Conventional Diagnostic Techniques Research Articles

AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring.

The steady progress in consumer electronics, together with improvement in microflow techniques, nanotechnology, and data processing, has led to implementation of cost-effective, user-friendly portable devices, which play the role of not only gadgets but also diagnostic tools. Moreover, numerous smart devices monitor patients' health, and some of them are applied in point-of-care (PoC) tests as a reliable source of evaluation of a patient's condition. Current diagnostic practices are still based on laboratory tests, preceded by the collection of biological samples, which are then tested in clinical conditions by trained personnel with specialistic equipment. In practice, collecting passive/active physiological and behavioral data from patients in real time and feeding them to artificial intelligence (AI) models can significantly improve the decision process regarding diagnosis and treatment procedures via the omission of conventional sampling and diagnostic procedures while also excluding the role of pathologists. A combination of conventional and novel methods of digital and traditional biomarker detection with portable, autonomous, and miniaturized devices can revolutionize medical diagnostics in the coming years. This article focuses on a comparison of traditional clinical practices with modern diagnostic techniques based on AI and machine learning (ML). The presented technologies will bypass laboratories and start being commercialized, which should lead to improvement or substitution of current diagnostic tools. Their application in PoC settings or as a consumer technology accessible to every patient appears to be a real possibility. Research in this field is expected to intensify in the coming years. Technological advancements in sensors and biosensors are anticipated to enable the continuous real-time analysis of various omics fields, fostering early disease detection and intervention strategies. The integration of AI with digital health platforms would enable predictive analysis and personalized healthcare, emphasizing the importance of interdisciplinary collaboration in related scientific fields.

Hidden Appendicoliths and Their Impact on the Severity and Treatment of Acute Appendicitis.

Background/Objectives: In patients diagnosed with uncomplicated acute appendicitis (UAA), the absence of calcified deposits or stones, called appendicoliths, often leads to consideration of non-operative treatment (NOT), despite the notable treatment failure rate associated with this approach. Previous research has indirectly estimated the prevalence of appendicoliths to range between 15% and 38% retrospectively by CT scan, intraoperative palpation, and pathology report, thereby potentially missing certain concrements. Our hypothesis proposes that this reported prevalence significantly underestimates the occurrence of appendicoliths, which could explain the high failure rate of 29% of patients with appendicitis observed with NOT. Methods: In our prospective study, conducted with a cohort of 56 adult patients diagnosed with acute appendicitis (AA), we employed intraoperative extracorporeal incisions of the vermiform appendix, in addition to standard diagnostic methods. Results: Our findings revealed 50% more appendicoliths by intraoperative incision (n = 36, p < 0.001) compared to preoperative imaging (n = 24). Appendicoliths were present in 71.4% (n = 40, p < 0.001) of AA patients. Conclusions: These results suggest that conventional diagnostic procedures plausibly underestimate the actual prevalence of appendicoliths, potentially elucidating the frequent treatment failures observed in NOT approaches applied to patients with UAA.

Deep and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis: A Comprehensive Review

The medical condition known as acute lymphoblastic leukemia (ALL) is characterized by an excess of immature lymphocyte production, and it can affect people across all age ranges. Detecting it at an early stage is extremely important to increase the chances of successful treatment. Conventional diagnostic techniques for ALL, such as bone marrow and blood tests, can be expensive and time-consuming. They may be less useful in places with scarce resources. The primary objective of this research is to investigate automated techniques that can be employed to detect ALL at an early stage. This analysis covers both machine learning models (ML), such as support vector machine (SVM) &amp; random forest (RF), as well as deep learning algorithms (DL), including convolution neural network (CNN), AlexNet, ResNet50, ShuffleNet, MobileNet, RNN. The effectiveness of these models in detecting ALL is evident through their ability to enhance accuracy and minimize human errors, which is essential for early diagnosis and successful treatment. In addition, the study also highlights several challenges and limitations in this field, including the scarcity of data available for ALL types, and the significant computational resources required to train and operate deep learning models.

