Unpaired fundus image enhancement based on constrained generative adversarial networks.

Fundus photography (FP) is a crucial technique for diagnosing the progression of ocular and systemic diseases in clinical studies, with wide applications in early clinical screening and diagnosis. However, due to the nonuniform illumination and imbalanced intensity caused by various reasons, the quality of fundus images is often severely weakened, brings challenges for automated screening, analysis, and diagnosis of diseases. To resolve this problem, we developed strongly constrained generative adversarial networks (SCGAN). The results demonstrate that the quality of various datasets were more significantly enhanced based on SCGAN, simultaneously more effectively retaining tissue and vascular information under various experimental conditions. Furthermore, the clinical effectiveness and robustness of this model were validated by showing its improved ability in vascular segmentation as well as disease diagnosis. Our study provides a new comprehensive approach for FP and also possesses the potential capacity to advance artificial intelligence-assisted ophthalmic examination.

Analysis of optical properties and response mechanism of H2S fluorescent probe based on rhodamine derivatives.

H2S plays a crucial role in numerous physiological and pathological processes. In this project, a new fluorescent probe, SG-H2S, for the detection of H2S, was developed by introducing the recognition group 2,4-dinitrophenyl ether. The combination of rhodamine derivatives can produce both colorimetric reactions and fluorescence reactions. Compared with the current H2S probes, the main advantages of SG-H2S are its wide pH range (5-9), fast response (30 min), and high selectivity in competitive species (including biological mercaptan). The probe SG-H2S has low cytotoxicity and has been successfully applied to imaging in MCF-7 cells, HeLa cells, and BALB/c nude mice. We hope that SG-H2S will provide a vital method for the field of biology.

Diagnosis of mental disorders using machine learning: Literature review and bibliometric mapping from 2012 to 2023.

Background: Mental disorders (MDs) are becoming a leading burden in non-communicable diseases (NCDs). As per the World Health Organization's 2022 assessment report, there was a steep increase of 25 % in MDs during the COVID-19 pandemic. Early diagnosis of MDs can significantly improve treatment outcome and save disability-adjusted life years (DALYs). In recent times, the application of machine learning (ML) and deep learning (DL)) has shown promising results in the diagnosis of MDs, and the field has witnessed a huge research output in the form of research publications. Therefore, a bibliometric mapping along with a review of recent advancements is required. Methods: This study presents a bibliometric analysis and review of the research, published over the last 10 years. Literature searches were conducted in the Scopus database for the period from January 1, 2012, to June 9, 2023. The data was filtered and screened to include only relevant and reliable publications. A total of 2811 journal articles were found. The data was exported to a comma-separated value (CSV) format for further analysis. Furthermore, a review of 40 selected studies was performed. Results: The popularity of ML techniques in diagnosing MDs has been growing, with an annual research growth rate of 17.05 %. The Journal of Affective Disorders published the most documents (n = 97), while Wang Y. (n = 64) has published the most articles. Lotka's law is observed, with a minority of authors contributing the majority of publications. The top affiliating institutes are the West China Hospital of Sichuan University followed by the University of California, with China and the US dominating the top 10 institutes. While China has more publications, papers affiliated with the US receive more citations. Depression and schizophrenia are the primary focuses of ML and deep learning (DL) in mental disease detection. Co-occurrence network analysis reveals that ML is associated with depression, schizophrenia, autism, anxiety, ADHD, obsessive-compulsive disorder, and PTSD. Popular algorithms include support vector machine (SVM) classifier, decision tree classifier, and random forest classifier. Furthermore, DL is linked to neuroimaging techniques such as MRI, fMRI, and EEG, as well as bipolar disorder. Current research trends encompass DL, LSTM, generalized anxiety disorder, feature fusion, and convolutional neural networks.

Comprehensive assessment of machine learning methods for diagnosing gastrointestinal diseases through whole metagenome sequencing data.

