Accurate non-invasive subclassification of Renal Cell Carcinoma (RCC) is important for early automated diagnosis, guiding early subclass-specific treatment for better prognosis. However, RCC subclassifications are based on post-biopsy histological cell-patterns. Biopsy is an invasive and costly procedure with many side effects, such as internal bleeding, infection, and pain at the puncture site. RCC subtyping from radiological images presents significant diagnostic challenges as visual patterns overlap for different classes. Previous research studies perform only binary classifications for malignancy. In this study, we propose a novel fusion-based model that integrates statistically driven radiomics features, convolutional neural network, including Convolutional Block Attention Modules (CBAM) and residual links, augmented with additionally weighted Mutual Information (MI) common to both statistically derived radiomics features and features derived by deep learning analysis, to achieve automated subclassification of three major RCC subtypes: clear cell RCC, papillary RCC, and chromophobe RCC. Convolution layers extract local features in radiology images, and CBAM emphasizes the most informative spatial features and channels. Residual links mitigate information loss during convolutional transformations. MI emphasizes common prominent features. We trained the model on the KiTS19 and KiTS21 challenge datasets, comprising 20,253 Computed Tomography (CT) slices. The framework achieves a validation accuracy of 96.7

Accurate grading of renal cell carcinoma (RCC) is critical for prognosis estimation and treatment planning. Current grading relies on post-biopsy histopathological manual assessment of whole-slide images (WSIs), which is time-consuming and subject to inter-observer variability. Automated multiclass RCC grading remains challenging due to subtle inter-grade morphological and textural differences that cannot be captured just by histomics features or deep learning features individually. This study proposes a decision level-fusion based novel framework using histomics, machine learning and deep learning methodologies to derive multiclass RCC grading, which gives significantly higher accuracy. The approach integrates convolutional network with reduced information loss using residual links, channel and spatial attention to derive prominent deep learning features, Minimum Redundancy–Maximum Relevance (mRMR) guided prominent histomics features selection, augmented with classical machine learning classifiers. Convolutional networks capture localized nuclear and textural patterns. The model was evaluated on a five-class grading task (non-cancerous, Grades 1–4 of clear cell RCC) using a publicly available standard kidney histopathology dataset comprising 4,003 WSIs. The experiment exhibits that histomics contribute significantly in deriving maximum accuracy. Yet deep features significantly enhance accuracy beyond histomics analysis. Experimental results exhibit a high validation accuracy of 96.0% and a test accuracy of 96.1%, which can assist clinical decisions.

Artificial intelligence, including deep learning models, will play a transformative role in automated medical image analysis for the diagnosis of cardiac disorders and their management. Automated accurate delineation of cardiac images is the first necessary initial step for the quantification and automated diagnosis of cardiac disorders. In this paper, we propose a deep learning based enhanced UNet model, U-R-Veda, which integrates convolution transformations, vision transformer, residual links, channel-attention, and spatial attention, together with edge-detection based skip-connections for an accurate fully-automated semantic segmentation of cardiac magnetic resonance (CMR) images. The model extracts local-features and their interrelationships using a stack of combination convolution blocks, with embedded channel and spatial attention in the convolution block, and vision transformers. Deep embedding of channel and spatial attention in the convolution block identifies important features and their spatial localization. The combined edge information with channel and spatial attention as skip connection reduces information-loss during convolution transformations. The overall model significantly improves the semantic segmentation of CMR images necessary for improved medical image analysis. An algorithm for the dual attention module (channel and spatial attention) has been presented. Performance results show that U-R-Veda achieves an average accuracy of 95.2%, based on DSC metrics. The model outperforms the accuracy attained by other models, based on DSC and HD metrics, especially for the delineation of right-ventricle and left-ventricle-myocardium.

