cell's ability for RVI, which correlates with Nuclear Factor Kappa Beta (NFkB) activation. Pharmacological inhibition and knockdown experiments reveal that Tumour Necrosis Factor Receptor 1 (TNFR1) initiates the hypertonicity induced NFkB signalling and RVI. At severe hypertonicities, the elevated MMC amplifies cytoplasmic microviscosity and hinders Receptor Interacting Protein Kinase 1 (RIPK1) recruitment at the TNFR1 complex, incapacitating the TNFR1-NFkB signalling and consequently, RVI. Together, our studies unveil the involvement of TNFR1-NFkB signalling in modulating RVI and demonstrate the pivotal role of MMC in determining cellular osmoadaptability.

Delineating dengue risk zones in Jaipur: An interdisciplinary approach to inform public health strategies.

Dengue fever (DF) is a pervasive public health concern in tropical climates, with densely populated regions, such as India, disproportionately affected. Addressing this issue requires a multifaceted understanding of the environmental and sociocultural factors that contribute to the risk of dengue infection. This study aimed to identify high-risk zones for DF in Jaipur, Rajasthan, India, by integrating physical, demographic, and epidemiological data in a comprehensive risk analysis framework. We investigated environmental variables, such as soil type and plant cover, to characterize the potential habitats of Aedes aegypti, the primary dengue vector. Concurrently, demographic metrics were evaluated to assess the population's susceptibility to dengue outbreaks. High-risk areas were systematically identified through a comparative analysis that integrated population density and incidence rates per ward. The results revealed a significant correlation between high population density and an increased risk of dengue, predominantly facilitated by vertical transmission. Spatially, these high-risk zones are concentrated in the northern and southern sectors of Jaipur, with the northern and southwestern wards exhibiting the most acute risk profiles. This study underscores the importance of targeted public health interventions and vaccination campaigns in vulnerable areas. It further lays the groundwork for future research to evaluate the effectiveness of such interventions, thereby contributing to the development of robust evidence-based strategies for dengue risk mitigation.

Germline variation contributes to false negatives in CRISPR-based experiments with varying burden across ancestries.

Reducing disparities is vital for equitable access to precision treatments in cancer. Socioenvironmental factors are a major driver of disparities, but differences in genetic variation likely also contribute. The impact of genetic ancestry on prioritization of cancer targets in drug discovery pipelines has not been systematically explored due to the absence of pre-clinical data at the appropriate scale. Here, we analyze data from 611 genome-scale CRISPR/Cas9 viability experiments in human cell line models to identify ancestry-associated genetic dependencies essential for cell survival. Surprisingly, we find that most putative associations between ancestry and dependency arise from artifacts related to germline variants. Our analysis suggests that for 1.2-2.5% of guides, germline variants in sgRNA targeting sequences reduce cutting by the CRISPR/Cas9 nuclease, disproportionately affecting cell models derived from individuals of recent African descent. We propose three approaches to mitigate this experimental bias, enabling the scientific community to address these disparities.

Exploring the role of Orexin-A neuropeptide in Parkinson's disease: A systematic review and meta-analysis.

BACKGROUND: Parkinson's disease (PD) is a progressive neurological condition that affects movement and coordination. Orexin-A (OXA) is an excitatory neuropeptide that is found throughout the central nervous system. There is growing interest in investigating the potential diagnostic and therapeutic utility of OXA in PD. To date, studies have reported a wide range of OXA concentrations in patients with PD. In this review, we discuss the current understanding of the dysregulation of OXA in PD and analyze its levels in the CSF. METHODS: We searched six databases (PubMed, Scopus, Web of Science, EMBASE, ProQuest, and EBSCOHost) and preprint servers using a predetermined search strategy through 4th March 4, 2023. The search keywords included "Parkinson's disease", "Orexin-A", "Hypocretin-1", "cerebrospinal fluid", and "CSF". Studies that reported OXA/Hypocretin-1 levels in the CSF of patients with PD were included. Two researchers independently reviewed the records and extracted data. FINDINGS: Eighteen studies involving 244 patients were analyzed. CSF Orexin-A concentrations were lower in patients with Parkinson's disease than in controls, with a mean difference of -59.21 (95 % CI: -89.10 to -29.32). The mean OXA levels were 281.52 (95 % CI: 226.65-336.40). CONCLUSION: Our analysis reveals lower concentrations of orexin-A in the cerebrospinal fluid of Parkinson's disease patients compared to controls, but within the normal range. These findings suggest a potential, but not significant, disruption in the orexinergic system associated with the disease.