The gut microbiome, linked significantly to host diseases, offers potential for disease diagnosis through machine learning (ML) pipelines. These pipelines, crucial in modeling diseases using high-dimensional microbiome data, involve selecting profile modalities, data preprocessing techniques, and classification algorithms, each impacting the model accuracy and generalizability. Despite whole metagenome shotgun sequencing (WMS) gaining popularity for human gut microbiome profiling, a consensus on the optimal methods for ML pipelines in disease diagnosis using WMS data remains elusive. Addressing this gap, we comprehensively evaluated ML methods for diagnosing Crohn's disease and colorectal cancer, using 2,553 fecal WMS samples from 21 case-control studies. Our study uncovered crucial insights: gut-specific, species-level taxonomic features proved to be the most effective for profiling; batch correction was not consistently beneficial for model performance; compositional data transformations markedly improved the models; and while nonlinear ensemble classification algorithms typically offered superior performance, linear models with proper regularization were found to be more effective for diseases that are linearly separable based on microbiome data. An optimal ML pipeline, integrating the most effective methods, was validated for generalizability using holdout data. This research offers practical guidelines for constructing reliable disease diagnostic ML models with fecal WMS data.

Federated learning using model projection for multi-center disease diagnosis with non-IID data.

Multi-center disease diagnosis aims to build a global model for all involved medical centers. Due to privacy concerns, it is infeasible to collect data from multiple centers for training (i.e., centralized learning). Federated Learning (FL) is a decentralized framework that enables multiple clients (e.g., medical centers) to collaboratively train a global model while retaining patient data locally for privacy. However, in practice, the data across medical centers are not independently and identically distributed (Non-IID), causing two challenging issues: (1) catastrophic forgetting at clients, i.e., the local model at clients will forget the knowledge received from the global model after local training, causing reduced performance; and (2) invalid aggregation at the server, i.e., the global model at the server may not be favorable to some clients after model aggregation, resulting in a slow convergence rate. To mitigate these issues, an innovative Federated learning using Model Projection (FedMoP) is proposed, which guarantees: (1) the loss of local model on global data does not increase after local training without accessing the global data so that the performance will not be degenerated; and (2) the loss of global model on local data does not increase after aggregation without accessing local data so that convergence rate can be improved. Extensive experimental results show that our FedMoP outperforms state-of-the-art FL methods in terms of accuracy, convergence rate and communication cost. In particular, our FedMoP also achieves comparable or even higher accuracy than centralized learning. Thus, our FedMoP can ensure privacy protection while outperforming centralized learning in accuracy and communication cost.

A class-imbalanced hybrid learning strategy based on Raman spectroscopy of serum samples for the diagnosis of hepatitis B, hepatitis A, and thyroid dysfunction.

Computer-aided vibrational spectroscopy detection technology has achieved promising results in the field of early disease diagnosis. Yet limited by factors such as the number of actual samples and the cost of spectral acquisition in clinical medicine, the data available for model training are insufficient, and the amount of data varies greatly between different diseases, which constrain the performance optimization and enhancement of the diagnostic model. In this study, vibrational spectroscopy data of three common diseases are selected as research objects, and experimental research is conducted around the class imbalance situation that exists in medical data. When dealing with the challenge of class imbalance in medical vibrational spectroscopy research, it no longer relies on some kind of independent and single method, but considers the combined effect of multiple strategies. SVM, K-Nearest Neighbor (KNN), and Decision Tree (DT) are used as baseline comparison models on Raman spectroscopy medical datasets with different imbalance rates. The performance of the three strategies, Ensemble Learning, Feature Extraction, and Resampling, is verified on the class imbalance dataset by G-mean and AUC metrics, respectively. The results show that all the above three methods mitigate the negative impact caused by unbalanced learning. Based on this, we propose a hybrid ensemble classifier (HEC) that integrates resampling, feature extraction, and ensemble learning to verify the effectiveness of the hybrid learning strategy in solving the class imbalance problem. The G-mean and AUC values of the HEC method are 82.7 % and 83.12 % for the HBV dataset, is 2.02 % and 1.98 % higher than the optimal strategy; 83.62 % and 83.76 % for the HCV dataset, is 9.79 % and 8.47 % higher than the optimal strategy; while for the thyroid dysfunction dataset are 77.56 % and 77.85 %, is 6.92 % and 6.36 % higher than that of the optimal strategy, respectively. The experimental results show that the G-mean and AUC metrics of the HEC method are higher than those of the baseline classifier as well as the optimal combination using separate strategies. It can be seen that the HEC method can effectively counteract the unfavorable effects of imbalance learning and is expected to be applied to a wider range of imbalance scenarios.