The present study aimed to investigate the Exemestane - maleic acid (Ex-Mal) cocrystal using Fourier-transform Raman (FT-Raman), Fourier-transform Infrared (FT-IR), Powder X-ray Diffraction (PXRD) and Differential Scanning Calorimeter (DSC) experimental techniques and quantum chemical calculations. Exemestane (Ex) is an irreversible aromatase inhibitor, clinically used for the treatment of postmenopausal women having primary or advanced breast cancer. Maleic acid (Mal), a dicarboxylic acid is found in many vegetables and fruits and is used as a food additive and sweetener. Solution crystallization of Ex and Mal was done in the generation of Ex-Mal cocrystal (1:1). PXRD and DSC analysis confirmed the successful generation of Ex-Mal Cocrystal. Intermolecular hydrogen bonding (O4-H12 & sdot;& sdot;& sdot;O13 & C8-O3 & sdot;& sdot;& sdot;H36) also suggested the formation of a multi-component system, Ex-Mal cocrystal (1:1), as the respective modes shifted in the vibrational (Raman & IR) spectra of cocrystal than the Ex. Further, the quantum theory of atoms in molecules (QTAIM) has been done to analyze the strength and nature of inter-/intra molecular hydrogen bonding. Natural bond orbital (NBO) analysis was performed to check the stabilization energy of the system. The frontier molecular orbital (FMO) analysis predicted that Ex-Mal cocrystal is more reactive and less stable than Ex. Molecular electrostatic potential (MEP) surface showed that electrophilic (C16-H36)/(O4-H12), and nucleophilic (C8=O3)/(C17=O13) reactive groups in Ex/ Mal and Mal/Ex, respectively, neutralized after the formation of Ex-Mal cocrystal, confirming the presence of hydrogen bonding interactions (C16-H36 center dot center dot center dot O3=C8) and (C17=O13 center dot center dot center dot H12-O4). Molar Refractivity (MR) was calculated to check the drug-likeness behaviours of Ex-mal cocrystal. This study provided a comparison of experimental and theoretical results to understand the hydrogen bonding interactions and structural activity of the system.

Polymeric amorphous solid dispersions (ASDs) are frequently used to improve solubility and oral bioavailability of poorly water-soluble drugs. The goal of the current investigation was to correlate the variability in the presence of polymeric stabilizer, from different sources and batches, with the stability of the spray-dried ASDs. Polyvinylpyrrolidone-vinyl acetate (PVPVA) and griseofulvin (GSV) were selected as model stabilizer and drug, respectively. Powder X-ray diffraction (pXRD), Differential Scanning Calorimetry (DSC), and Polarized Light Microscopy (PLM) confirmed the amorphous form and phase homogeneity of the spray-dried ASDs. pXRD and modulated DSC were used to determine the functionality (% crystallization) and intermediate functionality (enthalpy relaxation) of PVPVA, respectively, in spray-dried ASDs. The variability in different physicochemical properties of PVPVA, viz. glass transition temperature, viscosity of PVPVA solution in acetone, K-value, residue on ignition, true density, increase in water activity after storage, surface free energy, solvent evaporation kinetics, and diffusion coefficient of GSV in acetone in presence of PVPVA, were measured. Later, these properties were correlated with functionality and intermediate functionality using Pearson's correlation coefficient. Strong correlation (r ≥ 0.6) was observed with solvent evaporation kinetics, diffusion coefficient of GSV in acetone, in presence of PVPVA (indicates interaction of drug with polymer in feed solution of spray drying), K-value (indicates polymer molecular weight and chain length), and increase in water activity after storage (indicates interaction of polymer with water). Functionality-related characteristics (FRCs), and their corresponding functionality-related tests (FRTs), that can affect the functionality of PVPVA, as a stabilizer in GSV-PVPVA spray-dried ASD have been proposed. The learnings of this study will be beneficial to ensure robust performance of spray-dried ASDs containing other poorly soluble drug(s) and water soluble polymer(s). Due attention should be paid to ensure compliance to the proposed FRCs of the stabilizer from different batches and sources, during the manufacturing of spray-dried ASD, to ensure consistent quality and performance.

A standardized polyphenol-enriched fraction (IPHRFPPEF) was formulated into a phospholipid complex (IPHRFPPEF-PC) to enhance oral bioavailability and evaluate stability, toxicity, and in vivo anti-inflammatory activity in Sprague Dawley rats. IPHRFPPEF was prepared from crude extract using XAD-HP7/Diaion-HP20 resin column chromatography and analyzed via HPLC and NMR. Total phenolic and flavonoid contents were quantified, with IPHRFPPEF showing higher values than the crude fraction. The phospholipid complex was prepared via solvent evaporation and assessed for bioavailability, stability, and toxicity. Key results demonstrated a 1.99-fold, 2.03-fold, and 1.66-fold increase in plasma concentrations of isorhamnetin, kaempferol and quercetin respectively. Acute oral toxicity testing showed an LD50 of 5000 mg/kg (GHS Category 5), and repeated-dose studies confirmed safety. IPHRFPPEF-PC exhibited enhanced pharmacokinetics and potent in vivo anti-inflammatory effects. In conclusion, the development of IPHRFPPEF-PC from a standardized polyphenol-enriched fraction offers a safe and effective therapeutic approach, with significant potential for future applications in treating inflammatory conditions.

