A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentration.

Suspended sediment concentration prediction is critical for the design of reservoirs, dams, rivers ecosystems, various operations of aquatic resource structure, environmental safety, and water management. In this study, two different machine models, namely the cascade correlation neural network (CCNN) and feedforward neural network (FFNN) were applied to predict daily-suspended sediment concentration (SSC) at Simga and Jondhara stations in Sheonath basin, India. Daily-suspended sediment concentration and discharge data from 2010 to 2015 were collected and used to develop the model to predict suspended sediment concentration. The developed models were evaluated using statistical indices like Nash and Sutcliffe efficiency coefficient (NES), root mean square error (RMSE), Willmott's index of agreement (WI), and Legates-McCabe's index (LM), supplemented by a scatter plot, density plots, histograms and Taylor diagram for graphical representation. The developed model was evaluated and compared with CCNN and FFNN. Nine input combinations were explored using different lag-times for discharge (Qt-n) and suspended sediment concentration (St-n) as input variables, with the current suspended sediment concentration as the desired output, to develop CCNN and FFNN models. The CCNN4 model with 4 lagged inputs (St-1, St-2, St-3, St-4) outperformed the other developed models with the lowest RMSE = 95.02 mg/l and the highest NES = 0.0.662, WI = 0.890 and LM = 0.668 for the Jondhara Station while the same CCNN4 model secure as the best with the lowest RMSE = 53.71 mg/l and the highest NES = 0.785, WI = 0.936 and LM = 0.788 for the Simga Station. The result shows the CCNN model was better than the FFNN model for predicting daily-suspended sediment at both stations in the Sheonath basin, India. Overall, CCNN showed better forecasting potential for suspended sediment concentration compared to FFNN at both stations, demonstrating their applicability for hydrological forecasting with complex relationships.

Extracellular vesicles-powered immunotherapy: Unleashing the potential for safer and more effective cancer treatment.

Cancer treatment has seen significant advancements with the introduction of Onco-immunotherapies (OIMTs). Although some of these therapies have received approval for use, others are either undergoing testing or are still in the early stages of development. Challenges persist in making immunotherapy widely applicable to cancer treatment. To maximize the benefits of immunotherapy and minimize potential side effects, it's essential to improve response rates across different immunotherapy methods. A promising development in this area is the use of extracellular vesicles (EVs) as novel delivery systems. These small vesicles can effectively deliver immunotherapies, enhancing their effectiveness and reducing harmful side effects. This article discusses the importance of integrating nanomedicines into OIMTs, highlighting the challenges with current anti-OIMT methods. It also explores key considerations for designing nanomedicines tailored for OIMTs, aiming to improve upon existing immunotherapy techniques. Additionally, the article looks into innovative approaches like biomimicry and the use of natural biomaterial-based nanocarriers (NCs). These advancements have the potential to transform the delivery of immunotherapy. Lastly, the article addresses the challenges of moving OIMTs from theory to clinical practice, providing insights into the future of using advanced nanotechnology in cancer treatment.

Carbon nanotubes in plant dynamics: Unravelling multifaceted roles and phytotoxic implications.

Carbon nanotubes (CNTs) have emerged as a promising frontier in plant science owing to their unique physicochemical properties and versatile applications. CNTs enhance stress tolerance by improving water dynamics and nutrient uptake and activating defence mechanisms against abiotic and biotic stresses. They can be taken up by roots and translocated within the plant, impacting water retention, nutrient assimilation, and photosynthesis. CNTs have shown promise in modulating plant-microbe interactions, influencing symbiotic relationships and mitigating the detrimental effects of phytopathogens. CNTs have demonstrated the ability to modulate gene expression in plants, offering a powerful tool for targeted genetic modifications. The integration of CNTs as sensing elements in plants has opened new avenues for real-time monitoring of environmental conditions and early detection of stress-induced changes. In the realm of agrochemicals, CNTs have been explored for their potential as carriers for targeted delivery of nutrients, pesticides, and other bioactive compounds. CNTs have the potential to demonstrate phytotoxic effects, detrimentally influencing both the growth and developmental processes of plants. Phytotoxicity is characterized by induction of oxidative stress, impairment of cellular integrity, disruption of photosynthetic processes, perturbation of nutrient homeostasis, and alterations in gene expression. This review aims to provide a comprehensive overview of the current state of knowledge regarding the multifaceted roles of CNTs in plant physiology, emphasizing their potential applications and addressing the existing challenges in translating this knowledge into sustainable agricultural practices.

