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The early diagnosis of hepatocellular carcinomas (HCCs) remains challenging in the clinic. Primovist‐enhanced magnetic resonance imaging (MRI) aids HCC diagnosis but loses sensitivity for tumors <2 cm. Therefore, developing advanced MRI contrast agents is imperative for improving the diagnostic accuracy of HCCs in very‐early‐stage. To address this challenge, PEGylated ultra‐small iron oxide nanoparticles (PUSIONPs) are synthesized and employed as liver‐specific T1 MRI contrast agents. Intravenous delivery produces simultaneous hyperintense HCC and hypointense hepatic parenchyma signals on T1 imaging, creating an extraordinarily high tumor‐to‐liver contrast. Systematic studies uncover PUSIONP distribution in hepatic parenchyma, HCC lesions at the organ, tissue, cellular, and subcellular levels, revealing endosomal confinement of PUSIONP without aggregation. By mimicking such situations, the dependency of relaxometric properties on local PUSIONP concentration is investigated, emphasizing the key role of different endosomal concentrations in liver and tumor cells for high tumor‐to‐liver contrast and clear tumor boundaries. These findings offer exceptional imaging capabilities for early HCC diagnosis, potentially benefiting real HCC patients. [ABSTRACT FROM AUTHOR]

PurposeMethodsResultsConclusionsTo describe the application of artificial intelligence (AI) in ophthalmic diseases and its possible future directions.A retrospective review of the literature from PubMed, Web of Science, and Embase databases (2019–2024).AI assists in cataract diagnosis, classification, preoperative lens calculation, surgical risk, postoperative vision prediction, and follow-up. For glaucoma, AI enhances early diagnosis, progression prediction, and surgical risk assessment. It detects diabetic retinopathy early and predicts treatment effects for diabetic macular edema. AI analyzes fundus images for age-related macular degeneration (AMD) diagnosis and risk prediction. Additionally, AI quantifies and grades vitreous opacities in uveitis. For retinopathy of prematurity, AI facilitates disease classification, predicting disease occurrence and severity. Recently, AI also predicts systemic diseases by analyzing fundus vascular changes.AI has been extensively used in diagnosing, following up, and predicting treatment outcomes for common blinding eye diseases. In addition, it also has a unique role in the prediction of systemic diseases. [ABSTRACT FROM AUTHOR]

Introduction: The subarachnoid space in the brain contains crucial blood vessels and cerebrospinal fluid. Aneurysms in these vessels can lead to subarachnoid hemorrhage (SAH), a serious stroke subtype with high morbidity and mortality rates. SAH treatment includes procedures like coiling and clipping, but these are available only at comprehensive stroke centers (CSCs), necessitating urgent diagnosis and transfer to specialized facilities. Methods: This IRB-approved study was conducted by Polk County Fire Rescue (PCFR) in Florida. PCFR, serving an 850,000-person population, implemented a three-step SAH protocol. The protocol uses both Ottawa SAH criteria and recurring symptoms, such as new-onset seizures and high systolic blood pressure, that were identified by EMS. Acute management included administering labetalol, levetiracetam, and ondansetron. Results: Of 2175 stroke patients, 80 screened positive for SAH and were eligible for transfer. Patients had a median age of 66, and 33% had an initial systolic BP over 220 mmHg. The interfacility transfer rate dropped from 12.9 to 3.6% after implementing the protocol. Conclusion: The PCFR protocol's effectiveness suggests its potential for nationwide implementation. Early SAH recognition and prompt transfer to CSCs reduce complications and improve outcomes. Accurate field diagnosis by EMTs can prevent unnecessary transfers and enhance patient care. Future improvements may include portable diagnostic tools and enhanced EMT training to further improve SAH patients' pre-hospital care. [ABSTRACT FROM AUTHOR]

Background: In addition to the great challenge of early diagnosis and prognosis in breast cancer (BC), the role of gene promoters in BC remains largely unexplored. This study aimed to evaluate aldo-keto reductase family 1 member B1 (AKR1B1) methylation as noninvasive biomarker for early BC diagnosis. Methods: A total of 200 (120 with BC, 40 with benign breast diseases, 40 healthy) Egyptian women were enrolled. AKR1B1 methylation level was determined using EpiTect Methyl II QPCR assay quantitative polymerase chain reaction. Results: Findings revealed that hypermethylation AKR1B1 was reported to be associated (P <.0001) with BC cases (93.2 [75.4-98.6]) compared with benign (23.9 [22.6-48.3]) or healthy (15.5 [10.6-16]) controls. It had a great diagnostic power (area under the curve [AUC] = 0.909) that was superior to cancer antigen (CA) 15-3 (AUC = 0.681) and carcinoembryonic antigen (CEA) (AUC = 0.539). Interestingly, AKR1B1 hypermethylation was reported to be significant in identifying BC early stages (AUC = 0.899) and grades (AUC = 0.903). Independent to hormonal status and HER2neu expression, AKR1B1 hypermethylation was related to some tumor severity features, including advanced stages, high histological grades, and lymph node invasion. Also, AKR1B1 high degrees of methylation were significantly correlated with the increase in CEA (r =.195; P =.027), CA-15.3 (r =.351; P =.0001) and tumor stages (r =.274; P =.014), grades (r =.253; P =.024), and lymph node invasion (r =.275; P =.014). Conclusions: This study revealed that aberrant AKR1B1 methylation could facilitate early BC detection from benign br0east disorders. Hypermethylated AKR1B1 was related to BC aggressiveness suggesting its potential role as diagnostic and prognostic BC biomarker. [ABSTRACT FROM AUTHOR]

Early diagnosis of inborn errors of immunity (IEIs) has been shown to reduce mortality, morbidity, and healthcare costs. The need for early diagnosis has led to the development of computational tools that trigger earlier clinical suspicion by physicians. Primary care professionals serve as the first line for improving early diagnosis. To this end, a computer-based tool (based on extended Jeffrey Modell Foundation (JMF) Warning Signs) was developed to assist physicians with diagnosis decisions for IEIs in the primary care setting. Two expert-guided scoring systems (one pediatric, one adult) were developed. IEI warning signs were identified and a panel of 36 experts reached a consensus on which signs to include and how they should be weighted. The resulting scoring system was tested against a retrospective registry of patients with confirmed IEI using primary care EHRs. A pilot study to assess the feasibility of implementation in primary care was conducted. The scoring system includes 27 warning signs for pediatric patients and 24 for adults, adding additional clinically relevant criteria established by expert consensus to the JMF Warning Signs. Cytopenias, ≥ 2 systemic infections, recurrent fever and bronchiectasis were the leading warning signs in children, as bronchiectasis, autoimmune diseases, cytopenias, and > 3 pneumonias were in adults. The PIDCAP (Primary Immune Deficiency "Centre d'Atenció Primària" that stands for Primary Care Center in Catalan) tool was implemented in the primary care workstation in a pilot area. The expert-based approach has the potential to lessen under-reporting and minimize diagnostic delays of IEIs. It can be seamlessly integrated into clinical primary care workstations. [ABSTRACT FROM AUTHOR]

Introduction: Cardiac amyloidosis is a rarely reported and potentially fatal variant of the systemic disease. Its early diagnosis could potentially lead to significantly improved clinical outcomes. Case presentation: A 56-year-old female presented with dyspnea and palpitations. Her physical exam and non-invasive evaluation with cardiac magnetic resonance imaging (CMRI) revealed restrictive cardiomyopathy, and the bone marrow biopsy results showed systemic amyloidosis. Discussion: The diagnosis of cardiac amyloidosis is not always straightforward, and delay can cause the progression of the disease and an increased risk of morbidity and mortality. Electrocardiograms, echocardiograms, cardiac magnetic resonance imaging, and histopathologic evaluation are the main methods for diagnosing cardiac amyloidosis. The treatment consists of controlling heart failure symptoms and disease-modifying interventions, including medical and surgical therapeutic methods. Clinical learning point (conclusion): Cardiac involvement is the main cause of death in systemic amyloidosis. Early suspicion, diagnosis, and treatment are crucial in improving patients' survival. CMRI can play an essential role in the diagnosis of cardiac Amyloidosis. A graphical abstract is provided for visual summary. [ABSTRACT FROM AUTHOR]