Integrating Machine Learning Algorithms and Advanced Computing Technology Using an Ensemble Hybrid Classifier

Ovarian Cancer (OC) is one of the major types of cancers in women worldwide. Despite the standardization of characteristics that can help distinguish benign from malignant ovarian masses, accurate predictive modelling following ultrasound (US) examination and biomarkers for ’progression-free survival’ is lacking in the field of ovarian cancer. Important leading factors in ovarian cancer lethality are the lack of diagnostic procedures and proper screening to detect early-stage ovarian cancer, and the rapid spread of the disease over the surface of the peritoneum. Therefore, developing tools for accurate screening and prognosis, as well as the diagnosis of early stage ovarian cancer, is a current clinical need. In this study, an ensemble classifier was developed as a novel means of ovarian cancer prediction, and its effectiveness was assessed. The ensemble classifier integrates various machine learning algorithms, including support vector machines (SVM), k-nearest neighbors (KNN), decision trees (DT), naïve Bayes (NB), and logistic regression (LR). Because ensembles may integrate the benefits of numerous models, they can mitigate the limitations of each model individually and improve the overall predictive performance, making them popular in the domain of machine learning. To increase predictive performance, an ensemble hybrid approach was created by utilizing a meta-classifier to merge many base classifiers. The performance with respect to various measures of the ensemble classifier was evaluated considering a comprehensive novel dataset of ovarian cancer patients, including tumor markers as well as clinical and ultrasound features. Through extensive cross-validation studies, the hybrid model showed better prediction accuracy of 95% which is approximately 6-17% improved than the baseline classifiers and state-of-the-art ensemble approaches in predicting ovarian cancer. After comparing the performance of the ensemble classifier with other existing classifiers, the ensemble classifier outperformed the individual models and conventional diagnostic techniques in terms of sensitivity (94%) and specificity (95%) through performance evaluation.

ENHANCE SKIN CANCER DEEP LEARNING-BASED CATEGORIZATION: A THOROUGH EXAMINATION AND COMPARATIVE STUDY

Abstract: One of the most prevalent types of cancer in the world is skin cancer, which includes melanoma, squamous cell carcinoma, and basal cell carcinoma. While increasing survival rates requires early discovery and treatment, conventional diagnostic techniques frequently have subjectivity and inconsistent results. The purpose of this study is to better understand how deep learning techniques, namely convolutional neural networks (CNNs), might improve the detection of skin cancer. Using the ISIC dataset, eight advanced models—DenseNet121, InceptionV3, ResNet50V2, VGG16, VGG19, InceptionResNetV2, Xception, and CNN—were trained and verified to categorize different types of skin lesions. The outcomes highlight the superior accuracy that models like as Xception and InceptionV3 can accomplish, highlighting their potential for accurate and timely detection. This investigation assesses how well different deep learning models diagnose different skin diseases. Models like Densenet121, InceptionV3, and VGG16 are compared based on metrics like accuracy, recall, precision, and F1-score. When it comes to multi-classification, InceptionResNetV2 and VGG16 stand out as the best performers with 0.9547 accuracy, while Custom CNN performs the worst with 0.8040. A thorough comparison of the accuracy, precision, recall, and F1-score performance indicators identifies the unique advantages and disadvantages of each model. This study demonstrates how artificial intelligence (AI) is revolutionizing medical imaging by offering a reliable, unbiased, and highly accurate method for detecting skin cancer. The integration of deep learning models into clinical practice is made possible by these discoveries, which might enhance patient outcomes and diagnostic accuracy in dermatology. IndexTerms – Python, Machine Learning, Deep Learning, Skin Cancer, Deep Learning Algorithms, Medical Imaging, Neural Network Models.

Artificial intelligence and personalized diagnostics in periodontology: A narrative review.

Periodontal diseases pose a significant global health burden, requiring early detection and personalized treatment approaches. Traditional diagnostic approaches in periodontology often rely on a "one size fits all" approach, which may overlook the unique variations in disease progression and response to treatment among individuals. This narrative review explores the role of artificial intelligence (AI) and personalized diagnostics in periodontology, emphasizing the potential for tailored diagnostic strategies to enhance precision medicine in periodontal care. The review begins by elucidating the limitations of conventional diagnostic techniques. Subsequently, it delves into the application of AI models in analyzing diverse data sets, such as clinical records, imaging, and molecular information, and its role in periodontal training. Furthermore, the review also discusses the role of research community and policymakers in integrating personalized diagnostics in periodontal care. Challenges and ethical considerations associated with adopting AI-based personalized diagnostic tools are also explored, emphasizing the need for transparent algorithms, data safety and privacy, ongoing multidisciplinary collaboration, and patient involvement. In conclusion, this narrative review underscores the transformative potential of AI in advancing periodontal diagnostics toward a personalized paradigm, and their integration into clinical practice holds the promise of ushering in a new era of precision medicine for periodontal care.

