Reliable Approach Research Articles

Reconstructing Molecular Orbitals with Laser-Induced Electron Tunneling Spectroscopy

Photoelectron spectroscopy in intense laser fields has proven to be a powerful tool for providing detailed insights into molecular structure. The ionizing molecular orbital, however, has not been reconstructed from the photoelectron spectra, because its phase information is difficult to access. Here, we propose a method to retrieve the phase information of the ionizing molecular orbital. By analyzing the interference pattern in the photoelectron spectrum, the weighted coefficients and the relative phases of the constituent atomic orbitals for a molecular orbital can be extracted. With this information, we reconstruct the highest occupied molecular orbital of N 2 . Our work provides a reliable and straightforward approach for reconstructing molecular orbitals with the photoelectron spectroscopy.

Selective extraction and quantitative analysis of pyrroloquinoline quinone from food.

Pyrroloquinoline quinone (PQQ) is a bioactive compound that has attracted significant attention due to its potential health benefits. In this study, we developed a new magnetic molecularly imprinted nanoparticle (MMIN) for the selective extraction and determination of PQQ from food samples. The MMIN was synthesized using a surface molecular imprinting technique with PQQ as the template molecule, Fe3O4 nanoparticles as the magnetic core, and methacrylic acid as the functional monomer. The MMIN exhibited high selectivity and affinity towards PQQ, allowing for efficient extraction and preconcentration of PQQ from complex food matrices. The extracted PQQ was then quantified using HPLC-DAD. The developed method showed good linearity (R2 = 0.9985) and low limits of detection (0.03 μg L-1). The accuracy and precision of the method were evaluated by analyzing spiked food samples, with average recoveries close to 89.8%. The MMIN also demonstrated good reusability, with negligible decrease in extraction efficiency after five cycles of use. Overall, the developed MMIN-based method provides a reliable and efficient approach for the analysis of PQQ in food samples.

A modified version of a Benson-type algorithm proposed for obtaining solutions with better dispersion on the non-dominated set of a non-convex multi-objective programming problem

This paper proposes a new method to approximate the non-dominated set of a general multi-objective programming (MOP) problem with continuous variables (that is, without any convexity assumption). The algorithm is based on a recent modification of Benson's method [14], and like Benson's idea, generates an inner approximation and an outer approximation of the (weakly) non-dominated set of an MOP. It is proved that this method provides a set of (weakly) $ \varepsilon $-non-dominated points. To evaluate the performance of the proposed algorithm, some different test problems are presented. The results show that the algorithm provides a reliable and practical approach to the approximation of non-dominated sets.

Assessment of Clinical VIDA Score, Lesional and Perilesional Dermoscopy to Evaluate Stability of the Disease in Vitiligo

Abstract Introduction: Vitiligo is a complex dermatosis with an uncertain etiology, variable clinical presentations, an unpredictable course and prognosis, and difficult to treat. Surgical intervention is recommended for patients with a stable, nonprogressive vitiligo. Dermoscopy can detect the subtle changes in the pigmentary pattern and aid in the diagnosis of evolving lesions. This study was undertaken to measure the stability of the disease by classifying the patients as clinically stable and active and observing dermatological parameters. Objective: The aim of the study is to evaluate assess the correlation between the clinical and dermoscopic features in stable and active cases of vitiligo. Materials and Methods: Forty patients of vitiligo. Clinical VIDA score and six lesional and perilesional dermoscopic parameters were analyzed. Statistical analysis was done to determine their correlation. Results: Altered pigment network (91.6%) was the most observed dermoscopic feature followed by perifollicular depigmentation (66.7%) in clinically unstable patients and altered pigment network (87.5%) followed by leucotrichia (68.8%) was seen in clinically stable patients. However, vascularity and Koebner’s phenomena failed to produce the same. Conclusion: Lesional and perilesional dermoscopic examination together for the above four parameters (pigmentary network, perifollicular changes, perilesional hypopigmentary macules, and leucotrichia) will produce reliable diagnostic approach for differentiating patients as clinically stable or unstable along with clinical VIDA score.

Data Augmentation for Medical Image Classification based on Gaussian Laplacian Pyramid Blending with a Similarity Measure.