Small Renal Masses (SRM), (renal masses ≤ 4.0 cm in diameter), present significant diagnostic challenges when using radiological images. Current contrast enhancement-based methods need further advancements in fine subclassification of SRMs to identify the need to rule out borderline malignancy, and the need for regular monitoring. In this study, we propose an enhanced deep learning model that integrates convolutional layers, channel expansion and squeeze to extract important features, cross-channel attention, selective channel retention and residual links for an automated subclassification of T1a SRMs to improve diagnostics and medical treatment by ruling out possible future malignancy. Convolutional layers extract local features. Channel expansion and squeeze, augmented with cross-channel attention and selective channel retention, enhance the selection of the most informative features. Residual links help mitigate information-loss during convolutional transformation. We present an algorithm and evaluate our model on the KiTS19 and KiTS21 challenge datasets, comprising 8,262 CT slices. The model achieved a validation accuracy of 98.2

Dissolution of amorphous solid dispersion (ASD) involves complex array of molecular events at the surface of the dissolving ASD. In situ nanoparticle formation during dissolution of ASD has gained significant attention. Formation of nanoparticles during ASD dissolution commonly has been ascribed to Liquid Liquid Phase Separation (LLPS), and occurs after drug release, exceeding the amorphous solubility. Despite counter-intuitive observations, limited investigations have recently recognized Amorphous-Amorphous Phase Separation (AAPS) as a route of nanoparticle formation, in absence of LLPS. The understanding of macroscopic, microscopic, and molecular events, responsible for nanoparticle formation via AAPS remains elusive. In the present study, the mechanism of nanoparticle formation during dissolution of aprepitant (APR)-poly (vinyl pyrrolidone-vinyl acetate) (PVPVA) ASD has been studied to elucidate the effect of drug loading and related formulation factors on AAPS-mediated nanoparticle formation. PVPVA release was faster as compared to release of APR during surface normalized release (SNR) testing due to greater hydrophilicity of PVPVA. This made the surface of PVPVA-based dissolving compacts rough. Powder X-ray Diffraction (pXRD) analysis of post-dissolution compact revealed appearance of crystallinity in ASDs. Despite bulk concentration below amorphous solubility (similar to 9 mu g/mL at 37 degrees C), release of nanoparticle (similar to 200-300 nm), of semi-crystalline nature was observed at 5 % and 20 % w/w drug loaded ASDs, whereas, at 40 % w/w drug loading nanoparticle release was negligible. Spatial pattern of phase separation and solution-phase distribution study of APR revealed that nanoparticles were formed from dissolving ASD compact surface through AAPS-mediated local domain formation. In contrast, nanoparticles were absent in 40 % w/w drug loaded ASD due to high inter-connectivity and spatial density of APR-rich domain, in the dissolving compact surface. This study captures the simultaneous interplay of different physicochemical factors at dissolving ASD-water interface during dissolution of ASD, and its impact on bulk dissolution outcome. The understandings of this study have implications in rational design of ASD formulation for better biopharmaceutical performance.