Prostate Cancer: MRI Image Detection Based on Deep Learning: A Review

This comprehensive study delves into the transformative role of artificial intelligence (AI) and deep learning (DL) in the realm of prostate cancer care, an issue of paramount importance in men’s health worldwide. Prostate cancer, marked by the unchecked growth of cells in the prostate gland, poses risks of tumor formation and eventual metastasis. The crux of combating this disease lies in its early detection and precise diagnosis, for which traditional screening methodologies like Prostate-Specific Antigen (PSA) tests and multiparametric Magnetic Resonance Imaging (mp-MRI) are fundamental. The introduction of AI and DL into these diagnostic avenues has been nothing short of revolutionary, enhancing the precision of medical imaging and significantly reducing the rates of unnecessary biopsies. The advancements in DL, particularly through the use of convolutional neural networks (CNNs) and the application of multiparametric MRI, have been instrumental in improving the accuracy of diagnoses, foreseeing the progression of the disease, and tailoring individualized treatment regimens. This paper meticulously examines various DL models and their successful application in the detection, classification, and segmentation of prostate cancer, establishing their superiority over conventional diagnostic techniques. Despite the promising horizon these technologies offer, their implementation is not without challenges. The requisite for specialized expertise to handle these advanced tools and the ethical dilemmas they present, such as data privacy and potential biases, are significant hurdles. Nevertheless, the potential of AI and DL to inaugurate a new chapter in prostate cancer management is undeniable. The emphasis on interdisciplinary collaboration among scientists, clinicians, and technologists is crucial for pushing the boundaries of current research and clinical practice, ensuring the ethical deployment of AI and DL technologies. This collaborative effort is vital for realizing the full potential of these innovations in providing more accurate, efficient, and patient-centric care in the fight against prostate cancer, heralding a future where the burden of this disease is significantly mitigated.

Clinical application value of metagenome next-generation sequencing in pulmonary diffuse exudative lesions: a retrospective study.

This study aims to investigate the clinical application value of Metagenome Next-Generation Sequencing (mNGS) for pulmonary diffuse exudative lesions. From January 1, 2014, to November 31, 2021, 136 cases with chest radiologic presentations of pulmonary diffuse exudative lesions admitted to Fujian Provincial Hospital were included in the study; of those, 77 patients underwent mNGS pathogen detection. Based on the pathogen detection outcomes and clinical diagnoses, patients were categorized into an infection group (IG) and a non-infection group (NIG). A comparison was made between the diagnostic efficacy of the mNGS technique and traditional culture methods. Meanwhile, 59 patients clinically identified as having infectious pulmonary diffuse exudative lesions but who did not receive mNGS testing were designated as the non-NGS infection group (non-IG). A retrospective cohort study was conducted on patients in both the IG and non-IG, with a 30-day all-cause mortality endpoint used for follow-up. When compared to conventional culture methods, mNGS demonstrated an approximate 35% increase in sensitivity (80.0% vs 45.5%, P<0.001), without significant disparity in specificity (77.3% vs 95.5%, P=0.185). Under antibiotic exposure, the positivity rate detected by mNGS was notably higher than that by traditional culture methods, indicating that mNGS is less affected by exposure to antibiotics (P<0.05). Within 30 days, the all-cause mortality rate for patients in the IG versus the non-IG was 14.55% and 37.29%, respectively (P<0.05). Following a COX regression analysis to adjust for confounding factors, the analysis revealed that a CURB-65 score ≥3 points (HR=3.348, P=0.001) and existing cardiovascular disease (HR=2.473, P=0.026) were independent risk factors for these patients. Conversely, mNGS testing (HR=0.368, P=0.017) proved to be an independent protective factor. mNGS technology makes it easier to pinpoint the cause of pulmonary diffuse infectious exudative lesions without much interference from antibiotics, helping doctors spot and diagnose these issues early on, thereby playing a key role in helping them decide the best treatment approach for patients. Such conclusions may have a bias, as the performance of traditional methods might be underestimated due to the absence of complete results from other conventional diagnostic techniques like serological testing and PCR.

