Dose optimization of second window indocyanine green in meningioma patients.

OBJECTIVE: Surgery remains the first line treatment for meningiomas and can benefit from fluorescence-guided surgical techniques such as second-window indocyanine green (SWIG). In the current study, we compared the use of the standard SWIG dose of 5.0 mg/kg relative to 2.5 mg/kg indocyanine green (ICG) in meningioma patients. METHODS: Patients were prospectively enrolled in an IRB-approved study of SWIG and received either the standard dose of 5.0 mg/kg or a reduced dose of 2.5 mg/kg of ICG around 24 h prior to their surgery. Intraoperative near-infrared fluorescence imaging was performed with exo- and endoscopic systems. Signal-to-background ratio (SBR) was calculated to quantify fluorescence and was compared between 5.0 mg/kg and 2.5 mg/kg ICG. All patients received pre-operative MRI and, in select cases, the pre-operative MRI was correlated to intraoperative fluorescence imaging. RESULTS/DISCUSSION: In the current study, we found no significant difference in the SBR of meningiomas in patients that were administered with either 5.0 mg/kg or 2.5 mg/kg ICG. However, in five patients that received the standard-dose SWIG regimen of 5.0 mg/kg ICG we observed dose-related fluorescence quenching - referred to as "inversion" - that interfered with tumor visualization during fluorescence-guided surgery (FGS). When correlated to pre-operative MRI, a similar rim pattern was observed around the primary tumor on T2 FLAIR, which, in retrospect, could be used as a predictor for inversion during FGS in meningioma patients receiving standard-dose ICG. CONCLUSION: This study demonstrated that a reduced ICG dose was as effective as standard-dose SWIG in meningioma patients. We therefore recommend to adjust the standard ICG dose for meningioma patients to 2.5 mg/kg particularly when rim enhancement is observed on pre-operative T2 FLAIR.

Evaluation of Physicochemical Properties of Ipsapirone Derivatives Based on Chromatographic and Chemometric Approaches.

Drug discovery is a challenging process, with many compounds failing to progress due to unmet pharmacokinetic criteria. Lipophilicity is an important physicochemical parameter that affects various pharmacokinetic processes, including absorption, metabolism, and excretion. This study evaluated the lipophilic properties of a library of ipsapirone derivatives that were previously synthesized to affect dopamine and serotonin receptors. Lipophilicity indices were determined using computational and chromatographic approaches. In addition, the affinity to human serum albumin (HSA) and phospholipids was assessed using biomimetic chromatography protocols. Quantitative Structure-Retention Relationship (QSRR) methodologies were used to determine the impact of theoretical descriptors on experimentally determined properties. A multiple linear regression (MLR) model was calculated to identify the most important features, and genetic algorithms (GAs) were used to assist in the selection of features. The resultant models showed commendable predictive accuracy, minimal error, and good concordance correlation coefficient values of 0.876, 0.149, and 0.930 for the validation group, respectively.

Advances in AI-Driven Retention Prediction for Different Chromatographic Techniques: Unraveling the Complexity.

Retention prediction through Artificial intelligence (AI)-based techniques has gained exponential growth due to their abilities to process complex sets of data and ease the crucial task of identification and separation of compounds in most employed chromatographic techniques. Numerous approaches were reported for retention prediction in different chromatographic techniques, and consistent results demonstrated that the accuracy and effectiveness of deep learning models outclassed the linear machine learning models, mainly in liquid and gas chromatography, as ML algorithms use fewer complex data to train and predict information. Support Vector machine-based neural networks were found to be most utilized for the prediction of retention factors of different compounds in thin-layer chromatography. Cheminformatics, chemometrics, and hybrid approaches were also employed for the modeling and were more reliable in retention prediction over conventional models. Quantitative Structure Retention Relationship (QSRR) was also a potential method for predicting retention in different chromatographic techniques and determining the separation method for analytes. These techniques demonstrated the aids of incorporating QSRR with AI-driven techniques acquiring more precise retention predictions. This review aims at recent exploration of different AI-driven approaches employed for retention prediction in different chromatographic techniques, and due to the lack of summarized literature, it also aims at providing a comprehensive literature that will be highly useful for the society of scientists exploring the field of AI in analytical chemistry.