In the past few years, nanospecies formation during dissolution of amorphous solid dispersion (ASD) has gained significant attention, mainly due to their positive biopharmaceutical attributes. However, limited knowledge is available about the mechanism and factors involved in generation of nanospecies during dissolution of ionic polymer-based ASDs. In the present study, the mechanism of nanospecies formation during dissolution of ASDs containing aprepitant (APR)-Eudragit L100-55 has been investigated as a function of drug loading and microenvironmental pH. APR and Eudragit L100-55 at all drug loadings (5, 20, and 40% w/w) showed congruent release from the dissolving ASD compact surface during surface normalized release (SNR) testing due to drug-polymer interactions and relatively low hydrophilicity of Eudragit L100-55. Nanospecies in a bulk medium were observed only in the case of 5 and 20% w/w drug loaded ASDs. The concentration of the drug released from any ASDs did not exceed amorphous solubility in a bulk solution. Powder X-ray diffraction (pXRD) analysis of the postdissolution compact revealed the absence of crystallinity in ASDs. Confocal laser scanning microscopy (CLSM) and polarized light microscopy (PLM)-assisted analysis showed the absence of amorphous-amorphous phase separation (AAPS) at the surface of hydrated ASD. Experiments were conducted to simulate the unstirred water layer (UWL) and to investigate dissolution behavior. The microenvironmental pH of ASD was influenced by drug loading, and this influenced the amorphous solubility of APR. LLPS in an unstirred water layer (UWL) was one of the potential mechanisms behind nanospecies generation. Further, the solution phase behavior below LLPS concentration indicated nanospecies generation through ion-pair formation between oppositely charged APR and Eudragit L100-55 in UWL. This study accentuates the complexity of dissolution mechanism of ionic polymer-based ASD and should contribute to designing ASDs containing ionic polymers.

The pharmaceutical industry not only plays a crucial role in the healthcare system but also contributes significantly to the economy of a country. It is interesting to note that in 2024, a Danish pharmaceutical company held the title of Europe's most valuable company. Novo Nordisk, riding on popularity of a weight-loss drug, had a valuation of $600 billion in 2024, outweighing the GDP of Denmark Nat Med 2024, 30, 2049. Indian companies have achieved global recognition in generics, thus earning India the title of the "pharmacy of the world". This position was further strengthened during the COVID-19 pandemic when India supported the global needs with its high-capacity manufacturing and efficient supply chain management. However, despite these awards, the total valuation of the Indian pharmaceutical market, including its export values, is less than $100 billion. This highlights the focus of Indian pharmaceutical companies on generics, which generally generates less revenue with high volumes Journal of Positive School Psychology 2022, 9285. In contrast, innovative products can generate a high revenue and accelerate economic growth. Innovative companies spend a good amount of their funds on research and development. It is well-recognized that a synergy between academic research institutions and pharmaceutical companies supports innovative outcomes Res. Policy 1991, 20, 1-12. In this perspective, we discuss the prominent areas of pharmaceutical research in Indian academia and also analyze the status of solid-state pharmaceutics (SSP) and pharmaceutical crystal engineering research. The article discusses the evolution of research in SSP, its status, and future prospects. Authors emphasize the need for improvement of the research ecosystem for SSP, thus ensuring availability of optimal human resources for this critical component of the pharmaceutical industry. There is a need to create a "solid-state pharmaceutics research cluster" in India to accelerate the research and support the growth of the Indian pharmaceutical industry.

Altering surface chemistry of functional materials is an attractive route to enable large property enhancements without sacrificing overall structural-order, appealing to diverse fields of application sciences; however, the same remains unexplored for organic crystalline materials. Herein, piezoelectricity in pharmaceutical crystals is reported to show colossal surface charges driven by mechanical fracture - where a collection of dipoles arranged in polar head-to-tail fashion generates opposite surface charges on freshly fractured faces - causing them to actuate large distances over 75 µm in milliseconds. Kelvin probe force microscopy is leveraged to show many-fold surface potential enhancement in fractured surfaces relative to the pristine crystals. Further, complementarity of the surface potentials in a pair of fractured crystal shards and asymptotic decay behaviour with time are observed. Newly formed surfaces of the pharmaceutical crystals show long-lasting charges despite their relatively lower piezo-response confirmed by bulk piezometry. To establish the generality of surface phenomena, statistical analyses (≈50 samples) of post-fracture-attraction behaviour of crystals are performed. Finally, the application of fracture-driven surface charges in industrial processes is achieved by investigating flow-property and tablet-strength of bulk pharmaceutical materials. This multiscale approach unveils the symmetry-dependency of surface charges in fractured materials, and probes the same for utilisation in bulk-property engineering.