Evaluate effect of 126 pre-processing methods on various artificial intelligence models accuracy versus normal mode to predict groundwater level (case study: Hamedan-Bahar Plain, Iran).

The estimation of groundwater levels is crucial and an important step in ensuring sustainable management of water resources. In this paper, selected piezometers of the Hamedan-Bahar plain located in west of Iran. The main objective of this study is to compare effect of various pre-processing methods on input data for different artificial intelligence (AI) models to predict groundwater levels (GWLs). The observed GWL, evaporation, precipitation, and temperature were used as input variables in the AI algorithms. Firstly, 126 method of data pre-processing was done by python programming which are classified into three classes: 1- statistical methods, 2- wavelet transform methods and 3- decomposition methods; later, various pre-processed data used by four types of widely used AI models with different kernels, which includes: Support Vector Machine (SVR), Artificial Neural Network (ANN), Long-Short Term memory (LSTM), and Pelican Optimization Algorithm (POA) - Artificial Neural Network (POA-ANN) are classified into three classes: 1- machine learning (SVR and ANN), 2- deep learning (LSTM) and 3- hybrid-ML (POA-ANN) models, to predict groundwater levels (GWLs). Akaike Information Criterion (AIC) were used to evaluate and validate the predictive accuracy of algorithms. According to the results, based on summation (train and test phases) of AIC value of 1778 models, average of AIC values for ML, DL, hybrid-ML classes, was decreased to -25.3%, -29.6% and -57.8%, respectively. Therefore, the results showed that all data pre-processing methods do not lead to improvement of prediction accuracy, and they should be selected very carefully by trial and error. In conclusion, wavelet-ANN model with daubechies 13 and 25 neurons (db13_ANN_25) is the best model to predict GWL that has -204.9 value for AIC which has grown by 5.23% (-194.7) compared to the state without any pre-processing method (ANN_Relu_25).

Exosome-Based Macromolecular neurotherapeutic drug delivery approaches in overcoming the Blood-Brain barrier for treating brain disorders.

Delivering drugs to the brain is a complex challenge in medical research, particularly for disorders like Alzheimer's and Parkinson's. The blood-brain barrier restricts the entry of many therapeutic molecules, hindering their effectiveness. Nanoparticles, a potential solution, face issues like toxicity and limited approvals. A new avenue explores the use of small extracellular vesicles (sEVs), i.e., exosomes, as natural carriers for drug delivery. sEVs, tiny structures below 150 nm, show promise due to their minimal immune response and ability to precisely deliver drugs. This review focuses on the potential of sEVs-based drug delivery systems for treating neurological disorders, brain cancers, and other brain-related issues. Notably, bioengineered sEVs-carrying therapeutic compounds exhibit promise in early studies. The unique features of sEVs, such as their small size and natural properties, position them as candidates to overcome challenges in drug delivery to the brain. Ongoing clinical trials and research into sEVs behavior within the body further highlight their potential for revolutionizing drug delivery and addressing complex brain conditions.

Development and evaluation of a pH-responsive Mimosa pudica seed mucilage/ß- cyclodextrin-co-poly(methacrylate) hydrogel for controlled drug delivery: In vitro and in vivo assessment.

In this comprehensive investigation, a novel pH-responsive hydrogel system comprising mimosa seed mucilage (MSM), ß-cyclodextrin (ß-CD), and methacrylic acid (MAA) was developed via free radical polymerization technique to promote controlled drug delivery. The hydrogel synthesis involved strategic variations in polymer, monomer, and crosslinker content in fine-tuning its drug-release properties. The resultant hydrogel exhibited remarkable pH sensitivity, selectively liberating the model drug (Capecitabine = CAP) under basic conditions while significantly reducing release in an acidic environment. Morphological, thermal, and structural analyses proved that CAP has a porous texture, high stability, and an amorphous nature. In vitro drug release experiments showcased a sustained and controlled release profile. Optimum release (85.33 %) results were recorded over 24 h at pH 7.4 in the case of MMB9. Pharmacokinetic evaluation in healthy male rabbits confirmed bioavailability enhancement and sustained release capabilities. Furthermore, rigorous toxicity evaluations and histopathological analyses ensured the safety and biocompatibility of the hydrogel. This pH-triggered drug delivery system can be a promising carrier system for drugs involving frequent administrations.