Tryptamine: A privileged scaffold for the management of Alzheimer's disease.

Alzheimer's disease (AD) is a chronic and irreversible neurodegenerative disease associated with aging. It is characterized by the progressive loss of memory and other cognitive functions. Although the exact etiology of AD is not well explored, several factors, such as the deposition of amyloid-ß (Aß) plaques, hyperphosphorylation of tau protein, presence of low levels of acetylcholine, and generation of oxidative stress, are key mediators in the progression of AD. Currently, the clinical treatment options for AD are limited and are based on cholinesterase (ChE) inhibitors (e.g., donepezil, rivastigmine, and galantamine), N-methyl- d-aspartic acid receptor antagonists (e.g., memantine), and the recently approved Aß modulator (e.g., aducanumab). Tryptamine (2-(1H-indol-3-yl)ethan-1-amine) is a small molecule that contains an indole nucleus and an ethylamine side chain. It is also the active metabolite of tryptophan. It possesses a wide range of biological activities related to neurodegenerative disorders, such as ChE inhibition, Aß aggregation inhibition, antioxidant effects, monoamine-oxidase inhibition, and neuroprotection. Several tryptamine-based hybrid analogs are currently being investigated as multifunctional agents for the development of novel hybrids for AD treatment. Thus, this review article aims to provide in-depth insights into the research progress and strategies for designing multifunctional agents used in Alzheimer's therapy.

Carbamate as a potential anti-Alzheimer's pharmacophore: A review.

Alzheimer's disease (AD) is a progressive age-related neurodegenerative brain disorder, which leads to loss of memory and other cognitive dysfunction. The underlying mechanisms of AD pathogenesis are very complex and still not fully explored. Cholinergic neuronal loss, accumulation of amyloid plaque, metal ions dyshomeostasis, tau hyperphosphorylation, oxidative stress, neuroinflammation, and mitochondrial dysfunction are major hallmarks of AD. The current treatment options for AD are acetylcholinesterase inhibitors (donepezil, rivastigmine, and galantamine) and NMDA receptor antagonists (memantine). These FDA-approved drugs mainly provide symptomatic relief without addressing the pathological aspects of disease progression. So, there is an urgent need for novel drug development that not only addresses the basic mechanisms of the disease but also shows the neuroprotective property. Various research groups across the globe are working on the development of multifunctional agents for AD amelioration using different core scaffolds for their design, and carbamate is among them. Rivastigmine was the first carbamate drug investigated for AD management. The carbamate fragment, a core scaffold of rivastigmine, act as a potential inhibitor of acetylcholinesterase. In this review, we summarize the last 10 years of research conducted on the modification of carbamate with different substituents which primarily target ChE inhibition, reduce oxidative stress, and modulate Aß aggregation.

Berberine derivatives as inhibitors of acetylcholinesterase: A systematic review.

Alzheimer's disease (AD) is a chronic age-related neurodegenerative brain disorder characterized by the impairment of memory accompanied by worsening of thinking ability of an individual. The exact pathophysiology of AD is not fully understood. However low level of the neurotransmitter named acetylcholine (ACh), aggregation of Aß peptide into toxic Aß plaque, hyperphosphorylation of tau, bio-metal imbalance, and oxidative stress are the main hallmarks of this disease. Due to the complex pathophysiology of AD, no specific treatment is available in the market, and treatment is only limited to the symptomatic relief. So, there is an urgent need for the development of new drug candidate, which can have disease-modifying effect and improve learning and memory in AD patient. Therefore, berberine-based multifunction compounds with potential cholinesterase inhibitory properties were reviewed in this article. Structure-activity relationship (SAR) and biological activity provide highlights on the new derivatives used for the management of AD.