Current artificial intelligence based models for automated medical diagnosis lack human vision accuracy and efficiency for detecting cardiac structures and functional abnormalities from radiologic images. This study presents a human visual attention inspired deep learning model for the semantic segmentation of Cardiac Magnetic Resonance images to improve the accuracy and efficiency in detecting cardiac structures. The model integrates YOLO, Visual-saliency-map analysis of known features involving texture and pixel intensities, attention-based encoder-decoder model with minimal information loss. YOLO provides a fast region-of-interest detection, pruning redundant image-space and feature-space for efficient target localization. Visual-saliency-maps enhance the probability for pixels associated with target structures. Visual-saliency-map derivation uses a top-down and bottom-up analysis of computationally derived features. The top-down process applies the prior knowledge of known textural and pixel-intensity patterns associated with the cardiac structures. The bottom-up process derives semantically-rich local and global features from the images. Derived visual-saliency-maps are combined with feature-maps, derived by convolution layers, in the encoder-stage to boost the focus on relevant cardiac structures. Information loss in the encoder-decoder stage is minimized by integrating residual links, channel and spatial attention, visual-saliency-maps and edge-detection in the encoder-stage and passing this information as skip-connections to the corresponding decoder-stage. Algorithms are presented. The performance result shows that pruning the image-space and visual-saliency-map augmentation significantly improves the accuracy and inference-time of the semantic segmentation of cardiac structures.

OBJECTIVE:The objective of this study was to evaluate the efficacy and safety of topical nanoemulsion (NE)-loaded cream and gel formulations of Hippophae rhamnoides L. (sea buckthorn [SBT]) fruit oil for wound healing. MATERIALS AND METHODS:The NE-loaded cream and gel formulations of H. rhamnoides L. (SBT) fruit oil (IPHRFH) were prepared and evaluated for their wound-healing activity on female Sprague-Dawley (SD) rats. They were further divided into groups (seven) and the wound-healing activity was determined by measuring the area of the wound on the wounding day and on the 0th, 4th, 8th, and 10th days. The acute dermal toxicity of the formulations was assessed by observing the erythema, edema, and body weight (BW) of the rats. RESULTS:The topical NE cream and gel formulations of H. rhamnoides L. (SBT) fruit oil showed significant wound-healing activity in female SD rats. The cream formulation of IPHRFH showed 78.96%, the gel showed 72.59% wound contraction on the 8th day, whereas the positive control soframycin (1% w/w framycetin) had 62.29% wound contraction on the 8th day. The formulations also showed a good acute dermal toxicity profile with no changes significantly affecting BW and dermal alterations. CONCLUSIONS:The results of this study indicate that topical NE-loaded cream and gel formulation of H. rhamnoides L. (SBT) fruit oil are safe and effective for wound healing. The formulations showed no signs of acute dermal toxicity in female SD rats.

Automated noninvasive medical diagnosis of the heart is necessary for early detection of cardiac disorders and cost-effective management. Automated diagnosis comprises automated image segmentation and analysis of cardiac magnetic resonance, CT scan or echocardiogram. Automated segmentation of cardiac substructures and their feature-attributes is necessary for evaluating cardiac functions, disorders, and diagnosis of cardiovascular diseases such as cardiomyopathy, valvular diseases, abnormalities caused by septum perforations, ischemia and blood-flow rate. Semantic segmentation labels an image at the pixel-level, and is used to localize various subcomponents of an object. Localization of the subcomponents facilitates the detection of abnormalities, including abnormalities in cardiac wall motions in an aging heart with muscle abnormalities, vascular abnormalities, and valvular abnormalities. In this paper, we describe a model to improve semantic segmentation of CMR images. The model extracts edge-attributes and context information during down-sampling of the U-Net and infuses this information during up-sampling to localize three major cardiac structures: left ventricle cavity (LV); right ventricle cavity (RV); LV myocardium (LMyo). We present an algorithm and performance results. A comparison of our model with previous leading models, using similarity-metrics between the actual image and the segmented image, shows that our approach improves Dice similarity coefficient (DSC) by 2

Co-speech human gesture analysis is an important aspect for social conversational interactions involving human-robot interfaces. Co-speech gestures require synchronous integration of speech, human posture, and motions. Iconic gestures are a major subclass of co-speech gestures that express entities and actions by their attributes such as shape-contours, magnitude, and proximity using the synchronous motions of fingers, palms, and spoken phrases. The attributes of entities and actions correlate directly with the displayed contours. In this research, we describe an integrated technique that combines motion analysis to derive contours, synchronization of motion with speech to identify words corresponding to iconic gestures, and conceptual dependency of action words to drive iconic gestures. This technique models motion-sketched contour as a combination of synchronous color Petri net extended to model composite motions and contour-segment patterns. We present high-level algorithms and the corresponding implementation for the proposed technique and evaluate its performance. Performance results show approximately 90% recognition of simple contours, including closed contours.