Aim Method Results Conclusion Preeclampsia (PE) is a critical and severe disease in obstetrics, which seriously affects maternal and neonatal life safety and long‐term prognosis. However, the etiology and pathogenesis of PE are complex, and no unified conclusion has been reached. The types and number of exosomes and their transport substances in PE patients changed. The study of exosomes in PE patients helps clarify the etiology, diagnosis, effective treatment, accurate monitoring, and prognosis.The published articles were reviewed.Exosomes may affect endothelial and vascular production and function, participate in maternal‐fetal immune regulation, and transport substances such as miRNAs, lncRNAs, and proteins involved in the development of PE. Detection of the contents of exosomes can help in the early diagnosis of PE, and can help to improve PE by inhibiting the action of exosomes or preventing their binding to target organs.Exosomes may be involved in the development of PE, and exosomes can be used as markers for predicting the onset of PE and tracking the disease process and determining the prognosis, and exosomes have great potential in the treatment of PE. [ABSTRACT FROM AUTHOR]

Cell sorting holds broad applications in fields such as early cancer diagnosis, cell differentiation studies, drug screening, and single-cell sequencing. However, achieving high-throughput and high-purity in label-free single-cell sorting is challenging. To overcome this issue, we propose a label-free, high-throughput, and high-accuracy impedance-activated cell sorting system based on impedance detection and dual membrane pumps. Leveraging the low-latency characteristics of FPGA, the system facilitates real-time dual-frequency single-cell impedance detection with high-throughput (5 × 104 cells per s) for HeLa, MDA-MB-231, and Jurkat cells. Furthermore, the system accomplishes low-latency (less than 0.3 ms), label-free, high-throughput (1000 particles per s) and high-accuracy (almost 99%) single-particle sorting using FPGA-based high-precision sort-timing prediction. In experiments with Jurkat and MDA-MB-231 cells, the system achieved a throughput of up to 1000 cells per s, maintaining a pre-sorting purity of 28.57% and increasing post-sorting purity to 97.09%. These findings indicate that our system holds significant potential for applications in label-free, high-throughput cell sorting. [ABSTRACT FROM AUTHOR]

Alterations in pH are a hallmark in several pathologies including cancer, ischemia, and inflammation. Non-invasive magnetic resonance methods to measure pH offer a new approach for early diagnosis of diseases characterized by acid–base imbalances. The hyperpolarization with parahydrogen-induced polarization (PHIP) enhances inherently low signals in magnetic resonance experiments by several orders of magnitude and offers a suitable platform to obtain biocompatible markers in less than one minute. Here, we present an optimized preparation of an hyperpolarized H13CO3−/13CO2 pH sensor via non-enzymatic decarboxylation with H2O2 of [1-13C]pyruvate-d3 obtained by PHIP at 7 T. An improved 13C polarization of purified [1-13C]pyruvate-d3 in water with 36.65 ± 0.06% polarization was obtained starting from 50 mM precursor. Subsequent decarboxylation, H13CO3−/13CO2 exhibited 12.46 ± 0.01% of polarization at physiological pH, 45 seconds after the reaction start. Considering the dilution factor that [1-13C]pyruvate-d3 exhibits in vivo, we optimized our methodology to test the accuracy of the pH sensor at single digit millimolar concentration. In vitro pH estimations on phantoms and cell culture media demonstrated accurate pH calculations with uncertainties of less than 0.08 units. These promising results highlight the efficiency of a pH sensor generated via PHIP in less than one minute, with remarkable polarization, and biocompatibility suitable for future in vivo studies. [ABSTRACT FROM AUTHOR]

Accurate and early disease prediction enables patients to plan and improve their quality of life in the future. Early detection of neurodegenerative diseases, such as Parkinson's disease, is a high priority and a significant challenge in which physicians must act quickly to diagnose and predict the risk of disease severity. Machine learning (ML) models combined with feature selection (FS) techniques can assist physicians in quickly diagnosing a disease. FS technique optimally subsets features to improve model performance and reduce the number of tests required for a patient, thereby speeding up diagnosis. This paper proposes an e-diagnosis approach based on ML-FS algorithms to detect Parkinson's disease using data obtained from Parkinson's Progression Markers Initiative (PPMI) Online study. Also, it can be considered patient-oriented research as it uses self-reported online collected data. The results of six FS techniques pre-applied to classification algorithms named logistic regression, random forest, support vector machine, CatBoost, extreme learning machine, and XGBoost are shown in this study. Chi-square, mutual information, and analysis of variance (ANOVA) filter-based FS methods, while sequential feature selection, Boruta, and recursive feature elimination are considered wrapper methods. The outcomes show that random forest when trained on features selected by the recursive feature elimination technique help to build an efficient and effective approach for detecting Parkinson's disease. [ABSTRACT FROM AUTHOR]

Introduction: Approximately 50% of patients diagnosed with colorectal cancer develop colorectal cancer liver metastases (CRLM). Although curative intent liver resection provides 5-year survival of 40-50%, up to 70% of patients develop recurrence of CRLM. Detection of minimal residual disease (MRD) is essential for timely, optimized treatment. This study evaluated the feasibility and utility of using circulating tumor DNA (ctDNA) to identify MRD and predict disease recurrence. Methods: Patients with CRLM that underwent liver resection and had known KRAS or PIK3CA mutations were retrospectively identified. Serial blood samples were collected every 3 months following surgery for disease surveillance. ctDNA was isolated from the samples and analyzed with digital PCR (dPCR). Results: KRAS and PIK3CA mutations were identified by dPCR in 29 patients over 115 timepoints. In patients with detectable ctDNA at time of liver resection, 81% (13/16) developed disease recurrence, while 46% (6/13) of the patients with undetectable ctDNA recurred (p=0.064). Presence of ctDNA was detected in 27.6% (8/29) of the initial postoperative samples. Radiologic recurrence was later diagnosed in 100% (8/8) of these patients, while 52% (11/21) who had undetectable ctDNA postoperatively recurred (p=0.026). Detectable ctDNA postoperatively was associated with a shorter disease-free survival (DFS) of 9 months vs 13 months in patients who had undetectable ctDNA (HR 2.95, 95% CI 1.16-7.49; p=0.02). Conclusion: Liquid biopsy using dPCR can identify low levels of ctDNA, enabling early detection of disease recurrence. Additionally, the presence of ctDNA postoperatively was predictive of recurrence. This study corroborates current literature and provides rational for moving toward a clinical trial using ctDNA and dPCR to detect MRD after CRLM resection. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a global healthcare challenge lacking a simple and affordable detection method. We propose a novel deep learning framework, Eye-AD, to detect Early-onset Alzheimer's Disease (EOAD) and Mild Cognitive Impairment (MCI) using OCTA images of retinal microvasculature and choriocapillaris. Eye-AD employs a multilevel graph representation to analyze intra- and inter-instance relationships in retinal layers. Using 5751 OCTA images from 1671 participants in a multi-center study, our model demonstrated superior performance in EOAD (internal data: AUC = 0.9355, external data: AUC = 0.9007) and MCI detection (internal data: AUC = 0.8630, external data: AUC = 0.8037). Furthermore, we explored the associations between retinal structural biomarkers in OCTA images and EOAD/MCI, and the results align well with the conclusions drawn from our deep learning interpretability analysis. Our findings provide further evidence that retinal OCTA imaging, coupled with artificial intelligence, will serve as a rapid, noninvasive, and affordable dementia detection. [ABSTRACT FROM AUTHOR]