An Intelligent Bio-Inspired Autonomous Surveillance System Using Underwater Sensor Networks.

Energy efficiency is important for underwater sensor networks. Designing such networks is challenging due to underwater environmental traits that hinder network lifespan extension. Unlike terrestrial protocols, underwater settings require novel protocols due to slower signal propagation. To enhance energy efficiency in underwater sensor networks, ongoing research concentrates on developing innovative solutions. Thus, in this paper, an intelligent bio-inspired autonomous surveillance system using underwater sensor networks is proposed as an efficient method for data communication. The tunicate swarm algorithm is used for the election of the cluster heads by considering different parameters such as energy, distance, and density. Each layer has several clusters, each of which is led by a cluster head that continuously rotates in response to the fitness values of the SNs using the tunicate swarm algorithm. The performance of the proposed protocol is compared with existing methods such as EE-LHCR, EE-DBR, and DBR, and results show the network's lifespan is improved by the proposed work. Due to the effective fitness parameters during cluster head elections, our suggested protocol may more effectively achieve energy balance, resulting in a longer network lifespan.

Rationalizing mineral gypsum use through microbially enriched municipal solid waste compost for amelioration and regaining productivity potential of degraded alkali soils.

Reclamation of alkali soils to harness their productivity potential is more complex due to the presence of excess sodium ions, poor hydraulic conductivity and infiltration rate, resulting in poor plant growth and crop productivity. Sodic soil reclamation using inorganic ameliorants like mineral gypsum or phosphogypsum is beyond the reach of small and marginal farmers having alkali soils because of their higher market prices and shortage of availability. Conjoint use of inorganic and organic amendments can be a pragmatic solution for improving soil physico-chemical and biological properties and sustaining crop productivity. Municipal solid waste compost (MSWC) available in abundant quantity if enriched with the efficient halophilic microbial consortium and used in conjunction with a reduced dose of gypsum can be a cost-effective approach for sustainable reclamation of alkali soils and harnessing their productivity potential. Hence, a field experiment was conducted on a high alkali soil (pH2 9.2 ± 0.10), electrical conductivity (EC) 1.14 ± 0.12 dS m-1, exchangeable sodium percentage 48 ± 2.50 and organic carbon (0.30%) was conducted during 2018-19 to 2020-21 to study the combined effect inorganic and organic (enriched municipal solid waste compost (EMSWC)) amendments on amelioration of alkali soils and sustaining productivity of rice-wheat cropping system. Application of gypsum @ 25% GR + enriched MSW compost @ 10 t ha-1 (T6) showed significant improvement in soil physico-chemical and biological properties over the sole application of organic (T3 and T4), inorganic (T2) and control (T1). A significant improvement in soil fertility status in terms of available nitrogen and micronutrients as well as CO3, HCO3, Cl, Ca and Mg content were recorded with the combined application of organic and inorganic soil amendments (T5 and T6) over the sole application of mineral gypsum. Soil microbial biomass carbon (MBC), nitrogen (MBN) and phosphorus (MBP) improved significantly due to the application of EMSWC with gypsum over the application of gypsum only. Grain yield of rice and wheat increased significantly (P < 0.05) owing to the application of a reduced dose of gypsum (25% GR) and EMSWC @ 10 t ha-1 (T6) with values of 5.55 and 3.83 t ha-1, respectively over rest of the treatments. Three years economic analysis of the study revealed that treatments T6 and T5 gave the highest positive net return whereas it was lowest in treatment T1 and negative in treatment T2. The highest benefit-to-cost ratio (B:C) was obtained in treatments T6 and T5 which were significantly higher compared to the rest of the treatments.

Prevalence of bovine brucellosis in India: a meta-analysis.