Ultrasmall CuMn-His Nanozymes with Multienzyme Activity at Neutral pH: Construction of a Colorimetric Sensing Array for Biothiol Detection and Disease Identification.

Biothiol assays offer vital insights into health assessment and facilitate the early detection of potential health issues, thereby enabling timely and effective interventions. In this study, we developed ultrasmall CuMn-Histidine (His) nanozymes with multiple enzymatic activities. CuMn-His enhanced peroxidase (POD)-like activity at neutral pH was achieved through hydrogen bonding and electrostatic effects. In addition, CuMn-His possesses laccase (LAC)-like and superoxide dismutase (SOD)-like activities at neutral pH. Based on three different enzyme mimetic activities of CuMn-His at neutral pH, the colorimetric sensing array without changing the buffer solution was successfully constructed. The array was successfully used for the identification of three biothiols, glutathione (GSH), cysteine (Cys), and homocysteine (Hcy). Subsequently, excellent application results were shown in complex serum and cellular level analyses. This study provides an innovative strategy for the development of ultrasmall bimetallic nanozymes with multiple enzymatic activities and the construction of colorimetric sensing arrays.

Diffuse and focal liver fat: advanced imaging techniques and diagnostic insights.

The fatty liver disease represents a complex, multifaceted challenge, requiring a multidisciplinary approach for effective management and research. This article uses conventional and advanced imaging techniques to explore the etiology, imaging patterns, and quantification methods of hepatic steatosis. Particular emphasis is placed on the challenges and advancements in the imaging diagnostics of fatty liver disease. Techniques such as ultrasound, CT, MRI, and elastography are indispensable for providing deep insights into the liver's fat content. These modalities not only distinguish between diffuse and focal steatosis but also help identify accompanying conditions, such as inflammation and fibrosis, which are critical for accurate diagnosis and management.

Artificial intelligence in retinal screening using OCT images: A review of the last decade (2013-2023).

BACKGROUND AND OBJECTIVES: Optical coherence tomography (OCT) has ushered in a transformative era in the domain of ophthalmology, offering non-invasive imaging with high resolution for ocular disease detection. OCT, which is frequently used in diagnosing fundamental ocular pathologies, such as glaucoma and age-related macular degeneration (AMD), plays an important role in the widespread adoption of this technology. Apart from glaucoma and AMD, we will also investigate pertinent pathologies, such as epiretinal membrane (ERM), macular hole (MH), macular dystrophy (MD), vitreomacular traction (VMT), diabetic maculopathy (DMP), cystoid macular edema (CME), central serous chorioretinopathy (CSC), diabetic macular edema (DME), diabetic retinopathy (DR), drusen, glaucomatous optic neuropathy (GON), neovascular AMD (nAMD), myopia macular degeneration (MMD) and choroidal neovascularization (CNV) diseases. This comprehensive review examines the role that OCT-derived images play in detecting, characterizing, and monitoring eye diseases. METHOD: The 2020 PRISMA guideline was used to structure a systematic review of research on various eye conditions using machine learning (ML) or deep learning (DL) techniques. A thorough search across IEEE, PubMed, Web of Science, and Scopus databases yielded 1787 publications, of which 1136 remained after removing duplicates. Subsequent exclusion of conference papers, review papers, and non-open-access articles reduced the selection to 511 articles. Further scrutiny led to the exclusion of 435 more articles due to lower-quality indexing or irrelevance, resulting in 76 journal articles for the review. RESULTS: During our investigation, we found that a major challenge for ML-based decision support is the abundance of features and the determination of their significance. In contrast, DL-based decision support is characterized by a plug-and-play nature rather than relying on a trial-and-error approach. Furthermore, we observed that pre-trained networks are practical and especially useful when working on complex images such as OCT. Consequently, pre-trained deep networks were frequently utilized for classification tasks. Currently, medical decision support aims to reduce the workload of ophthalmologists and retina specialists during routine tasks. In the future, it might be possible to create continuous learning systems that can predict ocular pathologies by identifying subtle changes in OCT images.