We propose an enhanced deep learning-based model for image segmentation of the left and right ventricles and myocardium scar tissue from cardiac magnetic resonance (CMR) images. The proposed technique integrates UNet, channel and spatial attention, edge-detection based skip-connection and deep supervised learning to improve the accuracy of the CMR image-segmentation. Images are processed using multiple channels to generate multiple feature-maps. We built a dual attention-based model to integrate channel and spatial attention. The use of extracted edges in skip connection improves the reconstructed images from feature-maps. The use of deep supervision reduces vanishing gradient problems inherent in classification based on deep neural networks. The algorithms for dual attention-based model, corresponding implementation and performance results are described. The performance results show that this approach has attained high accuracy: 98% Dice Similarity Score (DSC) and significantly lower Hausdorff Distance (HD). The performance results outperform other leading techniques both in DSC and HD.

The solid-state properties of active pharmaceutical ingredient (API) have significant impact on its dissolution performance. In the present study, two different crystal habits viz. rod and plate shape of form I of FEN were evaluated for dissolution profile using USP Type 2 and Type 4 apparatuses. Molecular basis of differential dissolution performance of different crystal habits was investigated. Rod (FEN-R) and plate (FEN-P) shaped crystal habits of Form I of FEN were generated using anti-solvent crystallization method. Despite the same polymorphic form and similar particle size distribution, FEN-P demonstrated higher dissolution performance than FEN-R. Crystal face indexation and electrostatic potential (ESP) map provided information on differential relative abundance of various facets and their molecular environment. In FEN-R, the dominant facet (001) is hydrophobic due to the exposure of chlorophenyl moiety. Whereas, in FEN-P the dominant facet (01–1) was hydrophilic due to the presence of chlorine and ester carbonyl groups. Deeper insight on the impact of different facets on dissolution behavior was obtained by energy framework analysis by unveiling strength of intermolecular interactions along various crystallographic facets. Moreover, type 4 apparatus provided higher discriminatory ability over USP Type 2 apparatus, in probing the crystal habit induced differential dissolution performance of FEN. The findings of this study emphasize that crystal habit should be considered as an important critical material attribute (CMA) during formulation development of FEN and due considerations should be given to the selection of the appropriate dissolution testing set-up for establishing in vitro-in vivo correlation.

The impressive performance of Transformer models in natural language tasks has sparked considerable interest within the computer vision community, leading to a focused exploration of their application to address challenges in computer vision problems. Transformers excel in modeling long-range dependencies among input sequence elements and enable parallel processing, which makes them different from traditional recurrent networks like Long Short-Term Memory (LSTM). Notably, Transformers exhibit minimal need for inductive biases in their design, making them naturally suited for diverse tasks. Their straightforward design allows the processing of multiple modalities, such as images, videos, text, and speech, using similar processing blocks, demonstrating exceptional scalability to large-capacity networks and extensive datasets. Leveraging these strengths has led to significant advancements in various vision tasks, marking a departure from convolutional networks in image classification. This survey provides a comprehensive overview of Transformer models in the field of computer vision, starting with an introduction to the core concepts that contributed to their success. The exploration includes a comparative analysis of the advantages and limitations of popular techniques, both in terms of architectural design and experimental value. Additionally, the survey delves into open research directions and potential future works, aiming to stimulate further interest within the community and address current challenges in the application of Transformer models in computer vision.

Development of Amorphous Solid Dispersion (ASD) requires an in-depth characterization at different stages due to its structural and functional complexity. Various tools are conventionally used to investigate the processing, stability, and functionality of ASDs. However, many subtle features remain poorly understood due to lack of nano-scale characterization tools in routine practice. Atomic force microscopy (AFM) is a type of scanning probe microscopy, used for high resolution imaging and measuring features at the nano-scale. In recent years AFM has been used increasingly as a characterization tool in different areas of the development of ASD, including drug-polymer miscibility, localized characterization of the phase separated domains, lateral molecular diffusivity on ASD surface, crystallinity and crystallization kinetics in ASD, phase behavior of ASD during dissolution, and conformation of polymer during dissolution. In this review, we have highlighted the current applications of AFM in capturing critical aspects of stability and dissolution behavior of ASD. Potential areas of future development in this domain have been discussed.