Protein biomarker detection via differential dynamic microscopy

We demonstrate a pioneering application of differential dynamic microscopy (DDM) for disease diagnosis by monitoring the dynamic signal of probe beads. When the target antibody binds to our sensitized probe beads, it allows these originally non-fluorescent beads to be indirectly decorated with fluorescently labeled secondary antibodies. Using standard epi-fluorescence microscopy without the need for purification, DDM reliably detects the resulting increase in fluorescence signal from the probe beads. It effectively discerns this signal from the background, which includes residual free fluorescent secondary antibodies. As a proof-of-concept, polystyrene beads (500 nm in diameter) were conjugated with the receptor binding domain of SARS-CoV-2’s spike protein to quantify immunoglobulin G1-specific antibodies against it in three different media of increasing complexity (phosphate buffer, model saliva and fetal bovine serum). Our results show that as the medium becomes more complex, more dilution and/or longer incubation time may be needed to overcome the competition effect between the target and interfering proteins. By optimizing imaging parameters and sample preparation we could surpass clinically relevant limits of detection in both saliva and serum samples. In contrast to conventional diagnostic techniques including ELISA and PCR, DDM brings many advantages including simplicity of the preparation protocol, high speed, and versatility without requiring very expensive or complicated equipment. As such, DDM-based disease diagnostics has the potential to be utilized in many laboratories, especially in remote regions with limited resources and point-of-use applications may also be feasible by miniaturizing the imaging setup.

SPR and Double Resonance LPG Biosensors for Helicobacter pylori BabA Antigen Detection.

Given the medical and social significance of Helicobacter pylori infection, timely and reliable diagnosis of the disease is required. The traditional invasive and non-invasive conventional diagnostic techniques have several limitations. Recently, opportunities for new diagnostic methods have appeared based on the recent advance in the study of H. pylori outer membrane proteins and their identified receptors. In the present study we assess the way in which outer membrane protein-cell receptor reactions are applicable in establishing a reliable diagnosis. Herein, as well as in other previous studies of ours, we explore the reliability of the binding reaction between the best characterized H. pylori adhesin BabA and its receptor, the blood antigen Leb. For the purpose we developed surface plasmon resonance (SPR) and double resonance long period grating (DR LPG) biosensors based on the BabA-Leb binding reaction for diagnosing H. pylori infection. In SPR detection, the sensitivity was estimated at 3000 CFU/mL-a much higher sensitivity than that of the RUT test. The DR LPG biosensor proved to be superior in terms of accuracy and sensitivity-concentrations as low as 102 CFU/mL were detected.

Performance of CBNAAT on Pleural Biopsies Obtained by Semirigid Thoracoscopy for Diagnosis of Unexplained Pleural Effusion: A Prospective Study from North India.

Exudative pleural effusions are commonly encountered in clinical practice, but in about one-fourth of cases, etiology remains elusive after initial evaluation. Medical thoracoscopy with semirigid thoracoscope is a minimally invasive procedure with high diagnostic yield for diagnosing pleural diseases, especially these undiagnosed exudative pleural effusions. In tubercular endemic areas, often, these effusions turn out to be tubercular, but the diagnosis of tubercular pleural effusion is quite challenging due to the paucibacillary nature of the disease. Although culture is the gold standard, it is time-consuming. Cartridge-based nucleic acid amplification test (CBNAAT) is a novel rapid diagnostic test for tuberculosis (TB) and has been recommended as the initial diagnostic test in patients suspected of having extrapulmonary TB (EPTB). We conducted a prospective observational study of 50 patients with undiagnosed pleural effusion admitted to our tertiary care hospital. The primary aim of the study is to evaluate the diagnostic performance of CBNAAT on thoracoscopic guided pleural biopsy and compare it with conventional diagnostic techniques like histopathology and conventional culture. Of 50 undiagnosed pleural effusions, TB (50%) was the most common etiology. The overall diagnostic yield of semirigid thoracoscopy in this study was 74%. Our study showed that CBNAAT of pleural biopsies had a sensitivity of 36% only but a specificity of 100%. The sensitivity of CBNAAT was not far superior to the conventional culture. Tuberculosis (TB) is a common cause of undiagnosed pleural effusion in our set-up. CBNAAT testing of pleural biopsy, though, is a poor rule-out test for pleural TB, but it may aid in the early diagnosis of such patients.