A new machine learning model to predict the prognosis of cardiogenic brain infarction.

Cardiogenic cerebral infarction (CCI) is a disease in which the blood supply to the blood vessels in the brain is insufficient due to atherosclerosis or stenosis of the coronary arteries in the patient's heart, which leads to neurological deficits. To predict the pathogenic factors of cardiogenic cerebral infarction, this paper proposes a machine learning based analytical prediction model. 494 patients with CCI who were hospitalized for the first time were consecutively included in the study between January 2017 and December 2021, and followed up every three months for one year after hospital discharge. Clinical, laboratory and imaging data were collected, and predictors associated with relapse and death in CCI patients at six months and one year after discharge were analyzed using univariate and multivariate logistic regression methods, meanwhile established a new machine learning model based on the enhanced moth-flame optimization (FTSAMFO) and the fuzzy K-nearest neighbor (FKNN), called BITSAMFO-FKNN, which is practiced on the dataset related to patients with CCI. Specifically, this paper proposes the spatial transformation strategy to increase the exploitation capability of moth-flame optimization (MFO) and combines it with the tree seed algorithm (TSA) to increase the search capability of MFO. In the benchmark function experiments FTSAMFO beat 5 classical algorithms and 5 recent variants. In the feature selection experiment, ten times ten-fold cross-validation trials showed that the BITSAMFO-FKNN model proved actual medical importance and efficacy, with an accuracy value of 96.61%, sensitivity value of 0.8947, MCC value of 0.9231, and F-Measure of 0.9444. The results of the trial showed that hemorrhagic conversion and lower LVDD/LVSD were independent risk factors for recurrence and death in patients with CCI. The established BITSAMFO-FKNN method is helpful for CCI prognosis and deserves further clinical validation.

Portable hydrogel kit based on Michael addition reaction for (E)-2-hexenal gas detection.

Plants exhibit rapid responses to biotic and abiotic stresses by releasing a range of volatile organic compounds (VOCs). Monitoring changes in these VOCs holds the potential for the early detection of plant diseases. This study proposes a method for identifying late blight in potatoes based on the detection of (E)-2-hexenal, one of the major VOC markers released during plant infection by Phytophthora infestans. By combining the Michael addition reaction with cysteine-mediated etching of aggregation-induced emission gold nanoclusters (Au NCs), we have developed a portable hydrogel kit for on-site detection of (E)-2-hexenal. The Michael addition reaction between (E)-2-hexenal and cysteine effectively alleviates the etching of cysteine-mediated Au NCs, leading to a distinct fluorescence color change in the Au NCs, enabling a detection limit of 0.61 ppm. Utilizing the superior loading and diffusion characteristics of the three-dimensional structure of agarose hydrogel, our sensor demonstrated exceptional performance in terms of sensitivity, selectivity, reaction time, and ease of use. Moreover, quantitative measurement of (E)-2-hexenal was made easier by using ImageJ software to transform fluorescent images from the hydrogel kit into digital data. Such method was effectively used for the early detection of potato late blight. This study presents a low-cost, portable fluorescent analytical tool, offering a new avenue for on-site detection of plant diseases.

Revised criteria for diagnosis and staging of Alzheimer's disease: Alzheimer's Association Workgroup.