Alterations in pH are a hallmark in several pathologies including cancer, ischemia, and inflammation. Non-invasive magnetic resonance methods to measure pH offer a new approach for early diagnosis of diseases characterized by acid–base imbalances. The hyperpolarization with parahydrogen-induced polarization (PHIP) enhances inherently low signals in magnetic resonance experiments by several orders of magnitude and offers a suitable platform to obtain biocompatible markers in less than one minute. Here, we present an optimized preparation of an hyperpolarized H13CO3−/13CO2 pH sensor via non-enzymatic decarboxylation with H2O2 of [1-13C]pyruvate-d3 obtained by PHIP at 7 T. An improved 13C polarization of purified [1-13C]pyruvate-d3 in water with 36.65 ± 0.06% polarization was obtained starting from 50 mM precursor. Subsequent decarboxylation, H13CO3−/13CO2 exhibited 12.46 ± 0.01% of polarization at physiological pH, 45 seconds after the reaction start. Considering the dilution factor that [1-13C]pyruvate-d3 exhibits in vivo, we optimized our methodology to test the accuracy of the pH sensor at single digit millimolar concentration. In vitro pH estimations on phantoms and cell culture media demonstrated accurate pH calculations with uncertainties of less than 0.08 units. These promising results highlight the efficiency of a pH sensor generated via PHIP in less than one minute, with remarkable polarization, and biocompatibility suitable for future in vivo studies. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common neurodegenerative disease, with an increasing prevalence. Currently, there is no ideal diagnostic molecular imaging agent for diagnosing AD. Antibodies (Abs) have been proposed to close this gap as they can bind selectively and with high affinity to amyloid β (Aβ)--one of the molecular hallmarks of AD. Abs can even be designed to selectively bind Aβ oligomers or isoforms, which are difficult to target with small imaging agents. Conventionally, Abs must be labeled with long-lived radionuclides which typically results in in high radiation burden to healthy tissue. Pretargeted imaging could solve this challenge as it allows for the use of short-lived radionuclides. To develop pretargeted imaging tools that can enter the brain, AD mouse models are useful as they allow testing of the imaging approach in a relevant animal model that could predict its clinical applicability. Several mouse models for AD have been developed with different characteristics. Commonly used models are: 5xFAD, APP/PS1 and tg-ArcSwe transgenic mice. In this study, we aimed to identify which of these models were best suited to investigate pretargeted imaging approaches beyond the blood brain barrier. We evaluated this by pretargeted autoradiography using the Aβ-targeting antibody 3D6 and an 111In-labeled Tz. Evaluation criteria were target-to-background ratios and accessibility. APP/PS1 mice showed Aβ accumulation in high and low binding brain regions and is as such less suitable for pretargeted purposes. 5xFAD and tg-ArcSwe mice showed similar uptake in high binding regions whereas low uptake in low binding regions and are better suited to evaluate pretargeted imaging approaches. 5xFAD mice are advantaged over tg-ArcSwe mice as pathology can be traced early (6 months compared to 18 months of age) and as 5xFAD mice are commercially available. [ABSTRACT FROM AUTHOR]

Optical imaging is a powerful tool for early disease detection and effective treatment planning, but its accuracy is often compromised by the uptake of imaging materials by the mononuclear phagocyte system (MPS). Herein, we leverage multivalent host-guest interactions between cyanine dyes and ß-cyclodextrin polymers to develop supramolecular probes with enhanced stability, optical, and transport profiles for accurate in vivo imaging. These multivalent interactions not only ensure the stability of the probes but also enhance fluorescence efficiency by minimizing nonradiative decay. Our self-assembly approach effectively modulates probe size and surface properties, enabling evasion of MPS clearance and promoting prolonged bloodstream circulation, thereby improving the signal-to-background ratio for imaging. The effectiveness of our design is demonstrated by substantial advancements in the early diagnosis of acute kidney injury and by providing high-contrast imaging and precise surgical navigation across various tumor models. Our strategy not only advances optical imaging materials toward clinical translation but also establishes a versatile platform applicable to multiple imaging modalities. [ABSTRACT FROM AUTHOR]

Rice is the most widely consumed grain across the world. The rice plants often suffer from diseases. Early detection of such diseases and adopting remedial measures can help the farmers to avoid major losses and can produce best-quality crops in large quantities. However, the conventional rice leaf disease detection techniques are often not accurate, time-consuming and sometimes require laboratory testing. In this context, automatic rice leaf disease detection techniques are presented based on the various deep learning classifiers (namely MobileNetV2, ResNet50, VGG16 and Le-Net5) and an Android application is also developed in order to instantly determine the possible rice diseases from the uploaded rice leaf images captured by the smartphone. The developed models are tested using the publicly available benchmark rice leaf dataset containing three types of rice leaf diseases, namely bacterial leaf blight, leaf smut and brown spot. Experimental results show that MobileNetV2 model performed better compared to other models in terms of classification accuracy, recall and F1-score. The results of statistical significance test also confirmed the superiority of the MobileNetV2 model over other compared deep learning models. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) for extracting structural information from structural magnetic resonance imaging (sMRI), combined with functional magnetic resonance imaging (fMRI) and neuropsychological features, has emerged as a pivotal tool for early diagnosis of Alzheimer's disease (AD). However, the fixed-size convolutional kernels in CNNs have limitations in capturing global features, reducing the effectiveness of AD diagnosis. We introduced a group self-calibrated coordinate attention network (GSCANet) designed for the precise diagnosis of AD using multimodal data, including encompassing Haralick texture features, functional connectivity, and neuropsychological scores. GSCANet utilizes a parallel group self-calibrated module to enhance original spatial features, expanding the field of view and embedding spatial data into channel information through a coordinate attention module, which ensures long-term contextual interaction. In a four-classification comparison (AD vs. early MCI (EMCI) vs. late MCI (LMCI) vs. normal control (NC)), GSCANet demonstrated an accuracy of 78.70%. For the three-classification comparison (AD vs. MCI vs. NC), it achieved an accuracy of 83.33%. Moreover, our method exhibited impressive accuracies in the AD vs. NC (92.81%) and EMCI vs. LMCI (84.67%) classifications. GSCANet improves classification performance at different stages of AD by employing group self-calibrated to expand features receptive field and integrating coordinated attention to facilitate significant interactions among channels and spaces. Providing insights into AD mechanisms and showcasing scalability for various disease predictions. [ABSTRACT FROM AUTHOR]

Precise and efficient identification of single‐stranded nucleic acids is crucial for both pathological research and early diagnosis of diseases, such as cancers. Therefore, we have devised a novel biosensor that utilizes an elegantly designed handgrip‐ring structure sensing probe to enhance the detection sensitivity and reduce background signals. The handgrip‐ring structure sensing probe combines ring padlock‐based target recognition and hairpin structure probe‐based signal amplification. The target sequences form a binding interaction with the ring padlock in the sensing probe, leading to the elongation of the single‐stranded chain with the assistance of polymerase. This elongation step results in the release of the hairpin probe, triggering a signal amplification process. This design significantly minimized the potential discrepancies that may occur during the signal amplification process, hence bestowing the approach with a low level of background signals. By utilizing this innovative design, the current biosensor demonstrates a remarkable ability to detect miRNA with a limit as low as 376 aM and single‐stranded DNA sequences with a limit as low as 45.3 aM. In addition, it possesses exceptional discrimination capabilities. The efficacy of this approach in diagnosing targets was also effectively proved by the rational redesign of the ring padlock. [ABSTRACT FROM AUTHOR]

Background: For early detection and postoperative monitoring of upper tract urothelial carcinoma (UTUC), the traditional detection method was limited to its invasiveness and insufficient sensitivity. We aim to use urine tumour DNA (utDNA) for detecting minimal residual disease (MRD), early diagnosis and perioperative monitoring in UTUC. Method: We previously established a utDNA multidimensional bioinformatic valuation model, named utLIFE, using low‐coverage whole‐genome sequencing and targeted deep sequencing. This prospective cohort enrolled 93 patients diagnosed with UTUC without metastasis. We collected morning urine samples on the day of surgery and the discharge day after the operation for utLIFE testing. In addition, we also enrolled 80 healthy controls to further validate the specificity of the utLIFE model in the study. Results: The utLIFE of preoperative samples could discriminate UTUC with high specificity (96.25%, 77/80), and high sensitivity (96.77%, 90/93) regardless of stage and grade. The sensitivity of utLIFE was significantly higher than urine cytology (p < 0.001) and fluorescence in situ hybridisation (FISH) (p < 0.001) (N = 19), especially in early‐stage and low‐grade UTUC. Postoperative utLIFE scores were significantly decreased compared with those of preoperative samples (79 vs. 36, p < 0.001), indicating its association with tumour burden. For special pathology types, utLIFE performed less well in sensitivity and perioperative alteration. Conclusion: In conclusion, we established a bioinformatic utDNA valuation model, utLIFE, which was validated to be a rapid and noninvasive approach with high sensitivity for early detection and MRD monitoring for UTUC. [ABSTRACT FROM AUTHOR]