Breast cancer is a devastating disease that affects women worldwide, and computer-aided algorithms have shown potential in automating cancer diagnosis. Recently Generative Artificial Intelligence (GenAI) opens new possibilities for addressing the challenges of labeled data scarcity and accurate prediction in critical applications. However, a lack of diversity, as well as unrealistic and unreliable data, have a detrimental impact on performance. Therefore, this study proposes an augmentation scheme to address the scarcity of labeled data and data imbalance in medical datasets. This approach integrates the concepts of the Gaussian-Laplacian pyramid and pyramid blending with similarity measures. In order to maintain the structural properties of images and capture inter-variability of patient images of the same category similarity-metric-based intermixing has been introduced. It helps to maintain the overall quality and integrity of the dataset. Subsequently, deep learning approach with significant modification, that leverages transfer learning through the usage of concatenated pre-trained models is applied to classify breast cancer histopathological images. The effectiveness of the proposal, including the impact of data augmentation, is demonstrated through a detailed analysis of three different medical datasets, showing significant performance improvement over baseline models. The proposal has the potential to contribute to the development of more accurate and reliable approach for breast cancer diagnosis.

Estimating Maximum Deflection of PCI Girder Bridge Using Tiltmeter Sensor: A Practical Approach for Structural Monitoring

Bridges are critical infrastructures subjected to dynamic and repetitive loads over extended periods, necessitating continuous monitoring to ensure their structural integrity. Monitoring the performance of a bridge involves several key parameters, with maximum deflection being of utmost importance. This study aims to develop an equation model using multiple regression analysis to establish the relationship between deflection values and rotation values obtained from a PCI girder bridge as a project case study. A 3-D Finite Element Method model was developed to accurately represent the bridge's geometric and mechanical properties. Actual data on maximum deflection and rotation were acquired by subjecting the FEM model to realistic load conditions. Regression analysis was conducted to derive equations for calculating maximum deflection based on the measured rotation and strain data. The accuracy of the estimated maximum deflection was validated by comparing it with values obtained from 3D structural analysis using FEM software The results demonstrate that the proposed regression equation accurately estimates the maximum deflection, with an average accuracy rate of 98.185%. Thus, this method presents a reliable approach for estimating the maximum deflection based on tiltmeter readings, offering a practical solution with a commendable level of accuracy.

DESIGN OF A SWITCHED BEAM ANTENNA SYSTEM USING A 4 × 4 BUTLER MATRIX FOR WIRELESS COMMUNICATION APPLICATIONS

Demand for quality service within the cellular network, which has many cells within the cluster, has certain requirements to satisfy the customer or subscriber at any cost. Hence, a reliable approach to provide quality service to the subscriber is needed, and in that attempt, a switched beam system is proposed, which is composed of a Butler matrix operating at 2 GHz for 5G wireless communication systems. The Butler matrix operates at 2 GHz, however, and has an overall bandwidth of 1.2 GHz, ranging from 1.8 GHz to 3 GHz. The beamforming operation is completed by incorporating an antenna array; typically for such applications a linear array with half lambda spacing is preferred. Here, the antenna array is made of a dielectric material, hence forming a dielectric resonator array operating at the same frequency as that of the Butler matrix. To complete the switched beam system, a radiofrequency source is chosen, typically USRP 2900 from National Instruments, to provide excitation to the Butler matrix throughout its operation. The simulation is carried out in Ansys HFSS and PCB Antenna Designer from MathWorks. This article gives the simulation as well as measured results of the Butler matrix and dielectric resonator antenna and draws a conclusion of the performance of the system based on simulated and measured results.

Ahmed glaucoma valve augmented with mitomycin C and amniotic membrane as a first intervention in advanced primary congenital glaucoma

Aim The goal of this study is to assess the effectiveness of implantation of an Ahmed glaucoma valve (AGV) augmented with the use of mitomycin-C (MMC) and preserved amniotic membrane (AM), as a first line of intervention in cases of advanced primary congenital glaucoma (PCG). Patients and methods Prospective clinical and interventional trial was done on 30 eyes of 24 patients, aged between 6 months and 3 years, with advanced PCG at presentation. AGV (model FP8) implantation augmented with use of MMC and two layers of cryopreserved human AM as a primary intervention. Results At all-time intervals (at day 1, 1, 3, and 12 months following surgery), the postoperative intraocular pressures (IOPs) were significantly lower than the preoperative IOPs (16.83±2.78 mmHg at month 12 compared with a preoperative IOP of 38.29±4.23 mmHg). At all times, the postoperative corneal diameter was not noticeably smaller than the preoperative values (13.79±0.57 mm at month 12 compared with preoperative value of 13.93±0.58 mm). Conclusion AGV augmented with the use of MMC and AM can be considered as a secure and reliable approach for management of cases of advanced PCG, without additional risks compared with other techniques.