The National Institute on Aging and the Alzheimer's Association convened three separate work groups in 2011 and single work groups in 2012 and 2018 to create recommendations for the diagnosis and characterization of Alzheimer's disease (AD). The present document updates the 2018 research framework in response to several recent developments. Defining diseases biologically, rather than based on syndromic presentation, has long been standard in many areas of medicine (e.g., oncology), and is becoming a unifying concept common to all neurodegenerative diseases, not just AD. The present document is consistent with this principle. Our intent is to present objective criteria for diagnosis and staging AD, incorporating recent advances in biomarkers, to serve as a bridge between research and clinical care. These criteria are not intended to provide step-by-step clinical practice guidelines for clinical workflow or specific treatment protocols, but rather serve as general principles to inform diagnosis and staging of AD that reflect current science. HIGHLIGHTS: We define Alzheimer's disease (AD) to be a biological process that begins with the appearance of AD neuropathologic change (ADNPC) while people are asymptomatic. Progression of the neuropathologic burden leads to the later appearance and progression of clinical symptoms. Early-changing Core 1 biomarkers (amyloid positron emission tomography [PET], approved cerebrospinal fluid biomarkers, and accurate plasma biomarkers [especially phosphorylated tau 217]) map onto either the amyloid beta or AD tauopathy pathway; however, these reflect the presence of ADNPC more generally (i.e., both neuritic plaques and tangles). An abnormal Core 1 biomarker result is sufficient to establish a diagnosis of AD and to inform clinical decision making throughout the disease continuum. Later-changing Core 2 biomarkers (biofluid and tau PET) can provide prognostic information, and when abnormal, will increase confidence that AD is contributing to symptoms. An integrated biological and clinical staging scheme is described that accommodates the fact that common copathologies, cognitive reserve, and resistance may modify relationships between clinical and biological AD stages.

[Clinical Evaluation of Dural (Spinal) Membrane Repair Materials].

Objective: To select high-quality and cost-effective dural (spinal) membrane repair materials, in order to reduce the cost of consumables procurement, save medical insurance funds, and optimize hospital operation and management. Methods: Taking the BS06B disease group (spinal cord and spinal canal surgery without extremely severe or severe complications and comorbidities, mainly diagnosed as congenital tethered cord syndrome) as an example, a retrospective analysis was conducted on the relevant data of surgical treatment for congenital tethered cord syndrome conducted in our hospital from January 2021 to June 2023. Safety and efficacy indicators in clinical application (incidence of postoperative epidural hemorrhage, incidence of postoperative purulent cerebrospinal meningitis, incidence of cerebrospinal fluid leakage, surgical duration, and postoperative hospital stay) were compared. Results: There was no difference in safety and effectiveness between different brands of dura mater repair materials. Conclusion: For the repair of small incisions in dura mater surgery, high-quality and cost-effective dura mater repair materials can be selected to reduce hospital costs and control expenses for the disease group.

Extracellular Vesicle Preparation and Analysis: A State-of-the-Art Review.

In recent decades, research on Extracellular Vesicles (EVs) has gained prominence in the life sciences due to their critical roles in both health and disease states, offering promising applications in disease diagnosis, drug delivery, and therapy. However, their inherent heterogeneity and complex origins pose significant challenges to their preparation, analysis, and subsequent clinical application. This review is structured to provide an overview of the biogenesis, composition, and various sources of EVs, thereby laying the groundwork for a detailed discussion of contemporary techniques for their preparation and analysis. Particular focus is given to state-of-the-art technologies that employ both microfluidic and non-microfluidic platforms for EV processing. Furthermore, this discourse extends into innovative approaches that incorporate artificial intelligence and cutting-edge electrochemical sensors, with a particular emphasis on single EV analysis. This review proposes current challenges and outlines prospective avenues for future research. The objective is to motivate researchers to innovate and expand methods for the preparation and analysis of EVs, fully unlocking their biomedical potential.

A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems.