Transforming Pharmaceutical Synthesis with Sein-E-B Nanocomposite Photocatalyst through 1,4-NAD(P)H Cofactor Regeneration and C-N Bond Activation.

The need for sunlight chemical renewal and contemporary organic transformation has fostered the advancement of environmentally friendly photocatalytic techniques. For the first time, we report on the novel crafting of a bright future with selenium-infused Eosin-B (Sein-E-B) nanocomposite photocatalysts in this work. The Sein-E-B nanocomposite materials were created using a hydrothermal process for solar chemical regeneration and organic transformation under visible light. The synthesized samples were subjected to UV-DRS-visible spectroscopy, FT-IR, SEM, EDX, EIS and XRD analysis. The energy band gap of the Sein-E-B nanocomposite photocatalyst was measured using UV-DRS, and the result was around 2.06 eV. to investigate the generated Sein-E-B catalytic activity as a nanocomposite for 1,4-NADH/NADPH re-formation and C-N bond activation. This novel photocatalyst offers a promising alternative for the regeneration of solar chemicals and C-N bond creation between pyrrole and aryl halides.

Advancing Diabetes Management: The Future of Enzyme-Less Nanoparticle-Based Glucose Sensors-A Review.

BACKGROUND: Glucose is vital for biological processes, requiring blood sugar levels to be maintained between 3.88 and 6.1 mmol/L, especially during fasting. Elevated levels signal diabetes, a global concern affecting 537 million people, necessitating effective glucose-monitoring devices. METHOD: Enzyme-based sensors, though selective, are sensitive to environmental factors. Nonenzymatic sensors, especially those with nanoparticles, offer stability, high surface area, and cost-effectiveness. Existing literature supports their immediate glucose oxidation, showcasing exceptional sensitivity. RESULTS: This review details nonenzymatic sensors, highlighting materials, detection limits, and the promise of nanoparticle-based designs, which exhibit enhanced sensitivity and selectivity in glucose detection. CONCLUSION: Nanoparticle-based sensors, as reviewed, show potential for glucose monitoring, overcoming enzyme-based limitations. The conclusion suggests future directions for advancing these sensors, emphasizing ongoing innovation in this critical research area.

Chylolymphatic mesenteric cyst: A rare differential diagnosis of cystic mass in abdomen: A case report.

INTRODUCTION: Mesenteric cysts are the rare benign intraperitoneal tumor, which are common in the mesentery of the terminal ileum. Though mesenteric cysts are frequently seen, chylolymphatic mesenteric cysts in children are rare entities. CASE PRESENTATION: Our case is a case of a 3-year-old female child presenting to our center with a complain of abdominal pain. Ultrasonography (USG) of the abdomen revealed a cystic lesion in the right upper abdomen, and computed tomography (CT) scan showed a large homogenous fluid density lesion in the right upper abdomen, giving a provisional diagnosis of mesenteric cyst. She was managed surgically: the cyst was excised, the involved segment of bowel was resected and the remaining was anastomosed. On histopathology, the final diagnosis of a chylolymphatic mesenteric cyst was made. DISCUSSION: Clinical presentations may vary from asymptomatic abdominal lump to features of acute abdomen. Imaging modalities like ultrasonography and computed tomography scans are the mainstay of investigations. Surgical management with excision of cyst, and resection of the involved bowel segment is the treatment of choice, as done in our patient. The final diagnosis is made after the histopathologic examination of the excised specimen. CONCLUSION: Chylolymphatic mesenteric cysts are rare, and have varied clinical presentations. Although rare, chylolymphatic mesenteric cysts should be considered a differential diagnosis in patients presenting with cystic masses in the abdomen.

A rare case of schwannomatosis with meningioma: a case report.