Introduction: Early diagnosis of cervical cancer at the precancerous stage is critical for effective treatment and improved patient outcomes. Objective: This study aims to explore the use of SWIN Transformer and Convolutional Neural Network (CNN) hybrid models combined with transfer learning to classify precancerous colposcopy images. Methods: Out of 913 images from 200 cases obtained from the Colposcopy Image Bank of the International Agency for Research on Cancer, 898 met quality standards and were classified as normal, precancerous, or cancerous based on colposcopy and histopathological findings. The cases corresponding to the 360 precancerous images, along with an equal number of normal cases, were divided into a 70/30 train–test split. The SWIN Transformer and CNN hybrid model combines the advantages of local feature extraction by CNNs with the global context modeling by SWIN Transformers, resulting in superior classification performance and a more automated process. The hybrid model approach involves enhancing image quality through preprocessing, extracting local features with CNNs, capturing the global context with the SWIN Transformer, integrating these features for classification, and refining the training process by tuning hyperparameters. Results: The trained model achieved the following classification performances on fivefold cross-validation data: a 94% Area Under the Curve (AUC), an 88% F1 score, and 87% accuracy. On two completely independent test sets, which were never seen by the model during training, the model achieved an 80% AUC, a 75% F1 score, and 75% accuracy on the first test set (precancerous vs. normal) and an 82% AUC, a 78% F1 score, and 75% accuracy on the second test set (cancer vs. normal). Conclusions: These high-performance metrics demonstrate the models' effectiveness in distinguishing precancerous from normal colposcopy images, even with modest datasets, limited data augmentation, and the smaller effect size of precancerous images compared to malignant lesions. The findings suggest that these techniques can significantly aid in the early detection of cervical cancer at the precancerous stage. [ABSTRACT FROM AUTHOR]

The complex nature of human brain tissues is important in ensuring accurate diagnosis to save human lives. Research on early detection of brain diseases has gained significant prominence within medical intelligence using highly complex model architectures with only a single label that cannot be verified. This paper introduces an innovative approach to Siamese Network Genetic Algorithm (SN‐GA) leveraging Siamese contrastive learning for classifying brain images across diverse diseases. Our core architecture is a Bi‐Convolutional Neural Network (Bi‐CNN) optimized by a genetic algorithm to enhance brain image classification. Specifically, five widely recognized transfer learning‐based architectures, namely AlexNet, Efficient‐B0, VGG‐16, ResNet‐50, and Inception‐v3, have been incorporated to evaluate the effectiveness of the proposed SN‐GA system. The performance of these models has been rigorously analyzed and compared using two distinct datasets: Brain tumors and Alzheimer's datasets. The experimental results robustly affirm the efficacy of the proposed Siamese model, yielding exceptional levels of accuracy, precision, and recall, all peaking at 97%. These findings underscore the potential and resilience of the optimized Siamese network in the context of brain disease classification, emphasizing its significance in advancing the field of medical imaging and diagnosis, with implications for early intervention and patient care. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD), the most prevalent form of dementia, is expected to rise dramatically in incidence due to the global population aging. Traditional diagnostic approaches, such as cerebrospinal fluid analysis and positron emission tomography, are expensive and invasive, limiting their routine clinical use. Recent advances in blood-based biomarkers, including amyloid-beta, phosphorylated tau, and neurofilament light, offer promising non-invasive alternatives for early AD detection and disease monitoring. This review synthesizes current research on these blood-based biomarkers, highlighting their potential to track AD pathology and enhance diagnostic accuracy. Furthermore, this review uniquely integrates recent findings on protein-protein interaction networks and microRNA pathways, exploring novel combinations of proteomic, genomic, and epigenomic biomarkers that provide new insights into AD's molecular mechanisms. Additionally, we discuss the integration of these biomarkers with advanced neuroimaging techniques, emphasizing their potential to revolutionize AD diagnostics. Although large-scale validation is still needed, these biomarkers represent a critical advancement toward more accessible, cost-effective, and early diagnostic tools for AD. [ABSTRACT FROM AUTHOR]

The convergence of advanced nanotechnology with disease diagnosis has ushered in a transformative era in healthcare, empowering early and accurate detection of diseases and paving the way for timely interventions, improved treatment outcomes, and enhanced patient well‐being. The development of novel materials is frequently the impetus behind significant advancements in sensor technology. Among them, single‐walled carbon nanotubes (SWCNTSs) have emerged as promising nanomaterials for developing biosensors. Their unique optical, electrical, and biocompatibility properties make them promising candidates for enhancing the sensitivity and real‐time monitoring capabilities of biosensors, as well as for enabling various bioimaging techniques. Recent studies have demonstrated the utility of SWCNTS‐based biosensors in the real‐time monitoring of biological analytes, such as nitric oxide and hydrogen peroxide (H2O2), with potential implications for disease understanding and therapeutic response assessment. Moreover, SWCNTSs have shown promise in bioimaging applications, including fluorescence, Raman spectroscopy, and photoluminescence imaging of biological samples. This article delves into the core principles, design strategies, and operational mechanisms that underpin SWCNTS‐bioimaging techniques‐based biosensors. It emphasizes on their unique properties and versatile functionalization of carbon nanotubes, laying the foundation for their integration into biosensor platforms and applications aimed at diagnosing a wide spectrum of diseases including infectious diseases, cancer, neurological disorders, and metabolic conditions. [ABSTRACT FROM AUTHOR]

In recent years, the convergence of smart electronic textile (e‐textile) and digital technology has emerged as a transformative shift in healthcare, offering innovative solutions for point‐of‐care diagnostics. However, the development of textile electronics with exceptional functionality and comfort still remains challenging. Here, all‐electrospun piezoelectric smart e‐textile empowered is reported by Internet of Things (IoT) and machine learning for advanced point‐of‐care diagnostics. The resulting e‐textile exhibits exceptional breathability (b ≈ 4.13 kg m−2 d−1), flexibility, water‐resistive properties (water contact angle ≈137°), and mechano‐sensitivity of 1.5 V N−1 due to its mechanical‐to‐electrical energy conversion abilities. It can efficiently monitor different critical biomedical healthcare signals, such as, arterial pulse and respiration rate. Importantly, the e‐textile sensor demonstrates remarkable attributes, generating an open circuit voltage of 10.5 V, a short circuit current of 7.7 µA, and power density of 4.2 µW cm−2. Moreover, the e‐textile provides real‐time, non‐invasive monitoring of human physiological movements through IoT. It is worth highlighting that the machine learning showcases an impressive 96% of accuracy in detecting respiratory signals, representing a significant accomplishment. Thus, this e‐textile has enormous potential in remote patient monitoring and early disease detection, aiming to reduce healthcare costs, enhance patient outcomes, and improve the overall quality of medical care. [ABSTRACT FROM AUTHOR]

Early detection of severe acute respiratory syndrome coronavirus 2 infection by diagnostic tests can prompt actions to reduce transmission and improve treatment efficacy, especially for vulnerable groups such as immunocompromised individuals. Recent evidence suggests that sampling the throat in addition to the nose improves clinical sensitivity during early infection for both antigen and molecular coronavirus disease 2019 tests. We urge test manufacturers to validate tests for use with throat swab, in combination with nasal swabs. [ABSTRACT FROM AUTHOR]