In modern healthcare, integrating Artificial Intelligence (AI) and Internet of Medical Things (IoMT) is highly beneficial and has made it possible to effectively control disease using networks of interconnected sensors worn by individuals. The purpose of this work is to develop an AI-IoMT framework for identifying several of chronic diseases form the patients' medical record. For that, the Deep Auto-Optimized Collaborative Learning (DACL) Model, a brand-new AI-IoMT framework, has been developed for rapid diagnosis of chronic diseases like heart disease, diabetes, and stroke. Then, a Deep Auto-Encoder Model (DAEM) is used in the proposed framework to formulate the imputed and preprocessed data by determining the fields of characteristics or information that are lacking. To speed up classification training and testing, the Golden Flower Search (GFS) approach is then utilized to choose the best features from the imputed data. In addition, the cutting-edge Collaborative Bias Integrated GAN (ColBGaN) model has been created for precisely recognizing and classifying the types of chronic diseases from the medical records of patients. The loss function is optimally estimated during classification using the Water Drop Optimization (WDO) technique, reducing the classifier's error rate. Using some of the well-known benchmarking datasets and performance measures, the proposed DACL's effectiveness and efficiency in identifying diseases is evaluated and compared.

Alterations in Gut Microbiota as Early Biomarkers for Predicting Inflammatory Bowel Disease Onset and Progression: A Systematic Review.

Inflammatory bowel disease (IBD) is a chronic ailment impacting the digestive system, triggered by an unusual reaction of the immune system. It includes two types of diseases: ulcerative colitis and Crohn's disease. Nonetheless, the diagnosis and evaluation of disease progression in IBD are difficult due to the absence of distinct indicators. While conventional biomarkers from blood plasma and feces, such as C-reactive protein, fecal calprotectin, and S100A12, can be employed to gauge inflammation, they are not exclusive to IBD. There is a broad consensus that intestinal microorganisms significantly contribute to the onset of intestinal imbalance, a condition intimately linked with the cause and development of IBD. Numerous studies have indicated that the makeup of intestinal microorganisms varies between individuals with IBD and those who are healthy, particularly concerning the diversity of microbes and the proportional prevalence of certain bacteria. A total of 1475 records underwent examination. Following the eligibility assessment, 17 reports were considered. The final review encompassed 12 studies, as five articles were excluded due to insufficient details regarding cases, controls, and comparability. This article suggests that gut microbiota has potential biomarkers for the noninvasive evaluation of IBD activity. Recognizing the microbiome linked with disease activity paves the way for the development of a group of microbiota-derived indicators to evaluate the initiation and advancement of IBD. This article discusses whether changes in gut microbial composition can serve as early indicators of IBD onset and progression.

Novel CAD Diagnosis Method Based on Search, PCA, and AdaBoostM1 Techniques.

Background: Cardiovascular diseases (CVDs) are the primary cause of mortality worldwide, resulting in a growing number of annual fatalities. Coronary artery disease (CAD) is one of the basic types of CVDs, and early diagnosis of CAD is crucial for convenient treatment and decreasing mortality rates. In the literature, several studies use many features for CAD diagnosis. However, due to the large number of features used in these studies, the possibility of early diagnosis is reduced. Methods: For this reason, in this study, a new method that uses only five features-age, hypertension, typical chest pain, t-wave inversion, and region with regional wall motion abnormality-and is a combination of eight different search techniques, principal component analysis (PCA), and the AdaBoostM1 algorithm has been proposed for early and accurate CAD diagnosis. Results: The proposed method is devised and tested on a benchmark dataset called Z-Alizadeh Sani. The performance of the proposed method is tested with a variety of metrics and compared with basic machine-learning techniques and the existing studies in the literature. The experimental results have shown that the proposed method is efficient and achieves the best classification performance, with an accuracy of 91.8%, ever reported on the Z-Alizadeh Sani dataset with so few features. Conclusions: As a result, medical practitioners can utilize the proposed approach for diagnosing CAD early and accurately.