Zavegepant for Acute Treatment of Migraine: A Systematic Review and Meta-analysis of Randomized Controlled Trials.

OBJECTIVE: Evaluate the safety and efficacy of zavegepant (BHV-3500), a recently approved nasal spray containing a third-generation calcitonin gene-related peptide receptor antagonist, for treating acute migraine attacks. METHODS: A comprehensive search was conducted across various databases up to 06/26/2023 to identify relevant randomized clinical trials (RCTs) on zavegepant's efficacy and safety in treatment of acute migraine attacks. Primary outcome: freedom from pain at 2 hours postdose. Safety outcomes were evaluated based on adverse events (AEs), with zavegepant 10 mg and placebo groups compared for incidence of AEs. RESULTS: Two RCTs, involving 2061 participants (1014 receiving zavegepant and 1047 receiving placebo), were quantitatively analyzed. An additional trial was included for qualitative synthesis. Zavegepant 10 mg exhibited a significantly higher likelihood of achieving freedom from pain at 2 hours postdose compared with the placebo group (risk ratio [RR] 1.54, 95% confidence interval [CI] 1.28 to 1.84). It also showed superior relief from the most bothersome symptoms at 2 hours postdose compared with placebo (RR 1.26, 95% CI 1.13 to 1.42). However, the zavegepant 10 mg group experienced a higher incidence of AEs compared with placebo (RR 1.78, 95% CI 1.5 to 2.12), with dysgeusia being the most reported AE (RR 4.18, 95% CI 3.05 to 5.72). CONCLUSION: Zavegepant 10 mg is more effective than placebo in treating acute migraine attacks, providing compelling evidence of its efficacy in relieving migraine pain and most bothersome associated symptoms. Further trials are necessary to confirm its efficacy, tolerability, and safety in diverse clinic-based settings with varied patient populations.

The advent of RNA-based therapeutics for metabolic syndrome and associated conditions: a comprehensive review of the literature.

Metabolic syndrome (MetS) is a prevalent and intricate health condition affecting a significant global population, characterized by a cluster of metabolic and hormonal disorders disrupting lipid and glucose metabolism pathways. Clinical manifestations encompass obesity, dyslipidemia, insulin resistance, and hypertension, contributing to heightened risks of diabetes and cardiovascular diseases. Existing medications often fall short in addressing the syndrome's multifaceted nature, leading to suboptimal treatment outcomes and potential long-term health risks. This scenario underscores the pressing need for innovative therapeutic approaches in MetS management. RNA-based treatments, employing small interfering RNAs (siRNAs), microRNAs (miRNAs), and antisense oligonucleotides (ASOs), emerge as promising strategies to target underlying biological abnormalities. However, a summary of research available on the role of RNA-based therapeutics in MetS and related co-morbidities is limited. Murine models and human studies have been separately interrogated to determine whether there have been recent advancements in RNA-based therapeutics to offer a comprehensive understanding of treatment available for MetS. In a narrative fashion, we searched for relevant articles pertaining to MetS co-morbidities such as cardiovascular disease, fatty liver disease, dementia, colorectal cancer, and endocrine abnormalities. We emphasize the urgency of exploring novel therapeutic avenues to address the intricate pathophysiology of MetS and underscore the potential of RNA-based treatments, coupled with advanced delivery systems, as a transformative approach for achieving more comprehensive and efficacious outcomes in MetS patients.

Self-medication prevalence and associated factors among adult population in Northern India: A community-based cross-sectional study.