Ultrasensitive electrochemical detection and inhibition evaluation of DNA methyltransferase based on cascade strand displacement amplification.

An electrochemical sensing approach for ultrasensitive DNA methyltransferase (MTase) activity assay is proposed. After specific cleavage reaction in the presence of a methylated state, strand displacement polymerization (SDP) is initiated in the solution. The product of upstream SDP further triggers downstream SDP, which enriches abundant electrochemical species at the electrode. The whole process is quite convenient with shared enzymes. Due to the cascade signal amplification, ultrahigh sensitivity is promised. Inhibitor screening results are also demonstrated to be good. Besides, target MTase can be accurately determined in human serum samples, confirming excellent practical utility. This work provides a reliable approach for the analysis of MTase activity, which is of vital importance for related biological studies and clinical applications.

Assessment of effects in advances of accounting technologies on quality financial reports in Jordanian public sector

The study aimed to examine the effects of accounting technology improvements on the generation of accurate and reliable financial reports in the public sector of Jordan. In order to carry out this inquiry, the researchers set research goals and formulated null hypotheses that were derived from these objectives and afterwards used in the study. The study used an ex-post facto survey methodology as its research technique. The study sample included 250 persons employed at the Ministry of Finance in Jordan. The research included a sample size including 152 people. A questionnaire was used as the primary tool for data collection in this study. The validity of the instrument was established by an evaluation conducted by experts specialising in the field of testing and measurement. The evaluation of the instrument's dependability was performed using the Cronbach Alpha reliability approach, yielding a reliability coefficient ranging from 0.73 to 0.85. The findings of this research demonstrate that the instrument has a significant level of dependability. The data obtained from the surveys underwent analysis using the Pearson Product-Moment Correlation (PPMC) and regression analysis approaches. The present study offers empirical data and affirms the growing significance of financial reporting in the global economic landscape. Ensuring unwavering trust in the financial information pertaining to the public sector has considerable significance for investors. The article proposes that the establishment of a comprehensive framework of guidelines for enterprises' information technology infrastructure would be advantageous for regulatory bodies, such as the Jordan Central Bank. The aim of this method is to reduce the potential danger of the public sector being overwhelmed by outdated technology.

Nerve conduction study: A reliable approach to assess the effect of electromagnetic waves on median nerve

Background: Mobile phones and other touchscreen devices emit dangerous electromagnetic radiation that negatively affect nerve function, with the median nerve being the most vulnerable because it is mostly utilized for mobile phone typing. Nerve conduction studies (NCSs) can be used to successfully diagnose this change in nerve function NCS. Aims and Objectives: The goal of the study was to ascertain the impact of electromagnetic waves from mobile phones on the median nerve’s nerve conduction velocity (NCV). Materials and Methods: 150 young, healthy medical students participated in a cross-sectional (observational) study, and they were split into two groups based on how long they used their mobile phones: Group 1 (<30 min usage) and Group 2 (>30 min usage). The median nerves in both upper limbs were measured for their NCV, and the results were statistically analyzed using the student “t” test for equal variance for various parameters. Results: The research found that Group 1 participants, who use cell phones more frequently and for longer periods of time, had significantly reduced (impaired) sensory NCV of the median nerve. Conclusion: Smartphones and other touchscreen gadgets, depending on time of exposure and usage frequency, can negatively impact nerve function, resulting in a decrease in the median nerve’s conduction velocity, and this can be very efficiently diagnosed with the use of NCSs.