BACKGROUND: Bovine brucellosis is a highly contagious zoonotic disease that hinders production and is a vital public health concern. Even though brucellosis is one of the important diseases in India, the exact prevalence details of the disease are not known. OBJECTIVE: To derive an estimate of the prevalence of brucellosis in India. MATERIAL AND METHODS: A systematic review and meta-analysis were carried out by using PRISMA and MOOSE protocols. A total of 133 studies were taken from online sources and various publications. Among these, 69 studies were incorporated that include a total of 140908 bovines. The data were compiled from 1990 to 2019 around India. RESULTS: Pooled estimates of the prevalence of brucellosis in cattle and buffaloes were 16.6% (95% CI: 13.0, 21.1) and 14.2% (95% CI: 8.9, 21.8), respectively and in bovines was 15.1% (95% CI: 12.0, 18.8). The meta-analysis revealed that there was significant heterogeneity between the published studies. CONCLUSION: As the prevalence of bovine brucellosis in India is not known hence the present study will provide the knowledge on prevalence and epidemiology of bovine brucellosis in India and will be helpful for the government to make policy plans to control this disease in India.

Current trends in chromatographic prediction using artificial intelligence and machine learning.

Artificial intelligence (AI) and machine learning (ML) gained tremendous growth and are rapidly becoming popular in various fields of prediction due to their potential abilities, accuracy, and speed. Machine learning algorithms employ historical data to analyze or predict information using patterns or trends. AI and ML were most employed in chromatographic predictions and particularly attractive options for liquid chromatography method development, as they can help achieve desired results faster, more accurately, and more efficiently. This review aims at exploring various AI and ML models employed in the determination of chromatographic characteristics. This review also aims to provide deep insight into reported artificial neural network (ANN) associated techniques which maintained better accuracy and significant possibilities for chromatographic characteristics prediction in liquid chromatography over classical linear models and also emphasizes the integration of a fuzzy system with an ANN, as this integrated study provides more efficient and accurate methods in chromatographic prediction than other linear models. This study also focuses on the retention prediction of a target molecule employing QSRR methodology combined with an ANN, highlighting a more effective technique than the QSRR alone. This approach showed the benefits of combining AI or ML algorithms with the QSRR to obtain more accurate retention predictions, emphasizing the potential of artificial intelligence and machine learning for overcoming adversities in analytical chemistry.

Myocardial Infarction as a Consequence of Mitochondrial Dysfunction.

Acute myocardial infarction is an event of myocardial necrosis caused by unstable ischemic syndrome. Myocardial infarction (MI) occurs when blood stops flowing to the cardiac tissue or myocardium and the heart muscle gets damaged due to poor perfusion and reduced oxygen supply. Mitochondria can serve as the arbiter of cell fate in response to stress. Oxidative metabolism is the function of mitochondria within the cell. Cardiac cells being highly oxidative tissue generates about 90% of their energy through oxidative metabolism. In this review, we focused on the role of mitochondria in energy generation in myocytes as well as its consequences on heart cells causing cell damage. The role of mitochondrial dysfunction due to oxidative stress, production of reactive oxygen species, and anaerobic production of lactate as a failure of oxidative metabolism are also discussed.

Aortic stenosis: a review on acquired pathogenesis and ominous combination with diabetes mellitus.

BACKGROUND: Aortic stenosis (AS) is a progressive disease, with no pharmacological treatment. The prevalence of diabetes mellitus (DM) among AS patients is higher than in the general population. DM significantly increases the risk of AS development and progression from mild to severe. The interplay between AS and DM's mechanism is not entirely known yet. MAIN BODY: The increased accumulation of advanced glycation end products (AGEs) was linked to increased valvular oxidative stress, inflammation, expression of coagulation factors, and signs of calcification, according to an analysis of aortic stenotic valves. It is interesting to note that in diabetic AS patients, valvular inflammation did not correlate with serum glucose levels but rather only with long-term glycemic management markers like glycated haemoglobin and fructosamine. Transcatheter aortic valve replacement, which has been shown to be safer than surgical aortic valve replacement, is advantageous for AS patients who also have concurrent diabetes. Additionally, novel anti-diabetic medications have been proposed to lower the risk of AS development in DM patients, including sodium-glucose cotransporter-2 inhibitors and glucagon-like peptide-1 receptor agonist that target reduction of AGEs-mediated oxidative stress. CONCLUSIONS: There are little data on the effects of hyperglycemia on valvular calcification, but understanding the interactions between them is essential to develop a successful treatment strategy to stop or at least slow the progression of AS in DM patients. There is a link among AS and DM and that DM negatively impacts the quality of life and longevity of AS patients. The sole successful treatment, despite ongoing efforts to find new therapeutic modalities, involves aortic valve replacement. More research is required to find methods that can slow the advancement of these conditions, enhancing the prognosis and course of people with AS and DM.