Introduction: Schwannomatosis is characterized by multiple schwannomas without vestibular schwannomas or any other stigmata of neurofibromatosis type 2 (NF2). Schwannomatosis is a rare disorder, with a reported incidence ranging from 1 in 40 000 to 1 in 1.7 million. Meningioma is also associated with schwannomatosis in around 5% of cases. Case presentation: We describe a case of a 20-year-old female presenting with progressive weakness of the right lower limb for 7 months with a tingling sensation and numbness of the same limb for 6 months and was found to have schwannomatosis with multiple spinal and right cerebellopontine angle (CPA) (9th/10th cranial nerve) schwannomas and left anterior cranial fossa meningioma. Discussion: Schwannomas in schwannomatosis are seen along the cranial, spinal, and peripheral nerves but not along the vestibular nerve, as is characteristically seen in NF2. The occurrence of meningiomas is about 5% in individuals with schwannomatosis, and the patient in our case also had an associated meningioma. The tumor was confirmed to be a schwannoma based on features on an MRI examination and histological examination. Conclusion: It is of great significance to identify the entire spectrum of the disease in a patient with schwannomatosis, and to differentiate it from related conditions in order to track and surgically manage the patient appropriately based on symptomatology and imaging findings.

Unraveling the impact of digital transformation on green innovation through microdata and machine learning.

How to use digitalization to support the green transformation of organizations has drawn much attention based on the rapid development of digitalization. However, digital transformation (DT) may be hindered by the "IT productivity paradox." Exploring the influence of DT on green innovation, we analyze panel data encompassing A-share listed companies in Shanghai and Shenzhen spanning the period from 2010 to 2018. It tests the DT's non-linear impact, employing a random-forest and mediation effect models. The results reveal that (i) DT can promote green innovation; (ii) regarding heterogeneity, the promotion effect is mainly manifested in enterprises in non-state-owned and highly competitive industries; (iii) based on mechanism testing, DT relies on two routes to encourage green innovation: improving environmental information disclosure and reducing environmental uncertainty; and (iv) random-forest analysis shows that DT exhibits an inverted U-shaped non-linear effect on green innovation, including the "IT productivity paradox." This study enhances the existing discourse on DT and green innovation by furnishing empirical substantiation for the non-linear influence exerted by DT on green innovation. Furthermore, it imparts insights into the mechanisms and contextual limitations governing this association.

Targeting Pathways and Integrated Approaches to Treat Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a chronic symmetrical systemic disorder that not only affects joints but also other organs such as heart, lungs, kidney, and liver. Approximately there is 0.5%-1% of the total population affected by RA. RA pathogenesis still remains unclear due to which its appropriate treatment is a challenge. Further, multitudes of factors have been reported to affect its progression i.e. genetic factor, environmental factor, immune factor, and oxidative factor. Therapeutic approaches available for the treatment of RA include NSAIDs, DMARDs, enzymatic, hormonal, and gene therapies. But most of them provide the symptomatic relief without treating the core of the disease. This makes it obligatory to explore and reach the molecular targets for cure and long-term relief from RA. Herein, we attempt to provide extensive overlay of the new targets for RA treatment such as signaling pathways, proteins, and receptors affecting the progression of the disease and its severity. Precise modification in these targets such as suppressing the notch signaling pathway, SIRT 3 protein, Sphingosine-1-phosphate receptor and stimulating the neuronal signals particularly efferent vagus nerve and SIRT 1 protein may offer long term relief and potentially diminish the chronicity. To target or alter the novel molecules and signaling pathway a specific delivery system is required such as liposome, nanoparticles and micelles and many more. Present review paper discusses in detail about novel targets and delivery systems for treating RA.

Exploring nanocarriers as innovative materials for advanced drug delivery strategies in onco-immunotherapies.

In recent years, Onco-immunotherapies (OIMTs) have been shown to be a potential therapy option for cancer. Several immunotherapies have received regulatory approval, while many others are now undergoing clinical testing or are in the early stages of development. Despite this progress, a large number of challenges to the broad use of immunotherapies to treat cancer persists. To make immunotherapy more useful as a treatment while reducing its potentially harmful side effects, we need to know more about how to improve response rates to different types of immunotherapies. Nanocarriers (NCs) have the potential to harness immunotherapies efficiently, enhance the efficiency of these treatments, and reduce the severe adverse reactions that are associated with them. This article discusses the necessity to incorporate nanomedicines in OIMTs and the challenges we confront with current anti-OIMT approaches. In addition, it examines the most important considerations for building nanomedicines for OIMT, which may improve upon current immunotherapy methods. Finally, it highlights the applications and future scenarios of using nanotechnology.

Isolated pancreatic injury following blunt trauma in a seven-year-old girl: A case report.