Height estimation of pine (Pinus eldarica) single trees using slope corrected shadow length on unmanned aerial vehicle (UAV) imagery in a plantation forest

Tree height is one of the key parameters in forest plantations that plays a crucial role in estimation of above-ground biomass (AGB) of trees and stands. The parameter may be obtained by different methods from airborne remotely sensed datasets such shadow length of each tree individual or crown height models (CHMs). However, tree height estimation based on shadow length might be biased considering diverse topography of forest sites. Therefore, this study aims to develop a reliable method to estimate tree height in a plantation forest using shadow length on UAV imagery. First, heights of 151 pine (Pinus eldarica) trees were precisely measured in Pardisan Park, North Khorasan province, Iran. Additionally, a collection of images was captured by a Phantom 4Pro UAV in order to illustrate the study area. Then, two different approaches were considered to estimate the height of trees. In the first approach, tree heights were estimated based on shadow length on the UAV orthomosaic and correcting the effect of slope. The second approach considered the UAV-based CHM and height estimation using CHM segmentation and local maximum filtering. The results showed that tree heights estimated by the first approach were not significantly different from the insitu data (p=0.298). Furthermore, the heights estimated by the slope corrected shadow length showed higher precision compared to the heights estimated by the shadow length without slope correction (Relative root mean squared error, RRMSE 5.6% and 8.2% respectively). However, the heights obtained from the first approach were less precise than the second approach (RRMSE 5.6% and 4.2% respectively). In general, it was concluded that height estimation of pine trees based on shadow length after correction of slope effects can be considered as a reliable approach, although CHM is more efficient in estimating the tree heights. The findings of this study are applicable for height estimation of pine trees within plantation forests on UAV imagery.

The Applications of Machine Learning to Novel Drug Discovery

Drug discovery is a time-consuming, costly and often off-target discovery pipeline that nonetheless plays a crucial part in medical treatment field. In the past few decades, experimental assays remain the most reliable approach to screen compounds with huge cost. However, many artificial intelligence and machine learning algorithms have been implemented to modernize this field, such as through predicting molecular interactions or properties and analyzing biological data to identify potential drug targets, drug monitoring, and toxicity prediction. In summary, machine learning advancements provide critical support for logical drug design and discovery process, which could finally benefit all of humankind.

MMP-9 EXPRESSION IN ORAL SQUAMOUS CELL CARCINOMA USING IMMUNOHISTOCHEMISTRY, WESTERN BLOTTING, AND REVERSE TRANSCRIPTION POLYMERASE CHAIN REACTION TECHNIQUES

Research into matrix-metalloproteinases (MMP) and their inhibition in certain cancers has moved significantly, studies on MMP expression in oral squamous cell carcinoma (OSCC) remains scarce. MMP-9, a gelatinase-degrading enzyme, destroys matrix proteins in gingivitis and adult periodontitis. MMP-9 genetic (mRNA) and protein profiles were examined to determine MMP-9 expression in oral cancer in this study. Our gender findings indicate that the majority of patients were males and consumers of various tobacco forms.The buccal mucosa was involved in 52% of OSCCs, followed by the tongue (33%), and the gingivobuccal sulcus (9%).The levels of MMP-9 detected in OSCC grades were higher than controls in the order of well-, followed by moderately- and lastly poorly-differentiated grades.Immunohistochemistryseemed to be a reliable approach for measuring MMP-9 expression in OSCC samples when compared to RT-qPCR and western blotting. The association of high MMP-9 levels with well-differentiated OSCC grade implies its potential as a biomarker for early detection of OSCC which could lead to the development of early OSCC treatments. More studies with larger samples might aid in the evaluation of the techniques used.

Developing and Validating Critical-Heutagogy Learning (CHL) to Enhance Lifelong Learning: A Rasch Analysis Approach

This research is dedicated to the development and validation of Critical-Heutagogy Learning (CHL), an innovative pedagogical approach designed to foster lifelong learning among university students. In an era characterized by rapid changes, the imperative for lifelong learning is paramount, necessitating individuals to continually update their knowledge and skills to remain adaptable and successful in diverse environments. To evaluate the validity and reliability of CHL, this study employs Rasch analysis. The development phase of CHL encompassed an exhaustive review of relevant literature, consultations with subject matter experts, and an iterative design methodology. This process was critical to ensure that CHL adhered to established principles of lifelong learning while promoting critical thinking and heutagogical practices. For the validation phase, CHL tools were administered to a heterogeneous group of students across multiple educational contexts. The gathered data underwent rigorous Rasch analysis to ascertain the framework's reliability, evaluate item fit, and pinpoint areas necessitating enhancements. The findings indicate that CHL is both a valid and reliable approach, with a high percentage (89.5%) of student respondents affirming its effectiveness in fostering engagement in learning and enhancing autonomous learning competencies. Additionally, this study delves into the interactions between bias and differential item functioning. The outcomes of this research offer a valuable contribution to the field of education, presenting a rigorously validated framework that supports lifelong learning. This is achieved through the promotion of critical thinking and the facilitation of independent learning skills, thus aligning with contemporary educational objectives and needs.