Conservation Tillage and Waste Crop Residue Based Zinc Fortification in Rice and Wheat under Reclaimed Sodic Soils: Progress toward Nutrient Circularity and Sustainability.

Nearly 50% of the population across the globe is at risk of malnutrition with respect to zinc (Zn) in areas where a cereal based dietary system dominates. The present study estimated daily Zn intake in humans through field experiments in reclaimed sodic soil, utilizing waste crop residue (CR) in conservation tillage where CR played a vital role in enhancing Zn uptake in rice and wheat grains. Zn dynamics, its bioavailability, interaction with soil properties, and plausible contribution in dietary intake were studied extensively to supplement the research. A higher mobility factor (2.70%) and plant available Zn resulted in its higher uptake in rice (58.2 mg kg-1) and wheat (67.2 mg kg-1) under zero tillage in rice followed by zero tillage in wheat where CR was retained on the surface (ZTR-ZTW+CR). Daily Zn intake was found to be maximum (0.651 mg kg-1 day-1) under ZTR-ZTW+CR, demonstrating zinc sufficiency. Thus, this study may help in formulating actionable policies for combating both nutritional security and environmental hazards due to CR burning.

Artificial Intelligence Based Approach for Classification of Human Activities Using MEMS Sensors Data.

The integration of Micro Electronic Mechanical Systems (MEMS) sensor technology in smartphones has greatly improved the capability for Human Activity Recognition (HAR). By utilizing Machine Learning (ML) techniques and data from these sensors, various human motion activities can be classified. This study performed experiments and compiled a large dataset of nine daily activities, including Laying Down, Stationary, Walking, Brisk Walking, Running, Stairs-Up, Stairs-Down, Squatting, and Cycling. Several ML models, such as Decision Tree Classifier, Random Forest Classifier, K Neighbors Classifier, Multinomial Logistic Regression, Gaussian Naive Bayes, and Support Vector Machine, were trained on sensor data collected from accelerometer, gyroscope, and magnetometer embedded in smartphones and wearable devices. The highest test accuracy of 95% was achieved using the random forest algorithm. Additionally, a custom-built Bidirectional Long-Short-Term Memory (Bi-LSTM) model, a type of Recurrent Neural Network (RNN), was proposed and yielded an improved test accuracy of 98.1%. This approach differs from traditional algorithmic-based human activity detection used in current wearable technologies, resulting in improved accuracy.

Prevalence of babesiosis in bovines of India: a meta-analytical approach for 30 years (1990-2019).