Background Early diagnosis of invasive fungal disease is essential for optimizing management. Although the clinical utility of fungal polymerase chain reaction (PCR) testing on plasma and bronchoalveolar lavage (BAL) has been established, the role of follow-up testing remains unclear. Methods This was a retrospective single-center study. The yield of follow-up PCR for Aspergillus species, Mucorales agents, Fusarium species, Scedosporium species, dimorphic fungi, Pneumocystis jirovecii , and Candida species on plasma and/or BAL was measured at intervals of 1, 2, 3, and 4 weeks following a negative result. Results A total of 1389 follow-up tests on 406 plasma specimens from 264 patients and 983 BAL specimens from 431 patients were evaluated. Overall, the positivity rate at 1, 2, 3, and 4 weeks was 2.7% (4/148), 3.3% (4/123), 5.1% (4/78), and 3.5% (2/57), respectively, on plasma, and 0% (0/333), 0.3% (1/288), 0.4% (1/228), and 0.7% (1/134), respectively, on BAL. Conversions occurred with Aspergillus species, Mucorales agents, and Fusarium species PCR on plasma and Aspergillus species and P jirovecii PCR on BAL. All patients who converted were immunocompromised. Within 1 week of a prior negative test, 2 Aspergillus and 2 Mucorales PCRs were positive on plasma, and zero tests were positive on BAL. In week 1, only 1 Aspergillus species that was positive on day 7 was classified as probable fungal disease. Conclusions Fungal PCR follow-up testing on plasma and BAL within 4 weeks of a prior negative result was of low yield and rarely generated a positive result considered clinically significant in the first week. [ABSTRACT FROM AUTHOR]

Healthcare sensing devices enable continuous monitoring of diverse biosignals, such as respiration, heartbeat, temperature, inflammation, Electroencephalogram (EEG), and biomechanical movement, contributing to the management of health conditions and early diagnosis of diseases. However, clinically available tools have several limitations for real‐time measurement of biosignals in non‐hospital environments owing to their cumbersome, complex designs, and rigidity. To address these limitations, there has been a growing body of research that explores flexible and soft electronics for healthcare applications, which feature high mechanical compliance and adaptability. Especially, sensing devices based on fiber structures have attracted significant attention due to high flexibility, lightweight design, effective workspace and unique structural adaptability. Moreover, 1D sensing devices can be seamlessly integrated into garments and the human body with complex structures without any unconformity. In this perspective, The fabrication and electrical functionalization of fiber substrates and focus on recent advances in various fiber‐based sensing systems for strain, pressure, temperature, pH, biomarkers, and neural activity is explored. Additionally, biodegradable fiber‐based sensing devices as future healthcare technologies are briefly discussed. Finally, this article provides a summary and outlook on the remaining challenges for current fiber‐based sensing devices. [ABSTRACT FROM AUTHOR]

Lassa fever is an acute viral zoonotic illness endemic in parts of West Africa. In Nigeria, the disease causes periodic large outbreaks, putting strain on healthcare resources and infrastructure. This article reviews the burden and socioeconomic impact of Lassa fever in Nigeria. The vast majority of cases are mild or asymptomatic, however severe illness still occurs in 20% of patients. Late-stage pregnancy infection has high fatality rates. Diagnosis is challenging and early supportive care is critical for survival. The disease affects all age groups in endemic regions, especially impoverished rural communities. Outbreaks disrupt local economies and agriculture and require increased healthcare spending. Specific economic impacts include industry shutdowns, expenditure on protective equipment for healthcare workers, costs for specialized training programs and treatment facilities, high treatment costs for patients, deterrence of investors and businesses, job losses, and declines in tourism revenue. Social impacts include stigmatization of patients. Recommendations for control emphasize improvements to rural healthcare infrastructure, disease surveillance, affordable diagnostics and treatment access, community outreach and education programs, and fostering local economic growth and sustainability. A multidimensional approach engaging all levels of government and civil society is required to mitigate the burden of Lassa fever on Nigeria. This article covers the disease background, populations affected, economic and social impacts, and recommendations to address the substantial public health burden of Lassa fever outbreaks on Nigeria. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorder (ASD) poses a significant challenge in early diagnosis and intervention due to its multifaceted clinical presentation and lack of objective biomarkers. This research presents a novel approach, termed Neuro Connect, which integrates data-driven techniques with Bidirectional Gated Recurrent Unit (BiGRU) classification to enhance the prediction of ASD using functional Magnetic Resonance Imaging (fMRI) data. This study uses both structural and functional neuroimaging data to investigate the complex brain underpinnings of autism spectrum disorder (ASD). They use an Auto-Encoder (AE) to efficiently reduce dimensionality while retaining critical information by learning and compressing important characteristics from high-dimensional data. We treat the feature-extracted data using a BiGRU model for the classification task of predicting ASD. They provide a new optimization strategy, the Horse Herd Algorithm (HHA), and show that it outperforms other established optimizers, such SGD and Adam, in order to improve classification accuracy. The model’s performance is greatly enhanced by the HHA’s novel optimization technique, which more precisely refines weight modifications made during training. The proposed ASD and EEG dataset accuracy value is 99.5%, and 99.3 compared to the existing method the proposed has a high accuracy value. [ABSTRACT FROM AUTHOR]

Objective To characterize the relationship between gut microbiota and neonatal acute kidney injury biomarkers based on the gut-kidney axis. Study Design The Pubmed database was primarily searched to include relevant literature on gut microbiota and neonatal acute kidney injury biomarkers, which was subsequently organized and analyzed and a manuscript was written. Results Gut microbiota was associated with neonatal acute kidney injury biomarkers. These biomarkers included TIMP-2, IGFBP-7, VEGF, calbindin, GST, B2MG, ghrelin, and clusterin. Conclusion The gut microbiota is strongly associated with neonatal acute kidney injury biomarkers, and controlling the gut microbiota may be a potential target for ameliorating neonatal acute kidney injury. Key Points There is a bidirectional association between gut microbiota and AKI. Gut microbiota is closely associated with biomarkers of nAKI. Manipulation of gut microbiota may improve nAKI. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the effectiveness of multimodal features based on gait analysis and eye tracking for elderly people screening with subjective cognitive decline in the community. Methods: In the study, 412 cognitively normal older adults aged over 65 years were included. Among them, 230 individuals were diagnosed with nonsubjective cognitive decline and 182 with subjective cognitive decline. All participants underwent assessments using three screening tools: the traditional SCD9 scale, gait analysis, and eye tracking. The gait analysis involved three tasks: the single task, the counting backwards dual task, and the naming animals dual task. Eye tracking included six paradigms: smooth pursuit, median fixation, lateral fixation, overlap saccade, gap saccade, and anti-saccade tasks. Using the XGBoost machine learning algorithm, several models were developed based on gait analysis and eye tracking to classify subjective cognitive decline. Results: A total of 161 gait and eye-tracking features were measured. 22 parameters, including 9 gait and 13 eye-tracking features, showed significant differences between the two groups (p < 0.05). The top three eye-tracking paradigms were anti-saccade, gap saccade, and median fixation, with AUCs of 0.911, 0.904, and 0.891, respectively. The gait analysis features had an AUC of 0.862, indicating better discriminatory efficacy compared to the SCD9 scale, which had an AUC of 0.762. The model based on single and dual task gait, antisaccade, gap saccade, and median fixation achieved the best efficacy in SCD screening (AUC = 0.969). Conclusion: The gait analysis, eye-tracking multimodal assessment tool is an objective and accurate screening method that showed better detection of subjective cognitive decline. This finding provides another option for early identification of subjective cognitive decline in the community. [ABSTRACT FROM AUTHOR]