Objectives: This study aimed to determine self-medication prevalence and its associated factors. Methods: A community-based cross-sectional study was conducted in the urban and rural catchment areas of Uttar Pradesh, India, among 440 adults using a pretested, semistructured questionnaire. The Chi-square test and logistic regression were used to determine the association of self-medication prevalence with various independent variables. The associations were reported as adjusted odds ratios and 95% confidence intervals. Results: The prevalence of medication use was 66.4%. The majority of participants (45%) took medicine for fever, cough (40.1%), and cold (31.8%). Allopathy (83.2%) was the most common medicine system used for self-medication. More than half reported taking medicine such as paracetamol (52%), followed by cough syrup (21%) and antihistaminic (17%). Convenience (46%) and lack of time (35.3%) were commonly cited reasons for self-medication. Also, 64.4% of the respondents practiced self-medication on the pharmacist's recommendation. Urban participants (adjusted odds ratio: 9.85, 95% confidence interval: 5.32-18.23), females (adjusted odds ratio: 2.32, 95% confidence interval: 1.18-4.57), skilled workers (adjusted odds ratio: 5.62, 95% confidence interval: 1.80-17.5), and those who completed primary school (adjusted odds ratio: 2.48, 95% confidence interval: 1.16-5.25) were more likely to self-medicate than rural, male, unemployed, and illiterate participants, respectively. Also, participants whose income was 30,000 Indian rupees (adjusted odds ratio: 3.21, 95% confidence interval: 1.00-10.21) were more likely to self-medicate than those whose income was less than 4000. Conclusions: A high prevalence of self-medication was found, particularly in urban areas. Convenience and lack of time were commonly cited reasons for self-medication. Allopathy was the most widely used medicine system for self-medication. Antipyretics, cough syrups, and antiallergics were most commonly self-medicated. Gender, education, and income were associated with self-medication. The study highlighted the increased usage among females which could be further explored and role of pharmacists' recommendation as a major driver for self-medication.

Recent nanotheranostic approaches in cancer research.

Humanity is suffering from cancer which has become a root cause of untimely deaths of individuals around the globe in the recent past. Nanotheranostics integrates therapeutics and diagnostics to monitor treatment response and enhance drug efficacy and safety. We hereby propose to discuss all recent cancer imaging and diagnostic tools, the mechanism of targeting tumor cells, and current nanotheranostic platforms available for cancer. This review discusses various nanotheranostic agents and novel molecular imaging tools like MRI, CT, PET, SPEC, and PAT used for cancer diagnostics. Emphasis is given to gold nanoparticles, silica, liposomes, dendrimers, and metal-based agents. We also highlight the mechanism of targeting the tumor cells, and the limitations of different nanotheranostic agents in the field of research for cancer treatment. Due to the complexity in this area, multifunctional and hybrid nanoparticles functionalized with targeted moieties or anti-cancer drugs show the best feature for theranostics that enables them to work on carrying and delivering active materials to the desired area of the requirement for early detection and diagnosis. Non-invasive imaging techniques have a specificity of receptor binding and internalization processes of the nanosystems within the cancer cells. Nanotheranostics may provide the appropriate medicine at the appropriate dose to the appropriate patient at the appropriate time.

A novel and innovative cancer classification framework through a consecutive utilization of hybrid feature selection.

Cancer prediction in the early stage is a topic of major interest in medicine since it allows accurate and efficient actions for successful medical treatments of cancer. Mostly cancer datasets contain various gene expression levels as features with less samples, so firstly there is a need to eliminate similar features to permit faster convergence rate of classification algorithms. These features (genes) enable us to identify cancer disease, choose the best prescription to prevent cancer and discover deviations amid different techniques. To resolve this problem, we proposed a hybrid novel technique CSSMO-based gene selection for cancer classification. First, we made alteration of the fitness of spider monkey optimization (SMO) with cuckoo search algorithm (CSA) algorithm viz., CSSMO for feature selection, which helps to combine the benefit of both metaheuristic algorithms to discover a subset of genes which helps to predict a cancer disease in early stage. Further, to enhance the accuracy of the CSSMO algorithm, we choose a cleaning process, minimum redundancy maximum relevance (mRMR) to lessen the gene expression of cancer datasets. Next, these subsets of genes are classified using deep learning (DL) to identify different groups or classes related to a particular cancer disease. Eight different benchmark microarray gene expression datasets of cancer have been utilized to analyze the performance of the proposed approach with different evaluation matrix such as recall, precision, F1-score, and confusion matrix. The proposed gene selection method with DL achieves much better classification accuracy than other existing DL and machine learning classification models with all large gene expression dataset of cancer.