BACKGROUND: India has a massive population of bovines, which makes the framework of the economy mainly relying on milk and meat production. Parasitic diseases such as babesiosis are detrimental to bovines by decreasing animal welfare and production efficiency. AIM: Performing a meta-analysis of the prevalence of babesiosis over 30 years viz 1990 to 2019 within India to pool out individual studies from different country regions. MATERIAL AND METHODS: The studies were reviewed thoroughly to assess the quality, and it was done by following the preferred reporting items for systematic review and meta-analysis (PRISMA) and MOOSE protocols. The prevalence of babesiosis in cattle and buffaloes was calculated using meta-analysis tools using R-software and Q Statistics. RESULTS: The systematic review and meta-analysis performed on 47 studies among bovine, 48 studies among cattle, and 13 studies among buffaloes revealed the (pooled) prevalence of babesiosis in India as 10.9% (6.3%-18.2%; Q = 5132.03, d.f. = 46, P < 0.001), 11.9% (6.9%-19.8%; Q = 5060.2, d.f.=47, P < 0.001), and 6.0% (2.6%-13.2%; Q = 500.55, d.f.=12, P < 0.001), respectively, which provides a rather exact scenario of the prevalence of this haemoparasitic disease across the country. In addition, cattle were having higher risk of babesiosis than buffalo. CONCLUSION: The findings from the meta-analysis showed that the disease is prevalent across the country, and that bovines are highly affected by it. CLINICAL RELEVANCE: Appropriate prevention and control measures should be taken to mitigate this disease and enhance welfare and production performances of bovines.

A Lightweight Deep Learning-Based Approach for Jazz Music Generation in MIDI Format.

In today's real-world, estimation of the level of difficulty of the musical is part of very meaningful musical learning. A musical learner cannot learn without a defined precise estimation. This problem is not very basic but it is complicated up to some extent because of the subjectivity of the contents and the scarcity of the data. In this paper, a lightweight model that generates original music content using deep learning along with generating music based on a specific genre is proposed. The paper discusses a lightweight deep learning-based approach for jazz music generation in MIDI format. In this work, the genre of music chosen is Jazz, and the songs selected are classical numbers composed by various artists. All the songs are in MIDI format and there might be differences in the pace or tone of the music. It is prudential to make sure that the chosen datasets that do not have these kinds of differences and are similar to the final output as desired. A model is trained to take in a part of a music file as input and should produce its continuation. The result generated should be similar to the dataset given as the input. Moreover, the proposed model also generates music using a particular instrument.

MEUF for removal and recovery of valuable organic components present in effluents: A process intensified technology.

In recent years, the domain of the research space in novel separation process has been led by membrane systems as a panacea providing multifarious benefits of high separation efficiency, elimination of extreme process conditions, sustainability, and environment friendliness coupled with high operational flexibility. In this niche area, often, ultrafiltration is touted as a robust separation technique due to its high separation efficiency, membrane stability, and lower operating costs. The only drawback of relatively large pore size can be overcome by combining surfactant addition, leading to development of integrated processes termed as Micellar Enhanced Ultrafiltration. MEUF processes isolate and selectively separate valuable organics present in effluent streams. The process characteristics fit the bill as a typified example for process intensification Technology interventions for recycling of surfactants can enhance the cost-competitiveness of the process. This has the potential to develop into a broad-spectrum effluent treatment option with a change of surfactants for target contaminants. Here, in this review, we attempt to critically examine the unique features of this technology, development of spin-offs with wide-ranging applications. Specifically applications in removal of hazardous, and persistent components like dissolved organics have been critically studied. The focus was to highlight the crux of the novel technologies highlighting the efficacy and the underlying concept of process intensification. PRACTITIONER POINTS: Role of MEUF as a sustainable process intensifying separation technique for removal and recovery of organics. Novel process development using MEUF. Comparative performance analysis to assess efficacy. Discussions on future integrative process development. Sustainability aspect of MEUF with possibility of byproduct recovery.

A Machine Learning Model for Early Prediction and Detection of Sepsis in Intensive Care Unit Patients.