Background: The implementation of the WHO's 2015 recommendations in Benin, requires an assessment of the progress made over time in preventing the transmission of the infection to exposed-infants, and the identification of its determinants. Methods: This was a retrospective study of HIV-1 exposed-infants who underwent PCR between the 6th and 8th weeks of life. Early diagnostic tests were performed using the Abbott m2000 RealTime platform. Comparison of proportions tests (analysis of the significance of the difference in prevalence) with an error threshold of 5% were used to assess the determinants of the transmission. Statistical analysis was performed using R statistical software, version 4.1.3.0. Results: A total of 5,312 infants benefited from early diagnosis by PCR between 2016 and 2021. Among them, 52% are males, tritherapy before pregnancy was the majority treatment used by mothers (30.6%) and monotherapy that of newborns (70%). Mixed breastfeeding is the feeding method with the highest prevalence. The overall transmission rate was 3.4% over the six years. The highest prevalence was achieved in 2018 (4.2%) and the lowest in 2021 (2.7%). The prevalence was lower when mothers were on tritherapy before pregnancy. The determinants of transmission were: mixed breastfeeding, lack of treatment in mothers (22.4%), lack of treatment in infants (19.7%), undefined treatments or absence of treatment in the mother-child pair. Conclusion: This study shows the contribution over time of the PMTCT program to reducing HIV transmission among exposed-infants and also underlines the need for proper conduct of treatment in any women of childbearing age. [ABSTRACT FROM AUTHOR]

Objective: To assess the efficacy of continuous contactless vital signs monitoring with an automated Early Warning System (EWS) in detecting clinical deterioration among patients in general wards. Methods: A prospective observational cohort study was conducted in the medical unit of a tertiary care hospital in India, involving 706 patients over 84,448 monitoring hours. The study used a contactless ballistocardiography system (Dozee system) to continuously monitor heart rate, respiratory rate, and blood pressure. The study assessed total, mean, and median alerts at 24, 48, 72, 96, 120 h, and length of stay (LOS) before patient deterioration or discharge. It analyzed alert sensitivity and specificity, average time from initial alert to deterioration, and healthcare practitioners (HCP) activity. Study was registered with the Clinical Trials Registry-India CTRI/2022/10/046404. Results: Out of 706 patients, 33 (5%) experienced clinical deterioration, while 673 (95%) did not. The deterioration group consistently had a higher number of alerts compared to those who were discharged normally, across all time-points. On average, the time between the initial alert and clinical deterioration was 16 h within the last 24 h preceding the event. The sensitivity of the Dozee-EWS varied between 67% and 94%. HCP spend 10% of their time on vital signs check and documentation. Conclusions: This study suggests that utilizing contactless continuous vital signs monitoring with Dozee-EWS in general ward holds promise for enhancing the early detection of clinical deterioration. Further research is essential to evaluate the effectiveness across a wider range of clinical settings. [ABSTRACT FROM AUTHOR]

Abdominal aortic aneurysm (AAA) often remains undetected until rupture due to limited access to diagnostic ultrasound. This trial evaluated a deep learning (DL) algorithm to guide AAA screening by novice nurses with no prior ultrasonography experience. Ten nurses performed 15 scans each on patients over 65, assisted by a DL object detection algorithm, and compared against physician-performed scans. Ultrasound scan quality, assessed by three blinded expert physicians, was the primary outcome. Among 184 patients, DL-guided novices achieved adequate scan quality in 87.5% of cases, comparable to the 91.3% by physicians (p = 0.310). The DL model predicted AAA with an AUC of 0.975, 100% sensitivity, and 97.8% specificity, with a mean absolute error of 2.8 mm in predicting aortic width compared to physicians. This study demonstrates that DL-guided POCUS has the potential to democratize AAA screening, offering performance comparable to experienced physicians and improving early detection. [ABSTRACT FROM AUTHOR]

Background: Although the etiology of aneurysms remains elusive, recent advances in high-throughput sequencing technology and ongoing human microbiome investigations suggest a potential link between microbiome composition and the onset of various human diseases. Objective: This study aimed to utilize high-throughput 16 S rRNA gene sequencing to analyze the oral flora bacterial profiles of individuals, comparing patients with intracranial aneurysms to a healthy control group. Importantly, we sought to identify differences in the oral microbiota and offer novel insights and methods for early diagnosis and identification of intracranial aneurysms. Method: Saliva samples were collected from 60 patients with cerebral aneurysms (case group) and 130 healthy individuals (control group). The V3-V4 region of the bacterial 16 S rRNA gene was amplified and sequenced using the HiSeq high-throughput sequencing platform to establish the bacterial profile. Sequencing data were analyzed using QIIME2 and Metastats software to compare composition differences and relative abundance at the phylum and genus levels in the oral microbiota of the two groups. Results: Significant differences in oral microbiota composition were observed between patients in the case and control groups (P < 0.05). Genus-level identification highlighted key positions occupied by Eubacterium, Saccharimonadaceae, Rothia, Gemella, Streptococcus, Lactobacillales, Phocaeicola, Bacteroides, Saccharimonadales, and Abiotrophia. Conclusion: This study revealed noteworthy distinctions in the composition, abundance, and diversity of oral microbiota between intracranial aneurysm patients and healthy controls. These disparities suggest a potential correlation between oral microbiota and the development of intracranial aneurysms, offering new avenues for early diagnosis and intervention. However, limitations such as a small sample size, lack of prospective design, and absence of causal inference warrant further validation and exploration. [ABSTRACT FROM AUTHOR]

Background/Objectives: MRI is the gold standard for detecting meniscal tears; however, ultrasound may readily detect meniscal changes, obviating the need for MRI. We aim to (1) determine ultrasound sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy in detecting meniscal changes, and (2) describe characteristic meniscal changes in US and their prevalence. Methods: A retrospective analysis of knee ultrasound scans for the presence of medial and lateral meniscal tears was conducted. Meniscal changes were characterized into five US appearances (cleft, diminutive, cyst, displaced fragment, and extrusion) by the consensus of two musculoskeletal radiologists. Ultrasound findings were then compared to MRI results. Results: In total, 249 patients were included. Ultrasound sensitivity, specificity, PPV, NPV, and accuracy for medial meniscal tears were 79%, 97.3%, 95.3%, 86.6%, and 90%, respectively, and for lateral meniscal tears the ultrasound sensitivity, specificity, PPV, NPV, and accuracy were 63%, 99.5%, 96%, 93%, and 93.6%, respectively. The false negative and false positive rates for medial meniscal tears were 13.4% and 4.7%, respectively, and for the lateral meniscus, the false negative and false positive rates were 6.7% and 3.8%, respectively. Meniscal clefts were the most prevalent appearance in the medial meniscus followed by extrusions. Meniscal extrusions were the most prevalent appearance in the lateral meniscus followed by clefts. Conclusions: Community-based US is highly accurate in the detection of meniscal tears when compared with MRI, making it a valuable diagnostic imaging tool for detecting meniscal tears in a community setting where accessibility to MRI is limited or if there are MRI contraindications. [ABSTRACT FROM AUTHOR]

Objectives: The aim was to develop and validate the Communities Geriatric Mild Cognitive Impairment Risk Calculator (CGMCI-Risk), aiding community healthcare workers in the early identification of individuals at high risk of mild cognitive impairment (MCI). Methods: Based on nationally representative community survey data, backward stepwise regression was employed to screen the variables, and logistic regression was utilized to construct the CGMCI-Risk. Internal validation was conducted using bootstrap resampling, while external validation was performed using temporal validation. The area under the receiver operating characteristic curve (AUROC), calibration curve, and decision curve analysis (DCA) were employed to evaluate the CGMCI-Risk in terms of discrimination, calibration, and net benefit, respectively. Results: The CGMCI-Risk model included variables such as age, educational level, sex, exercise, garden work, TV watching or radio listening, Instrumental Activity of Daily Living (IADL), hearing, and masticatory function. The AUROC was 0.781 (95% CI = 0.766 to 0.796). The calibration curve showed strong agreement, and the DCA suggested substantial clinical utility. In external validation, the CGMCI-Risk model maintained a similar performance with an AUROC of 0.782 (95% CI = 0.763 to 0.801). Conclusions: CGMCI-Risk is an effective tool for assessing cognitive function risk within the community. It uses readily predictor variables, allowing community healthcare workers to identify the risk of MCI in older adults over a three-year span. [ABSTRACT FROM AUTHOR]