INTRODUCTION AND IMPORTANCE: Isolated pancreatic injury following blunt abdominal trauma is rare, and the absence of definite clinical signs and symptoms may delay diagnosis and management. CASE PRESENTATION: We present the case of a seven-year-old girl with a history of progressive, periumbilical abdominal pain and multiple episodes of non-bilious, non-blood mixed vomiting following a road traffic accident. Computed tomography (CT) imaging revealed an isolated grade three pancreatic injury managed conservatively. CLINICAL DISCUSSION: The patient usually presents with epigastric tenderness with or without a rise in pancreatic enzyme levels. CT grading of injury correlates with and guides the management approach, and CT abdomen with contrast is the preferred investigation for diagnosis. Management depends on the grade of injury, the hemodynamic status of the children, and the co-existence of other injuries. CONCLUSION: Isolated pancreatic injury, although rare, can go undiagnosed due to a lack of suspicion from the treating surgeon and may have dire consequences. Conservative management can be a suitable strategy for children with stable hemodynamic status.

Comprehensive Investigation of Cu2+ Adsorption from Wastewater Using Olive-Waste-Derived Adsorbents: Experimental and Molecular Insights.

This study investigates the efficacy of adsorbents from locally sourced olive waste-encompassing olive skins, leaves, and pits, recovered from the initial centrifugation of olives (OWP)-and a composite with sodium alginate (OWPSA) for the removal of Cu2+ ions from synthetic wastewater. Experimental analyses conducted at room temperature, with an initial Cu2+ concentration of 50 mg/L and a solid/liquid ratio of 1 g/L, showed that the removal efficiencies were approximately 79.54% and 94.54% for OWP and OWPSA, respectively, highlighting the positive impact of alginate on adsorption capacity. Utilizing statistical physics isotherm models, particularly the single-layer model coupled to real gas (SLMRG), allowed us to robustly fit the experimental data, providing insights into the adsorption mechanisms. Thermodynamic parameters affirmed the spontaneity and endothermic nature of the processes. Adsorption kinetics were interpreted effectively using the pseudo-second-order (PSO) model. Molecular modeling investigations, including the conductor-like screening model for real solvents (COSMO-RS), density functional theory (DFT), and atom-in-molecule (AIM) analysis, unveiled intricate molecular interactions among the adsorbent components-cellulose, hemicellulose, lignin, and alginate-and the pollutant Cu2+, confirming their physically interactive nature. These findings emphasize the synergistic application of experimental and theoretical approaches, providing a comprehensive understanding of copper adsorption dynamics at the molecular level. This methodology holds promise for unraveling intricate processes across various adsorbent materials in wastewater treatment applications.

Bentonite SDBS-loaded composite for methylene blue removal from wastewater: An experimental and theoretical investigation.

This study addresses the urgent need for practical solutions to industrial water contamination. Utilizing Algerian Bentonite as an adsorbent due to its regional prevalence, we focused on the efficiency of the Bentonite/Sodium dodecylbenzene sulfonate (SDBS) matrix in Methylene Blue (MB) removal. The zero-charge point and IR spectroscopy characterized the adsorbent. Acidic pH facilitated SDBS adsorption on Bentonite, achieving equilibrium in 30 min with a pseudo-second-order model. The UPAC and Freundlich model indicated a qmax of 25.97 mg/g. SDBS adsorption was exothermic at elevated temperatures. The loaded Bentonite exhibited excellent MB adsorption (pH 3-9) with PSOM kinetics. Maximum adsorption capacity using IUPAC and GILES-recommended isotherms was qmax = 23.54 mg/g. The loaded Bentonite's specific surface area was 70.01 m2/g, and the Sips model correlated well with experimental data (R2 = 0.98). This study highlights adsorption, mainly Bentonite/SDBS matrices, as a promising approach for remediating polluted areas by efficiently capturing and removing surfactants and dyes, contributing valuable insights to address industrial water contamination challenges.

Development of Meta-Chemical Surface by Dip-Pen Nanolithography for Precise Electrochemical Lead Sensing.