Silico-tuberculosis amidst COVID-19 pandemic: global scenario and Indian perspective.

Inhalation of crystalline silica-rich dust particles can result in the deadly occupational lung disorder called silicosis. The risk of contracting tuberculosis (TB) and the potential for lung cancer increase due to silicosis. This review article aims to bring to light the state of silicosis and TB scenario in the world and India for evaluating hurdles in the present and future to achieve the elimination road map and assess these conditions in the backdrop of the COVID-19 pandemic. A patient with silicosis has a 2.8-2.9 times higher risk of developing pulmonary TB and 3.7 times that of extrapulmonary TB. Incidences of missed cases when TB was misdiagnosed with silicosis due to indifferent clinical manifestations of the two in the initial stages are not uncommon. The duration of silica exposure and silicosis severity are directly related to the propensity to develop TB. As per a study, an average gap of 7.6 years has been noticed in a South African population for silico-tuberculosis to develop post-silicosis. In a study done on mine workers at Jodhpur, Rajasthan, it was seen that there is no definitive relation between patients with silicosis and the possibility of having COVID-19. There is a significant need to integrate the Silicosis control program with the TB elimination program for the government. A few steps can include assessing the workplaces, periodic monitoring of the workers' health, active case surveillance, identification of hotspots, and introducing reforms to curb the spread of dust and particulate matter from industrialised areas be taken in this regard.

Emerging zoonotic diseases and COVID-19 pandemic: global Perspective and Indian Scenario.

The current coronavirus disease 2019 (COVID-19) pandemic is one example of the scores of zoonotic diseases responsible for various outbreaks resulting in the deaths of millions of people for centuries. The COVID-19 pandemic has broken the age-old healthcare infrastructure and led to utter chaos. In the shadow of this pandemic, other zoonotic infections like the nipah virus, monkeypox, and langya virus, to name a few, have been neglected. Hence, outbreaks caused by such zoonotic viruses are rising in their endemic areas, like the Indian subcontinent. The mortality and morbidity due to such zoonoses are greater than usual due to the shortage of healthcare professionals caused by the COVID-19 crisis. Due to the lack of vaccines and therapeutics directed against this viral infection, treatment of patients is limited to supportive management and prevention, making preparedness for these potential zoonotic viral outbreaks essential. This paper highlights some of these zoonotic infections, which perpetuated and wreaked havoc while the world was occupied with containing the COVID-19 pandemic.

CO-WOA: Novel Optimization Approach for Deep Learning Classification of Fish Image.

The most significant groupings of cold-blooded creatures are the fish family. It is crucial to recognize and categorize the most significant species of fish since various species of seafood diseases and decay exhibit different symptoms. Systems based on enhanced deep learning can replace the area's currently cumbersome and sluggish traditional approaches. Although it seems straightforward, classifying fish images is a complex procedure. In addition, the scientific study of population distribution and geographic patterns is important for advancing the field's present advancements. The goal of the proposed work is to identify the best performing strategy using cutting-edge computer vision, the Chaotic Oppositional Based Whale Optimization Algorithm (CO-WOA), and data mining techniques. Performance comparisons with leading models, such as Convolutional Neural Networks (CNN) and VGG-19, are made to confirm the applicability of the suggested method. The suggested feature extraction approach with Proposed Deep Learning Model was used in the research, yielding accuracy rates of 100 %. The performance was also compared to cutting-edge image processing models with an accuracy of 98.48 %, 98.58 %, 99.04 %, 98.44 %, 99.18 % and 99.63 % such as Convolutional Neural Networks, ResNet150V2, DenseNet, Visual Geometry Group-19, Inception V3, Xception. Using an empirical method leveraging artificial neural networks, the Proposed Deep Learning model was shown to be the best model.

3D US-CT/MRI registration for percutaneous focal liver tumor ablations.