Recent Advances in Biosensors for Diagnosis of Autoimmune Diseases.

Over the last decade, autoimmune diseases (ADs) have undergone a significant increase because of genetic and/or environmental factors; therefore, their simple and fast diagnosis is of high importance. The conventional diagnostic techniques for ADs require tedious sample preparation, sophisticated instruments, a dedicated laboratory, and qualified personnel. For these reasons, biosensors could represent a useful alternative to these methods. Biosensors are considered to be promising tools that can be used in clinical analysis for an early diagnosis due to their high sensitivity, simplicity, low cost, possible miniaturization (POCT), and potential ability for real-time analysis. In this review, recently developed biosensors for the detection of autoimmune disease biomarkers are discussed. In the first part, we focus on the main AD biomarkers and the current methods of their detection. Then, we discuss the principles and different types of biosensors. Finally, we overview the characteristics of biosensors based on different bioreceptors reported in the literature.

A novel approach for early gastric cancer detection using a hybrid of alexnet, extreme learning machine, and adjusted gorilla troops optimization

Gastric cancer is a prevalent and highly lethal form of cancer that affects individuals worldwide. The early detection of gastric cancer plays a vital role in ensuring successful treatment and improved patient outcomes. However, identifying this malignancy at an early stage can be challenging, and conventional diagnostic techniques often exhibit limited accuracy. To address this issue, a groundbreaking study proposes a novel methodology that combines the utilization of the Alexandria Net (Alexnet), Extreme Learning Machine (ELM), and a modified metaheuristic algorithm. The integration of these techniques aims to enhance the accuracy and efficiency of gastric cancer detection. Additionally, a Modified version of the Dynamic Gorilla Troops Optimizer (DGTO) is incorporated as a dynamic metaheuristic algorithm. This algorithm optimizes the arrangement and selection of features within the model, significantly contributing to improved performance. To evaluate the effectiveness of the proposed methodology, the Alexnet/ELM-DGTO model is applied to a practical dataset known as GasHisSDB. Moreover, this approach outperforms several state-of-the-art methods in terms of precision, accuracy, recall, specificity, F1-score, and Area Under the Curve (AUC). This research paper makes a significant contribution to the field of early gastric cancer detection by introducing a novel methodology that overcomes the limitations of existing approaches. It offers a unique solution to the optimization problem of selecting the most suitable features for machine learning models. The effectiveness of the proposed methodology is further validated through its successful application on a practical dataset, highlighting its superiority over alternative methods. The results underscore the potential impact of utilizing the Alexnet/ELM-DGTO combination in the field of gastric cancer detection.

Idiopathic Portal Hypertension: A Case Report of an Adult Indian Rural Female of “Banti Syndrome”

Non-cirrhotic portal fibrosis (NCPF)/idiopathic portal fibrotic hypertension (IPH) is one of the major diseases that include a group of diseases caused by increased portal venous pressure which is thought to be due to intrahepatic or prehepatic lesions in the absence of liver cirrhosis. In the present case a 35-year-old female presented with abdominal fullness and pain in the upper abdomen for the past two months which was not associated with the consumption of water or food, nausea, or vomiting. When we investigated it was seen that she had severe anemia with pancytopenia with no malarial parasite on peripheral smear, liver renal and liver function tests were within the normal range. The diagnosis of Banti’s syndrome was made. However, the patient was then vitally stable and was started on propranolol and was advised for splenectomy due to refractory anemia. The present study provides easy and conventional diagnostic techniques for clinical diagnosis and proves the radiological technique.