The therapeutic efficacy and mechanism action of Si Cao formula in the treatment of psoriasis: A pilot clinical investigation and animal validation

Ethnopharmacological relevancePsoriasis is a chronic inflammation and relapsing disease that affected approximately 100 million individuals worldwide. In previous clinical study, it was observed that the topical application of Si Cao Formula (SCF) ameliorated psoriasis skin lesions and reduced the recurrence rate of patients over a period of three months. However, the precise mechanism remains unclear. Aim of the studyThe objective of this study was to assess the effectiveness and safety of SCF in patients diagnosed with psoriasis and explore the molecular mechanisms that contribute to SCF's therapeutic efficacy in psoriasis treatment. Materials and methodsA randomized, controlled, and pilot clinical study was performed. This study assessed 30 individuals diagnosed with mild to moderate plaque psoriasis. 15 of them underwent local SCF treatment, the others received calcipotriol intervention. The outcome measure focused on Psoriasis Area and Severity Index (PASI), Dermatology Life Quality Index (DLQI), and recurrence rate. In addition, IMQ-induced psoriasis-like mice model were used to assess the impact of SCF on ameliorating epidermal hyperplasia, suppressing angiogenesis, and modulating immune response. Furthermore, we performed bioinformatics analysis on transcriptome data obtained from skin lesions of mice model. This analysis allowed us to identify the targets and signaling pathways associated with the action of SCF. Subsequently, we conducted experimental validation to confirm the core targets. ResultsOur clinical pilot study demonstrated that SCF could ameliorate skin lesions in psoriasis patients with comparable efficacy of calcipotriol in drop of PASI and DLQI scores. SCF exhibited a significantly reduced recurrence rate within 12 weeks (33.3%). Liquid Chromatography Mass Spectrometry (LC-MS) identified 41 active constituents of SCF (26 cations and 15 anions). Animal experiments showed SCF ameliorates the skin lesions of IMQ-induced psoriasis like mice model and suppresses epidermal hyperkeratosis and angiogenesis. There were 845 up-regulated and 764 down-regulated DEGs between IMQ and IMQ + SCF groups. GO analysis revealed that DEGs were linked to keratinization, keratinocyte differentiation, organic acid transport epidermal cell differentiation, and carboxylic acid transport interferon-gamma production. KEGG pathway analysis showed that SCF may play a vital part through IL-17 and JAK/STAT signaling pathway. In addition, SCF could reduce the number of positive cells expressing PCNA, CD31, pSTAT3, CD3, and F4/80 within the epidermis of psoriatic lesions, as well as the expression of Il-17a and Stat3 in IMQ-induced psoriasis mice. ConclusionsOur research suggests that SCF serves as a reliable and efficient local approach for preventing and treating psoriasis. The discovery of plausible molecular mechanisms and therapeutic targets associated with SCF may support its broad implementation in clinical settings.

Benzenepoly(carboxylic acid)s as Exclusive Intrinsic Markers to Assess Riverine Export of Dissolved Black Carbon.

Landscape fires annually generate large quantities of black carbon. The water-soluble fraction of black carbon (i.e., dissolved black carbon/DBC) is an important constituent of the dissolved organic carbon (DOC) pool, playing a crucial role in the global budget of refractory carbon and climate change. A key challenge in constraining the flux and fate of riverine DBC is to develop targeted and accurate quantification methods. Herein, we report that benzenepentacarboxylic acid (B5CA) intrinsically present in DBC can be used as an exclusive and holistic marker (representing both condensed aromatics and less-/nonaromatic fractions) for DBC quantification. B5CA was universally detected in water extractions of biochar and fire-affected soils with relatively large abundance but not produced by nonthermogenic processes. It has good mobility in the environment as it is not readily precipitated by cations or adsorbed by common geosorbents. B5CA also represents the recalcitrant components of DBC with excellent stability against photodegradation and biodegradation. Applying B5CA as the DBC marker in surface waters of the Changjiang River (i.e., the third largest river in the world), we calculate the DBC concentration in the downstream Changjiang River to be 4.8 ± 5.5% of the DOC flux. Our work provides a simple and reliable approach for the accurate quantification and source tracking of DBC in the soil and aquatic carbon pools.