A Lateral Flow-Recombinase Polymerase Amplification Method for Colletotrichum gloeosporioides Detection.

The greater yam (Dioscorea alata), a widely cultivated and nutritious food crop, suffers from widespread yield reduction due to anthracnose caused by Colletotrichum gloeosporioides. Latent infection often occurs before anthracnose phenotypes can be detected, making early prevention difficult and causing significant harm to agricultural production. Through comparative genomic analysis of 60 genomes of 38 species from the Colletotrichum genus, this study identified 17 orthologous gene groups (orthogroups) that were shared by all investigated C. gloeosporioides strains but absent from all other Colletotrichum species. Four of the 17 C. gloeosporioides-specific orthogroups were used as molecular markers for PCR primer designation and C. gloeosporioides detection. All of them can specifically detect C. gloeosporioides out of microbes within and beyond the Colletotrichum genus with different sensitivities. To establish a rapid, portable, and operable anthracnose diagnostic method suitable for field use, specific recombinase polymerase amplification (RPA) primer probe combinations were designed, and a lateral flow (LF)-RPA detection kit for C. gloeosporioides was developed, with the sensitivity reaching the picogram (pg) level. In conclusion, this study identified C. gloeosporioides-specific molecular markers and developed an efficient method for C. gloeosporioides detection, which can be applied to the prevention and control of yam anthracnose as well as anthracnose caused by C. gloeosporioides in other crops. The strategy adopted by this study also serves as a reference for the identification of molecular markers and diagnosis of other plant pathogens.

A Unique Presentation of Secondary Syphilis With Painful Target Lesions.

Syphilis, caused by Treponema pallidum subsp. pallidum, remains a global health challenge, with a significant burden of new cases annually. The disease disproportionately affects men who have sex with men (MSMs) and endemic, low-income regions. While secondary syphilis typically manifests with a polymorphic rash, individuals with human immunodeficiency virus (HIV) coinfection may present with varied signs and symptoms. Here, we report a case of a 21-year-old male student with painful target lesions on his genitalia, deviating from the typical syphilis presentation. He was found to have concurrent molluscum contagiosum and HIV-1 infection. Serologic testing confirmed syphilis and anti-HIV-1 antibodies. Prompt initiation of antiretroviral therapy and benzathine penicillin G led to symptom resolution. This case highlights the importance of recognizing atypical painful target lesions as a potential manifestation of syphilis, especially in patients with HIV coinfection, to ensure timely diagnosis and treatment.

Optimal Screening and Detection Strategies for Cervical Lesions: A Retrospective Study.

Background: Cervical cancer is the fourth most common cancer among women worldwide. Cervical cancer usually develops from human papillomavirus (HPV) infection, which leads to cervical intraepithelial neoplasia (CIN1/2/3) and eventually invasive cervical cancer. Therefore, early-screening and detection of cervical lesions are crucial for preventing and treating cervical cancer. However, different regions have different levels of medical resources and availability of diagnostic methods. There is a need to compare the efficiency of different methods and combinations for detecting cervical lesions and provide recommendations for the optimal screening and detection strategies. Methods: The current clinical methods for screening and detection of cervical lesions mainly include TruScreen (TS), Thinprep cytologic test (TCT), HPV testing, and colposcopy, but their sensitivity and specificity vary and there is no standard protocol recommended. In this study, we retrospectively reviewed 2286 female samples that underwent cervical biopsy and compared the efficiency of different methods and combinations for detecting cervical lesions. Results: HPV screening showed the highest sensitivity for identifying women with CIN2+ cervical lesions compared with other single methods. Our results also showed the importance and necessary of the secondary diagnostic test like TCT and TS as a triage method before colposcopy examination and guided biopsy. Conclusions: Our study provides recommendations for the optimal screening and detection strategies for cervical lesions in different regions with different levels of development. As a non-invasive, easily operated, and portable device, TS is a promising tool to replace TCT for detecting cervical lesions in the health care center with insufficient medical resources.