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Background: Pulmonary hypertension (PH) is a pathophysiological problem that may involve several clinical symptoms and be linked to various respiratory and cardiovascular illnesses. Its diagnosis is made invasively by Right Cardiac Catheterization (RHC), which is difficult to perform routinely. Aim of the current study was to develop a Machine Learning (ML) algorithm based on the analysis of anamnestic data to predict the presence of an invasively measured PH. Methods: 226 patients with clinical indication of RHC for suspected PH were enrolled between October 2017 and October 2020. All patients underwent a protocol of diagnostic techniques for PH according to the recommended guidelines. Machine learning (ML) approaches were considered to develop classifiers aiming to automatically detect patients affected by PH, based on the patient’s characteristics, anamnestic data, and non-invasive parameters, transthoracic echocardiography (TTE) results and spirometry outcomes. Results: Out of 51 variables of patients undergoing RHC collected, 12 resulted significantly different between patients who resulted positive and those who resulted negative at RHC. Among them 8 were selected and utilized to both train and validate an Elastic-Net Regularized Generalized Linear Model, from which a risk score was developed. The AUC of the identification model is of 83 % with an overall accuracy of 74 % [95 % CI (61 %, 84 %)], indicating very good discrimination between patients with and without the pathology. Conclusions: The PH-targeted ML models could streamline routine screening for PH, facilitating earlier identification and better RHC referrals.

Lung cancer is the main cause of cancer-related deaths worldwide. Due to lack of obvious clinical symptoms in the early stage of the lung cancer, it is hard to distinguish between malignancy and pulmonary nodules. Understanding the immune responses in the early stage of malignant lung cancer patients may provide new insights for diagnosis. Here, using high-through-put sequencing, we obtained the TCRβ repertoires in the peripheral blood of 100 patients with Stage I lung cancer and 99 patients with benign pulmonary nodules. Our analysis revealed that the usage frequencies of TRBV, TRBJ genes, and V-J pairs and TCR diversities indicated by D50s, Shannon indexes, Simpson indexes, and the frequencies of the largest TCR clone in the malignant samples were significantly different from those in the benign samples. Furthermore, reduced TCR diversities were correlated with the size of pulmonary nodules. Moreover, we built a backpropagation neural network model with no clinical information to identify lung cancer cases from patients with pulmonary nodules using 15 characteristic TCR clones. Based on the model, we have created a web server named “Lung Cancer Prediction” (LCP), which can be accessed at http://i.uestc.edu.cn/LCP/index.html.

Objective: Fitz-Hugh-Curtis Syndrome (FHCS) is an inflammation of the liver capsule as a complication of pelvic inflammatory disease (PID) in sexually active women, mostly associated with Chlamydia trachomatis (C. trachomatis) and Neisseria gonorrhoeae. Classically presenting as sharp right upper quadrant pain, usually accompanied salpingitis and ascites. With nonspecific clinical presentation and poor specificity, definitive diagnosis needs tissue biopsy and culture by laparoscopy. Case report: We report the case of a 22-year-old female with a 2-month history of abdominal pain and distention. Symptomatic relief when supportive treatments were given, with the ultrasound and PET-CT suggested advanced bilateral ovarian cancer. After metagenomic next-generation sequencing (mNGS) detected C. trachomatis in ascitic fluid. Following anti-infective medication, clinical improvement was satisfactory and the patient was discharged. Conclusion: FHCS with distention was rare and challenging to diagnose. The mNGS would be a potent, non-invasive pathogen detection method with significant sensitivity and specificity.

This work reports the development of an electrochemical sensor using graphene-peptide conjugates for detecting colorectal cancer (CRC) biomarker leucine-rich alpha-2 glycoprotein-1 (LRG1). To enable LRG1 quantification, we rationally designed peptides with dual graphene anchoring motifs for optimal orientation and binding activity when immobilized on a reduced graphene oxide (rGO) electrochemical transducer surface. The graphene nanomaterial provides several advantages such as high conductivity, large surface area, and excellent stability that can enhance the sensor's analytical performance metrics. Furthermore, the synthetic peptides offer benefits like smaller size, specificity, ease of modification and cost-effective production compared to traditional antibody receptors. Under optimized conditions, the peptide sensor exhibited high sensitivity of 22.3 μA/(ng/mL.cm2), low limit of detection (75 pg/mL LRG1 in serum), accuracy of 101.1 % spiked recovery, and precision within 6 % RSD. Testing with colonoscopy-classified patient serum specimens discriminated normal, precancerous adenomatous polyps and malignant carcinoma stages based on LRG1 overexpression. A 24 % elevation for adenomas and 103 % higher levels in CRC were observed. Validation with spiked plasma samples indicated 97–104 % recovery and