Adaptive Method for Exploring Deep Learning Techniques for Subtyping and Prediction of Liver Disease

The term “Liver disease” refers to a broad category of disorders affecting the liver. There are a variety of common liver ailments, such as hepatitis, cirrhosis, and liver cancer. Accurate and early diagnosis is an emergent demand for the prediction and diagnosis of liver disease. Conventional diagnostic techniques, such as radiological, CT scan, and liver function tests, are often time-consuming and prone to inaccuracies in several cases. An application of machine learning (ML) and deep learning (DL) techniques is an efficient approach to diagnosing diseases in a wide range of medical fields. This type of machine-related learning can handle various tasks, such as image recognition, analysis, and classification, because it helps train large datasets and learns to identify patterns that might not be perceived by humans. This paper is presented here with an evaluation of the performance of various DL models on the estimation and subtyping of liver ailment and prognosis. In this manuscript, we propose a novel approach, termed CNN+LSTM, which is an integration of convolutional neural network (CNN) and long short-term memory (LSTM) networks. The results of the study prove that ML and DL can be used to improve the diagnosis and prognosis of liver disease. The CNN+LSTM model achieves a better accuracy of 98.73% compared to other models such as CNN, Recurrent Neural Network (RNN), and LSTM. The incorporation of the proposed CNN+LSTM model has better results in terms of accuracy (98.73%), precision (99%), recall (98%), F1 score (98%), and AUC (Area Under the Curve)-ROC (Receiver Operating Characteristic) (99%), respectively. The use of the CNN+LSTM model shows robustness in predicting the liver ailment with an accurate diagnosis and prognosis.

Assessment of valve regurgitation severity via contrastive learning and multi-view video integration

Objective. This paper presents a novel approach for addressing the intricate task of diagnosing aortic valve regurgitation (AR), a valvular disease characterized by blood leakage due to incompetence of the valve closure. Conventional diagnostic techniques require detailed evaluations of multi-modal clinical data, frequently resulting in labor-intensive and time-consuming procedures that are vulnerable to varying subjective assessment of regurgitation severity. Approach. In our research, we introduce the multi-view video contrastive network, designed to leverage multiple color Doppler imaging inputs for multi-view video processing. We leverage supervised contrastive learning as a strategic approach to tackle class imbalance and enhance the effectiveness of our feature representation learning. Specifically, we introduce a contrastive learning framework to enhance representation learning within the embedding space through inter-patient and intra-patient contrastive loss terms. Main results. We conducted extensive experiments using an in-house dataset comprising 250 echocardiography video series. Our results exhibit a substantial improvement in diagnostic accuracy for AR compared to state-of-the-art methods in terms of accuracy by 9.60%, precision by 8.67%, recall by 9.01%, and F 1-score by 8.92%. These results emphasize the capacity of our approach to provide a more precise and efficient method for evaluating the severity of AR. Significance. The proposed model could quickly and accurately make decisions about the severity of AR, potentially serving as a useful prescreening tool.

Multi-Convolutional Neural Network-Based Diagnostic Software for the Presumptive Determination of Non-Dermatophyte Molds

Based on the literature data, the incidence of superficial and invasive non-dermatophyte mold infection (NDMI) has increased. Many of these infections are undiagnosed or misdiagnosed, thus causing inadequate treatment procedures followed by critical conditions or even mortality of the patients. Accurate diagnosis of these infections requires complex mycological analyses and operator skills, but simple, fast, and more efficient mycological tests are still required to overcome the limitations of conventional fungal diagnostic procedures. In this study, software has been developed to provide an efficient mycological diagnosis using a trained convolutional neural network (CNN) model as a core classifier. Using EfficientNet-B2 architecture and permanent slides of NDM isolated from patient’s materials (personal archive of Prof. Otašević, Department of Microbiology and Immunology, Medical Faculty, University of Niš, Serbia), a multi-CNN model has been trained and then integrated into the diagnostic tool, with a 93.73% accuracy of the main model. The Grad-CAM visualization model has been used for further validation of the pattern recognition of the model. The software, which makes the final diagnosis based on the rule of the major method, has been tested with images provided by different European laboratories, showing an almost faultless accuracy with different test images.

Advances and Challenges in Wearable Glaucoma Diagnostics and Therapeutics.