PURPOSE: US-guided percutaneous focal liver tumor ablations have been considered promising curative treatment techniques. To address cases with invisible or poorly visible tumors, registration of 3D US with CT or MRI is a critical step. By taking advantage of deep learning techniques to efficiently detect representative features in both modalities, we aim to develop a 3D US-CT/MRI registration approach for liver tumor ablations. METHODS: Facilitated by our nnUNet-based 3D US vessel segmentation approach, we propose a coarse-to-fine 3D US-CT/MRI image registration pipeline based on the liver vessel surface and centerlines. Then, phantom, healthy volunteer and patient studies are performed to demonstrate the effectiveness of our proposed registration approach. RESULTS: Our nnUNet-based vessel segmentation model achieved a Dice score of 0.69. In healthy volunteer study, 11 out of 12 3D US-MRI image pairs were successfully registered with an overall centerline distance of 4.03±2.68 mm. Two patient cases achieved target registration errors (TRE) of 4.16 mm and 5.22 mm. CONCLUSION: We proposed a coarse-to-fine 3D US-CT/MRI registration pipeline based on nnUNet vessel segmentation models. Experiments based on healthy volunteers and patient trials demonstrated the effectiveness of our registration workflow. Our code and example data are publicly available in this r epository.

Can ventricular 3D ultrasound of neonates with posthemorrhagic hydrocephalus inform on the need for a ventriculoperitoneal shunt?

OBJECTIVE: Some neonates born prematurely with intraventricular hemorrhage develop posthemorrhagic hydrocephalus and require lifelong treatment to divert the flow of CSF. Early prediction of the eventual need for a ventriculoperitoneal shunt (VPS) is difficult, and early discussions with families are based on statistics and the grade of hemorrhage. The authors hypothesize that change in ventricular volume during ventricular taps that is measured with repeated 3D ultrasound (3D US) imaging of the lateral ventricles could be used to assess the risk of the future requirement of a VPS. METHODS: A total of 92 neonates with intraventricular hemorrhage who were treated in the NICU were recruited between April 2012 and November 2019. Only patients who required ventricular taps (VTs) were included in this study, resulting in the analysis of 19 patients with a total of 61 VTs. Among them, 14 patients were treated with a VPS, and in 5 patients the hydrocephalus resolved spontaneously. Parameters studied were total ventricular volume measured with 3D US, ventricular volume change after VT, the ratio between volume reduction and tap amount, the difference between tap amount and volume reduction after tap, the average tap amount, the average number of days between taps, pre-tap head circumference, and reduction in head circumference after tap. RESULTS: Statistically significant differences were found in ventricular volume reduction after tap (p = 0.007), the ratio between volume reduction and tap amount (p = 0.03), the difference between tap amount and volume reduction after tap (p = 0.05), and the interval of days between taps (p = 0.0115). CONCLUSIONS: Measuring with 3D US before and after VT can be a useful tool for quantifying ventricular volume. The findings in this study showed that neonates who experience a large reduction of ventricular volume after VT are more likely to be treated with a shunt than are neonates who experience a small reduction.

Enhancing bioflavor production by solid-state fermentation using Kluyveromyces marxianus and l-phenylalanine.

This study includes the utilization of sweet lemon peel (SLP) and sugarcane bagasse (SB) in solid-state fermentation using Kluyveromyces marxianus for bioflavor compounds production adopting response surface methodology. The major flavor compounds, 2-phenylethanol (2-PE) and 2-phenylethyl acetate (2-PEA) were quantified using gas chromatography-mass spectrometry with and without adding any supplements. Quantification of flavor compounds indicated that without adding any accessory in the substrate, the concentration of 2-PE using SLP and SB was 0.15 ± 0.003 mg/g and 0.14 ± 0.002 mg/g, respectively. Whereas 2-PEA concentration using SLP and SB was observed as 0.01 ± 0.008 mg/g and 0.02 ± 0.001 mg/g, respectively. The addition of l-phenylalanine (l-phe) in the substrates showed 30%-75% enhancement in the production of 2-PE and 2-PEA. The present study indicates that the K. marxianus is a potential microbial cell factory for the production of 2-PE and 2-PEA with the addition of synthetic l-phe having a plethora of applications in food and pharmaceutical industries.