Dip-pen nanolithography (DPN) is a powerful and unique technique for precisely depositing tiny nano-spherical cap shapes (nanoclusters) onto a desired surface. In this study, a meta-chemical surface (MCS; a pattern with advanced features) is developed by DPN and applied to electrochemical lead sensing, yielding a calibration curve in the ppb range. An ink mixture of PMMA and NTPH (which binds to Pb (II), as supported by DFT calculations) is patterned over a Pt surface. The average height of the nanoclusters is ≈13 nm with a high surface area-to-volume ratio, which depends on the ink composition and the MCS surface. This ratio affected the sensitivity of the MCS as a detecting tool. The results indicate that the sensor's features can be controlled by the ability to control the size of the nanoclusters, attributed to the unique properties of the DPN production method. These results are significant for the water-source purification industry.

Dacarbazine-primed carbon quantum dots coated with breast cancer cell-derived exosomes for improved breast cancer therapy.

Imprecise targeting of chemotherapeutic drugs often leads to severe toxicity during breast cancer therapy. To address this issue, we have devised a strategy to load dacarbazine (DC) into fucose-based carbon quantum dots (CQDs), which are subsequently coated with exosomes (Ex-DC@CQDs) derived from breast cancer cells. Nanoparticle tracking analysis and western blotting revealed that Ex-DC@CQDs retained the structural and functional characteristics of exosomes. We found that exosomes facilitated the transport of DC@CQDs to cancer cells via heparan sulfate proteoglycan (HSPG) receptors, followed by an augmented depolarization of the mitochondrial membrane potential, ROS generation, and induction of apoptosis leading to cell death. In vivo imaging and pharmacokinetic studies demonstrated enhanced antitumor targeting and efficacy compared to free DC which we attribute to an improved pharmacokinetic profile, a greater tumor accumulation via exosome-mediated- HSPG receptor-driven cell uptake, and sustained release of the Ex-DC@CQDs. Our findings may pave the way for the further development of biologically sourced nanocarriers for breast cancer targeting.

Chitosan-based intelligent polymeric networks for site-specific colon medication delivery: A comprehensive study on controlled release of diloxanide furoate and network formation dynamics.

Oral medications are prone to gastric degradation and enzymatic inactivation, diminishing their efficacy. This study investigates a solution by developing intelligent polymeric networks, incorporating chitosan, methacrylic acid, N, N, methylene bisacrylamide, and montmorillonite clay, to enable the controlled release of Diloxanide Furoate (DF), an anti-protozoal drug. Employing a swelling-assisted diffusion technique, drug loading percentages varied from 63.96 % to 76.82 % among different formulations. Increased chitosan and methacrylic acid content enhanced drug loading, while N, N, methylene bisacrylamide and montmorillonite clay demonstrated an inverse relationship affecting diffusion and swelling. Equilibrium swelling studies unveiled formulation-dependent behaviors, with chitosan reducing swelling and methacrylic acid promoting it. Higher N, N, methylene bisacrylamide concentrations decreased swelling, indicating a denser cross-linked structure, while montmorillonite clay reduced hydrophilicity and swelling capacity. Further analyses confirmed successful gel formation, particularly in formulations with higher chitosan, methacrylic acid, and N, N, methylene bisacrylamide content, while montmorillonite clay limited gel fraction due to restricted polymer chain mobility. Techniques such as Fourier transform infrared spectroscopy, Differential scanning calorimetry, and thermal gravimetric analyses supported network development, enhancing thermal stability and cross-linking density. This research underscores the flexibility of polymeric networks for precise drug delivery, offering potential advancements in targeted therapies for various medical conditions.

A review on fluoride contamination in groundwater and human health implications and its remediation: A sustainable approaches.

Contamination of drinking water due to fluoride (F-) is a major concern worldwide. Although fluoride is an essential trace element required for humans, it has severe human health implications if levels exceed 1.5 mg. L-1 in groundwater. Several treatment technologies have been adopted to remove fluoride and reduce the exposure risk. The present article highlights the source, geochemistry, spatial distribution, and health implications of high fluoride in groundwater. Also, it discusses the underlying mechanisms and controlling factors of fluoride contamination. The problem of fluoride-contaminated water is more severe in India's arid and semiarid regions than in other Asian countries. Treatment technologies like adsorption, ion exchange, precipitation, electrolysis, electrocoagulation, nanofiltration, coagulation-precipitation, and bioremediation have been summarized along with case studies to look for suitable technology for fluoride exposure reduction. Although present technologies are efficient enough to remove fluoride, they have specific limitations regarding cost, labour intensity, and regeneration requirements.