Objective: To explore the diagnostic value of intermittent changes in the nocturnal ST segment trend graph in a dynamic electrocardiogram (ECG) for coronary heart disease (CHD). Methods: A total of 205 patients who underwent coronary angiography were included in this retrospective study. The study sample was determined through a power analysis aimed at achieving power of 80% with a significance level of 0.05. The participants were divided into the CHD (n = 101) and the non-CHD (n = 104) group, based on the degree of coronary artery diameter stenosis. The morphological changes in the ST segment trend graph were observed and divided into two categories: 'wall-shaped' and 'peak-shaped' changes. Results: Among the 205 patients, 94 had nocturnal ST segment dynamic changes and 111 did not. The detection rate of CHD without nocturnal ST segment dynamic changes was 21.59%, significantly lower than the detection rate of 93.18% in those with nocturnal ST segment changes, reflecting a statistically significant difference (P < 0.05). The positive rate of ST segment in patients with single-vessel disease (71.88%) was lower than in patients with multi-vessel disease (78.57%), and both differences were statistically significant (P < 0.05). The duration of ST segment trend graph changes in 94 cases in the CHD group with intermittent changes in the nocturnal ST segment trend graph was higher than in the non-CHD group, but no significant difference was observed (P > 0.05). The detection rate of CHD in the peak-shaped dynamic change group of the nocturnal ST segment trend graph was significantly higher (76/82) than in the wall-shaped (6/82) dynamic change group (P < 0.05). Conclusion: Peak-shaped changes in the nocturnal ST segment trend graph indicate coronary artery lesions. Nocturnal ST segment changes observed through dynamic ECG monitoring can serve as a valuable non-invasive predictor for CHD, providing a feasible method for early diagnosis and intervention in clinical practice. [ABSTRACT FROM AUTHOR]

The glaucoma treatment paradigm is starting to change from a more reactive approach that relies on topical medications to a more proactive approach that leverages procedural interventions. This evolution toward interventional glaucoma has been enabled by a growing array of lower-risk minimally invasive procedures such as laser trabeculoplasty, minimally invasive glaucoma surgery, and procedural pharmaceuticals. A common feature of these glaucoma interventions—as with all glaucoma interventions—is the need for early, prompt, and accurate diagnosis. The present review summarizes new and upcoming developments in glaucoma diagnostics. These include technologies and techniques for home-based intraocular pressure measurement, novel visual field platforms, photography- and optical coherence tomography-based visualization, and artificial intelligence applications. They also include emerging technologies such as mitochondrial flavoprotein fluorescence imaging, detection of apoptosing retinal cells, collector channel visualization, and genetic testing. These diagnostic modalities have the potential to circumvent the limitations of traditional diagnostic methods. By increasing the frequency and feasibility of obtaining valuable glaucoma data with more rapid detection of disease and progression, these diagnostics may enable an interventional approach to glaucoma treatment for the betterment of patient care. Plain language summary: Diagnostics in glaucoma The treatment of glaucoma, the leading cause of irreversible blindness worldwide, relies on timely and accurate diagnosis and monitoring. Diagnosis may involve pressure-based, visual, molecular, genetic, and artificial intelligence modalities. This article reviews the new and upcoming diagnostic technologies in these areas. These technologies have the potential to overcome some of the challenges of traditional diagnostics, thereby enabling more rapid and accurate detection of disease and progression and a more effective interventional approach to glaucoma treatment. [ABSTRACT FROM AUTHOR]

The uptake of early infant HIV diagnosis services is crucial for preventing mother to child transmission of virus, and timely management. However, the uptake of the services remains a global challenge, despite major advances in HIV testing. This study investigated the uptake of early infant HIV diagnosis and its associated factors among mothersof exposed infants. The results showed that the uptake of early infant HIV diagnosis was 76%. Factors associated with the uptake are caregivers being married, have higher income level and having adequate knowledge on early infant HIV diagnosis. [ABSTRACT FROM AUTHOR]

For any agricultural society, the well-being of the plants is crucial to achieve a greater yield. The health and vigor of plants play a pivotal role in shaping the ultimate outcome of crop production. There are, too many infections affecting the plants generate harm to diverse economies and communities. It can also result in significant environmental losses. To prevent such losses, it is easier to diagnose diseases correctly and promptly at an early stage of plant life. This research mainly focuses on Muskmelon leaf diseases. Muskmelon is a remunerative crop with a short life span of around 65 days. Any disease attack in this duration will affect the crop entirely which in turn leads to yield loss. Hence, there needs an early prediction system for predicting diseases. The primary goal of this research is to develop a prediction model based of Multi Class Capsule Network for early detection of disease and pest in plants. The performance indicators examined for classification of leaf diseases are Accuracy, Precision, Recall, F1 score and, Loss function. The performance of Multi – Class Capsule Network [MCCN] is compared with existing pre-trained models such as, AlexNet, ResNet, VGG16, VGG19, GoogleNet, and CapsuleNet. Experimental results indicated that the MCCN model performs with an accuracy of 97.30% which is better than the accuracy of other models under considerations. [ABSTRACT FROM AUTHOR]

One in every three cancers diagnosed is a skin cancer. Europe has the global lead in the number of UV‐attributable cancer cases with the highest number of melanoma cases worldwide and the second highest number of keratinocyte cancers (KC). Further increases are expected in Europe for the coming decades. Projected increases are highest for KC with increases in incidence around 40% and increases in mortality around 50%, with KC mortality in males approximating melanoma mortality in females. The two main drivers for this skin cancer epidemic are ageing of the population but especially UV exposure. In conclusion, skin cancer represents a major challenge in the cancer field in Europe today and will continue to do so in the coming decades. This calls for a European skin cancer action plan intended to reduce avoidable UV exposure and to prepare the healthcare system to safeguard early diagnosis and treatment of skin cancer. [ABSTRACT FROM AUTHOR]

Despite the implementation of preventive measures to counteract the obesity epidemics, the prevalence of childhood obesity is still alarming all over the world. Childhood obesity is the most common risk factor for both cardiovascular and metabolic diseases. In fact, an earlier onset of obesity can cause a greater risk of adiposity tracking across the lifespan and consequently a longer exposure to cardiometabolic risk factors. Accumulating evidence provided by prospective and intervention studies demonstrated the link between pediatric obesity and selected subclinical signs of cardiovascular damage (atherosclerosis and left ventricular hypertrophy), or fatal and not fatal cardiovascular events as early as 40 years of age. The numerous guidelines and scientific documents published in the last years demonstrate the relevance of assessing cardiometabolic risk factors in children and adolescents with OB. This Position paper, released by experts of the "Childhood Obesity study group" within the Italian Society for Pediatric Endocrinology and Diabetology, aims to review the assessment of cardiometabolic risk factors and comorbidities in children and adolescents with OW/OB on the light of the most recent scientific evidence. The main recommendations are: (a) early detection of comorbidities, including hypertension, dyslipidemia, prediabetes/type 2 diabetes, metabolic dysfunction-associated steatotic liver disease, polycystic ovary syndrome, inactivity, obstructive sleep apnea and decline in kidney function; (b) weight loss treatment, which is associated with a reduction of all cardiometabolic risk factors; (c) specific treatment of comorbidities, through lifestyle modifications or pharmacological treatment added to lifestyle for suitable individuals; d). monitoring comorbidities for mitigating future morbidity and mortality. [ABSTRACT FROM AUTHOR]

Bioelectronic implantable devices are adept at facilitating continuous monitoring of health and enabling the early detection of diseases, offering insights into the physiological conditions of various bodily organs. Furthermore, these advanced systems have therapeutic capabilities in neuromodulation, demonstrating their efficacy in addressing diverse medical conditions through the precise delivery of stimuli directly to specific targets. This comprehensive review explores developments and applications of bioelectronic devices within the biomedical field. Special emphasis is placed on the evolution of closed‐loop systems, which stand out for their dynamic treatment adjustments based on real‐time physiological feedback. The integration of Artificial Intelligence (AI) and edge computing technologies is discussed, which significantly bolster the diagnostic and therapeutic functions of these devices. By addressing elemental analyses, current challenges, and future directions in implantable devices, the review aims to guide the pathway for advances in bioelectronic devices. [ABSTRACT FROM AUTHOR]