Glaucoma is a leading cause of irreversible blindness, and early detection and treatment are crucial for preventing vision loss. This review aims to provide an overview of current diagnostic and treatment standards, recent medical and technological advances, and current challenges and future outlook for wearable glaucoma diagnostics and therapeutics. Conventional diagnostic techniques, including the rebound tonometer and Goldmann Applanation Tonometer, provide reliable intraocular pressure (IOP) measurement data at single-interval visits. The Sensimed Triggerfish and other emerging contact lenses provide continuous IOP tracking, which can improve diagnostic IOP monitoring for glaucoma. Conventional therapeutic techniques include eye drops and laser therapies, while emerging drug-eluting contact lenses can solve patient noncompliance with eye medications. Theranostic platforms combine diagnostic and therapeutic capabilities into a single device. Advantages of these platforms include real-time monitoring and personalized medication dosing. While there are many challenges to the development of wearable glaucoma diagnostics and therapeutics, wearable technologies hold great potential for enhancing glaucoma management by providing continuous monitoring, improving medication adherence, and reducing the disease burden on patients and healthcare systems. Further research and development of these technologies will be essential to optimizing patient outcomes.

Machine learning approaches for early detection of non-alcoholic steatohepatitis based on clinical and blood parameters

This study aims to develop a machine learning approach leveraging clinical data and blood parameters to predict non-alcoholic steatohepatitis (NASH) based on the NAFLD Activity Score (NAS). Using a dataset of 181 patients, we performed preprocessing including normalization and categorical encoding. To identify predictive features, we applied sequential forward selection (SFS), chi-square, analysis of variance (ANOVA), and mutual information (MI). The selected features were used to train machine learning classifiers including SVM, random forest, AdaBoost, LightGBM, and XGBoost. Hyperparameter tuning was done for each classifier using randomized search. Model evaluation was performed using leave-one-out cross-validation over 100 repetitions. Among the classifiers, random forest, combined with SFS feature selection and 10 features, obtained the best performance: Accuracy: 81.32% ± 6.43%, Sensitivity: 86.04% ± 6.21%, Specificity: 70.49% ± 8.12% Precision: 81.59% ± 6.23%, and F1-score: 83.75% ± 6.23% percent. Our findings highlight the promise of machine learning in enhancing early diagnosis of NASH and provide a compelling alternative to conventional diagnostic techniques. Consequently, this study highlights the promise of machine learning techniques in enhancing early and non-invasive diagnosis of NASH based on readily available clinical and blood data. Our findings provide the basis for developing scalable approaches that can improve screening and monitoring of NASH progression.

Prospects, advances and biological applications of MOF-based platform for the treatment of lung cancer.

Nowadays in our society, lung cancer is exhibiting a high mortality rate and threat to human health. Conventional diagnostic techniques used in the field of lung cancer often necessitate the use of extensive instrumentation, exhibit a tendency for false positives, and are not suitable for widespread early screening purposes. Conventional approaches to treat lung cancer primarily involve surgery, chemotherapy, and radiotherapy. However, these broad-spectrum treatments suffer from drawbacks such as imprecise targeting and significant side effects, which restrict their widespread use. Metal-organic frameworks (MOFs) have attracted significant attention in the diagnosis and treatment of lung cancer owing to their tunable electronic properties and structures and potential applications. These porous nanomaterials are formed through the intricate assembly of metal centers and organic ligands, resulting in highly versatile frameworks. Compared to traditional diagnostic and therapeutic modalities, MOFs can improve the sensitivity of lung cancer biomarker detection in the diagnosis of lung cancer. In terms of treatment, they can significantly reduce side effects and improve therapeutic efficacy. Hence, this perspective provides an overview concerning the advancements made in the field of MOFs as potent biosensors for lung cancer biomarkers. It also delves into the latest research dealing with the use of MOFs as carriers for drug delivery. Additionally, it explores the applications of MOFs in various therapeutic approaches, including chemodynamic therapy, photodynamic therapy, photothermal therapy, and immunotherapy. Furthermore, this review comprehensively analyses potential applications of MOFs as biosensors in the field of lung cancer diagnosis and combines different therapeutic approaches aiming for enhanced therapeutic efficacy. It also presents a concise overview of the existing obstacles, aiming to pave the way for future advancements in lung cancer diagnosis and treatment.