Steady-Speed Traffic Capacity Analysis for Autonomous and Human-Driven Vehicles

As the automotive industry transitions towards the era of autonomous vehicles, it is imperative to assess and compare the following distances maintained by vehicles equipped with adaptive cruise control (ACC) systems against those of traditional human-driven vehicles. This study aims to provide insights into the future use of autonomous vehicles by empirically examining the following distances achieved under different driving conditions. Controlled experiments were conducted using three vehicles equipped with various types of ACC sensors, and comparable scenarios were replicated with human drivers. The experiments involved driving at multiple constant speeds to evaluate the efficacy of ACC in maintaining safe following distances. Our findings indicate that ACC systems consistently converge on optimal following distances, demonstrating their ability to regulate spacing between vehicles effectively. However, a notable downside emerged in terms of their adverse impact on road capacities, where the results indicate a mitigation in capacity percentages of 7.6%, 9.3%, and 15.6% for the three types of ACC-equipped vehicles compared to human drivers. This study sheds light on the intricate interplay between ACC systems and human driving behaviors, emphasizing the need to consider both factors when envisioning the future of autonomous vehicles. While ACC systems provide a standardized and reliable approach to following distances, the shorter distances observed in human-driven scenarios suggest a potential trade-off between safety and traffic capacity. These insights contribute to a comprehensive understanding of the dynamics involved in autonomous driving, facilitating informed decision making for the integration of autonomous vehicles into future transportation systems.

An overview of retinal light damage models for preclinical studies on age-related macular degeneration: identifying molecular hallmarks and therapeutic targets.

Age-related macular degeneration (AMD) is a complex, multifactorial disease leading to progressive and irreversible retinal degeneration, whose pathogenesis has not been fully elucidated yet. Due to the complexity and to the multiple features of the disease, many efforts have been made to develop animal models which faithfully reproduce the overall AMD hallmarks or that are able to mimic the different AMD stages. In this context, light damage (LD) rodent models of AMD represent a suitable and reliable approach to mimic the different AMD forms (dry, wet and geographic atrophy) while maintaining the time-dependent progression of the disease. In this review, we comprehensively reported how the LD paradigms reproduce the main features of human AMD. We discuss the capability of these models to broaden the knowledge in AMD research, with a focus on the mechanisms and the molecular hallmarks underlying the pathogenesis of the disease. We also critically revise the remaining challenges and future directions for the use of LD models.

CXCL9 as a Reliable Biomarker for Discriminating Anti-IFN-γ-Autoantibody-Associated Lymphadenopathy that Mimics Lymphoma.

The diagnosis of adult-onset immunodeficiency syndrome associated with neutralizing anti-interferon γ autoantibodies (AIGA) presents substantial challenges to clinicians and pathologists due to its nonspecific clinical presentation, absence of routine laboratory tests, and resemblance to certain lymphoma types, notably nodal T follicular helper cell lymphoma, angioimmunoblastic type (nTFHL-AI). Some patients undergo lymphadenectomy for histopathological examination to rule out lymphoma, even in the absence of a preceding clinical suspicion of AIGA. This study aimed to identify reliable methods to prevent misdiagnosis of AIGA in this scenario through a retrospective case-control analysis of clinical and pathological data, along with immune gene transcriptomes using the NanoString nCounter platform, to compare AIGA and nTFHL-AI. The investigation revealed a downregulation of the C-X-C motif chemokine ligand 9 (CXCL9) gene in AIGA, prompting an exploration of its diagnostic utility. Immunohistochemistry (IHC) targeting CXCL9 was performed on lymph node specimens to assess its potential as a diagnostic biomarker. The findings exhibited a significantly lower density of CXCL9-positive cells in AIGA compared to nTFHL-AI, displaying a high diagnostic accuracy of 92.3% sensitivity and 100% specificity. Furthermore, CXCL9 IHC demonstrated its ability to differentiate AIGA from various lymphomas sharing similar characteristics. In conclusion, CXCL9 IHC emerges as a robust biomarker for differentiating AIGA from nTFHL-AI and other similar conditions. This reliable diagnostic approach holds the potential to avert misdiagnosis of AIGA as lymphoma, providing timely and accurate diagnosis.