In today's scenario, sepsis is impacting millions of patients in the intensive care unit due to the fact that the mortality rate is increased exponentially and has become a major challenge in the field of healthcare. Such peoples require determinant care which increases the cost of the treatment by using a large number of resources because of the nonavailability of the resources. The treatment of sepsis is available in the early state, but treatment is not started at the right time, and then it converts to the advanced level of sepsis and increases the fatalities. Thus, an intensive analysis is required to detect and identify sepsis at the early stage. There are some models available that work based on the manual score and based on only the biomark features, but these are not fully automated. Some machine learning-based models are also available, which can reduce the mortality rate, but accuracy is not up to date. This paper proposes a machine learning model for early detecting and predicting sepsis in intensive care unit patients. Various models, random forest (RF), linear regression (LR), support vector machine (SVM), naive Bayes (NB), ensemble (of SVM, RF, NB, and LR), XGBoost, and proposed ensemble (of SVM, RF, NB, LR, and XGBoost), are simulated by using the collected data from intensive care unit patient's database that is based on the clinical laboratory values and vital signs. The performance of the models is evaluated by considering the same datasets. The balanced accuracy of RF, LR, SVM, NB, ensemble (of SVM, RF, NB, and LR), XGBoost, and proposed ensemble (of SVM, RF, NB, LR, and XGBoost) is 0.90, 0.73, 0.93, 0.74, 0.94, 0.95, and 0.96, respectively. It is also evident from the experimental results that the proposed ensemble model performs well as compared to the other models.

An Optimized Artificial Intelligence System Using IoT Biosensors Networking for Healthcare Problems.

In today's environment, electronics technology is growing rapidly because of the availability of the numerous and latest devices which can be deployed for monitoring and controlling the various healthcare systems. Due to the limitations of such devices, there is a dire need to optimize the utilization of the devices. In healthcare systems, Internet of things (IoT) based biosensors networking has minimal energy during transmission and collecting data. This paper proposes an optimized artificial intelligence system using IoT biosensors networking for healthcare problems for efficient data collection from the deployed sensor nodes. Here, an optimized tunicate swarm algorithm is used for optimizing the route for data collection and transmission among the patient and doctor. The fitness function of the optimized tunicate swarm algorithm used the distance, proximity, residual, and average energy of nodes parameters. The proposed method is attributed to the optimal CH chosen under TSA operation having a lower energy consumption. The performance of the proposed method is compared to the existing methods in terms of various metrics like stability period, lifetime, throughput, and clusters per round.

Assessment and Comparison of Three Dimensional Exoscopes for Near-Infrared Fluorescence-Guided Surgery Using Second-Window Indocyanine-Green.

OBJECTIVE: Compared to microscopes, exoscopes have advantages in field-depth, ergonomics, and educational value. Exoscopes are especially well-poised for adaptation into fluorescence-guided surgery (FGS) due to their excitation source, light path, and image processing capabilities. We evaluated the feasibility of near-infrared FGS using a 3-dimensional (3D), 4 K exoscope with nearinfrared fluorescence imaging capability. We then compared it to the most sensitive, commercially-available near-infrared exoscope system (3D and 960 p). In-vitro and intraoperative comparisons were performed. METHODS: Serial dilutions of indocyanine-green (1-2000 µg/mL) were imaged with the 3D, 4 K Olympus Orbeye (system 1) and the 3D, 960 p VisionSense Iridium (system 2). Near-infrared sensitivity was calculated using signal-to-background ratios (SBRs). In addition, three patients with brain tumors were administered indocyanine-green and imaged with system 1, with two also imaged with system 2 for comparison. RESULTS: Systems 1 and 2 detected near-infrared fluorescence from indocyanine green concentrations of >250 µg/L and >31.3 µg/L, respectively. Intraoperatively, system 1 visualized strong near-infrared fluorescence from two, strongly gadoliniumenhancing meningiomas (SBR=2.4, 1.7). The high-resolution, bright images were sufficient for the surgeon to appreciate the underlying anatomy in the near-infrared mode. However, system 1 was not able to visualize fluorescence from a weakly-enhancing intraparenchymal metastasis. In contrast, system 2 successfully visualized both the meningioma and the metastasis but lacked high resolution stereopsis. CONCLUSION: Three-dimensional exoscope systems provide an alternative visualization platform for both standard microsurgery and near-infrared fluorescent guided surgery. However, when tumor fluorescence is weak (i.e., low fluorophore uptake, deep tumors), highly sensitive near-infrared visualization systems may be required.