Background: Mycetoma is an uncommon and neglected tropical disease in Uganda. We aimed to assess baseline knowledge and provide community health workers (CHWs) in Northern Uganda with knowledge to identify and refer presumptive mycetoma cases. Methodology: Between March and August 2023, we conducted a concurrent triangulation mixed methods study among CHWs in Gulu and Pader districts on mycetoma. We conducted a 1 day in person training on mycetoma. Quantitative data were collected before (pretest), immediately (immediate posttest) and six months (6-month posttest) after the training and results compared using paired sample t test or one-way ANOVA. Qualitative data were collected using four focused group discussions, audio recorded, and analyzed using thematic content analysis. Principal findings: Forty-five CHWs were enrolled, mostly male (66.7%, n = 30), with a median age of 36 years (IQR 29 43). Out of a total score of 18, the baseline mean knowledge score was 7±2.42, improving to 11±1.99 immediately posttest (p<0.001), and 10±2.35 at 6 months (p<0.001), without additional training. Significant knowledge improvements at 6 months were observed among female participants (p = 0.004), those aged 30 40 years (p = 0.031) or 40+ years (p = 0.035), and those with secondary education (p = 0.007). Over 6 months, CHWs screened 2,773 adults, identifying and referring 30 presumptive mycetoma cases. Qualitative findings revealed challenges and barriers to early identification and referral of mycetoma presumptive cases including limited knowledge, stigma, myths, lack of an indigenous name for mycetoma, delayed decision making, and transportation barriers. Conclusions: This study highlights a significant knowledge gap among CHWs about mycetoma, with substantial improvement following training. The identification of presumptive cases by CHWs reflects their potential in community-based surveillance, emphasizing the need to integrate well-trained CHW to lead efforts for mycetoma surveillance and capacity building to enhance health outcomes in Uganda. Author summary: Mycetoma is a rare neglected tropical disease that affects the skin and the tissues beneath skin, causing severe health issues if not diagnosed and treated early. It disproportionately affects people in rural and impoverished communities in sub-Saharan Africa, including Uganda. Mycetoma often affects mostly young adults in their productive years especially those participating in agricultural activities. In our study, we trained community health workers (CHWs) in Northern Uganda for one day to improve their knowledge and skills in identifying and referring cases of mycetoma. Our results showed that the CHWs had significant knowledge improvement that persisted for at least six months without more training. Furthermore, they were able to identify 30 possible cases of mycetoma after assessing nearly 3,000 people in the communities. Furthermore, we found challenges and barriers to early detection of this disease such as limited initial knowledge, stigma, myths about the disease, and practical issues like transportation difficulties and long distances to health facilities. Therefore, our study shows that by empowering local health workers, we can improve early detection and treatment of mycetoma, ultimately enhancing health in affected regions. [ABSTRACT FROM AUTHOR]

Background: Acute flank pain is a common presentation in the Emergency Department (ED). Point-of-care ultrasound (PoCUS) has emerged as an invaluable tool in the evaluation of acute flank pain and provides real-time images resulting in the early identification of the underlying pathology. Case Report: Here we report a case of a young lady presented to the ED with a complaint of right flank pain followed by a syncopal episode. The diagnosis of ruptured angiomyolipoma was made using a PoCUS and an immediate consult to interventional radiology and urology was provided. Conclusion: This case highlights the importance of PoCUS in assessing patients presented to the ED with acute flank pain. Its incorporation into clinical practice promises to revolutionize the approach to renal emergencies, emphasizing its important role in delivering timely and effective care. [ABSTRACT FROM AUTHOR]

Heart sound auscultation plays a crucial role in the early diagnosis of cardiovascular diseases. In recent years, great achievements have been made in the automatic classification of heart sounds, but most methods are based on segmentation features and traditional classifiers and do not fully exploit existing deep networks. This paper proposes a cardiac audio classification method based on image expression of multidimensional features (CACIEMDF). First, a 102-dimensional feature vector is designed by combining the characteristics of heart sound data in the time domain, frequency domain and statistical domain. Based on the feature vector, a two-dimensional feature projection space is constructed by PCA dimensionality reduction and the convex hull algorithm, and 102 pairs of coordinate representations of the feature vector in the two-dimensional space are calculated. Each one-dimensional component of the feature vector corresponds to a pair of 2D coordinate representations. Finally, the one-dimensional feature component value and its divergence into categories are used to fill the three channels of a color image, and a Gaussian model is used to dye the image to enrich its content. The color image is sent to a deep network such as ResNet50 for classification. In this paper, three public heart sound datasets are fused, and experiments are conducted using the above methods. The results show that for the two-classification/five-classification task of heart sounds, the method in this paper can achieve a classification accuracy of 95.68%/94.53% when combined with the current deep network. [ABSTRACT FROM AUTHOR]

Intermediate uveitis is defined as inflammation of the vitreous and pars planitis. Etiology can be infectious, associated with a systemic disease, neoplastic or idiopathic. Pars planitis is the term used for idiopathic intermediate uveitis that presents with snowballs and snowbanks. While relatively rare, intermediate uveitis is present globally and typically affects adult females. Awareness of the presentation and a dilated fundus examination in patients presenting with floaters is essential to the diagnosis. PCR testing has enhanced the ability to diagnose infectious and neoplastic conditions that masquerade as intermediate uveitis. A structured review of systems and focused imaging, and laboratory testing will assist with early diagnosis and initiation of treatment. [ABSTRACT FROM AUTHOR]

Background: Pulmonary pseudoaneurysm (PAP) is a fatal complication of severe acute respiratory syndrome caused by coronavirus 2 infections in the lungs. Other causative agent such as fungal and tubercular also responsible for this entity. Early detection and treatment can save a patient's life. Case presentation: Here we describe a case of a left pulmonary artery pseudo aneurysm in a 54-year-old patient with COVID-19 pneumonia who presented with massive hemoptysis and which was treated with endovascular embolization. Conclusion: It is caused by vasculopathy due to immunosuppression /immune dysregulation in COVID-19. Minimally invasive endovascular embolization of pseudoaneurysm carries less mortality and morbidity. [ABSTRACT FROM AUTHOR]

Background: Early detection and accurate differentiation of malignant ground-glass nodules (GGNs) in lung CT scans are crucial for the effective treatment of lung adenocarcinoma. However, existing imaging diagnostic methods often struggle to distinguish between benign and malignant GGNs in the early stages. This study aims to predict the malignancy risk of GGNs observed in lung CT scans by applying two radiomics methods: topological data analysis and texture analysis. Methods: A retrospective analysis was conducted on 3223 patients from two centers between January 2018 and June2023. The dataset was divided into training, testing, and validation sets to ensure robust model development and validation. We developed topological features applied to GGNs using radiomics analysis based on homology. This innovative approach emphasizes the integration of topological information, capturing complex geometric and spatial relationships within GGNs. By combining machine learning and deep learning algorithms, we established a predictive model that integrates clinical parameters, previous radiomics features, and topological radiomics features. Results: Incorporating topological radiomics into our model significantly enhanced the ability to distinguish between benign and malignant GGNs. The topological radiomics model achieved areas under the curve (AUC) of 0.85 and 0.862 in two independent validation sets, outperforming previous radiomics models. Furthermore, this model demonstrated higher sensitivity compared to models based solely on clinical parameters, with sensitivities of 80.7% in validation set 1 and 82.3% in validation set 2. The most comprehensive model, which combined clinical parameters, previous radiomics features, and topological radiomics features, achieved the highest AUC value of 0.879 across all datasets. Conclusion: This study validates the potential of topological radiomics in improving the predictive performance for distinguishing between benign and malignant GGNs. By integrating topological features with previous radiomics and clinical parameters, our comprehensive model provides a more accurate and reliable basis for developing treatment strategies for patients with GGNs. [ABSTRACT FROM AUTHOR]