Objective: To develop a ultrasound images based dual-channel deep learning model to achieve accurate early diagnosis of thyroid nodules less than 1 cm. Methods: A dual-channel deep learning model called thyroid nodule transformer network (TNT-Net) was proposed. The model has two input channels for transverse and longitudinal ultrasound images of thyroid nodules, respectively. A total of 9649 nodules from 8455 patients across five hospitals were retrospectively collected. The data were divided into a training set (8453 nodules, 7369 patients), an internal test set (565 nodules, 512 patients), and an external test set (631 nodules, 574 patients). Results: TNT-Net achieved an area under the curve (AUC) of 0.953 (95 % confidence interval (CI): 0.934, 0.969) on the internal test set and 0.941 (95 % CI: 0.921, 0.957) on the external test set, significantly outperforming traditional deep convolutional neural network models and single-channel swin transformer model, whose AUCs ranged from 0.800 (95 % CI: 0.759, 0.837) to 0.856 (95 % CI: 0.819, 0.881). Furthermore, feature heatmap visualization showed that TNT-Net could extract richer and more energetic malignant nodule patterns. Conclusion: The proposed TNT-Net model significantly improved the recognition capability for thyroid nodules with size less than 1 cm. This model has the potential to reduce overdiagnosis and overtreatment of such nodules, providing essential support for precise management of thyroid nodules while complementing fine-needle aspiration biopsy.

Distorted body representations play a major role in the onset and maintenance of Schizophrenia. However, these distortions are difficult to assess because explicit assessments can provoke intense fears about the body and require a good insight. We proposed an implicit motor imagery task to a 14-year-old girl with Early-Onset Schizophrenia. The test consisted of presenting different openings varying in width. For each aperture, the young girl has to say if she could pass through without turning her shoulders. A critical aperture is determined as the first aperture for which she considered she could no longer pass, compared to her shoulders' width. The girl perceived herself as 51 % wider than she was, indicating a significant oversized body schema. The implicit assessments of body schema generate less anxiety and does not require a great level of insight; moreover, those are promising tools for early detection of disease in prodromal phases of Schizophrenia and assistance with differential diagnosis.

Summary: Background: Large language models (LLMs) have shown promising performance in various healthcare domains, but their effectiveness in identifying specific clinical conditions in real medical records is less explored. This study evaluates LLMs for detecting signs of cognitive decline in real electronic health record (EHR) clinical notes, comparing their error profiles with traditional models. The insights gained will inform strategies for performance enhancement. Methods: This study, conducted at Mass General Brigham in Boston, MA, analysed clinical notes from the four years prior to a 2019 diagnosis of mild cognitive impairment in patients aged 50 and older. We developed prompts for two LLMs, Llama 2 and GPT-4, on Health Insurance Portability and Accountability Act (HIPAA)-compliant cloud-computing platforms using multiple approaches (e.g., hard prompting, retrieval augmented generation, and error analysis-based instructions) to select the optimal LLM-based method. Baseline models included a hierarchical attention-based neural network and XGBoost. Subsequently, we constructed an ensemble of the three models using a majority vote approach. Confusion-matrix-based scores were used for model evaluation. Findings: We used a randomly annotated sample of 4949 note sections from 1969 patients (women: 1046 [53.1%]; age: mean, 76.0 [SD, 13.3] years), filtered with keywords related to cognitive functions, for model development. For testing, a random annotated sample of 1996 note sections from 1161 patients (women: 619 [53.3%]; age: mean, 76.5 [SD, 10.2] years) without keyword filtering was utilised. GPT-4 demonstrated superior accuracy and efficiency compared to Llama 2, but did not outperform traditional models. The ensemble model outperformed the individual models in terms of all evaluation metrics with statistical significance (p

Neurodegenerative diseases (NDs) have been a group of disorders characterized by neuronal death and functional loss including Alzheimer's disease (AD), Parkinson's disease (PD) and Huntington's disease (HD), etc. Since diagnostic methods for the NDs biomarkers have been based on traditional enzyme linked immunosorbent assay (ELISA) or Western Blotting, the antibody intrinsic drawbacks such as large molecular weight and high cost make early diagnosis of NDs quite challenging. The aptamers bridge this gap effectively and their versatile features such as small molecular weight, easy modification, high stability and low cost have been widely used to fabricate biosensors to diagnose NDs. Various aptamer-based biosensors have shown excellent performance in the early diagnosis of NDs. Several biomarkers including nucleic acid, protein, biomolecule, and exosome has been used as early diagnostic indicators for NDs. In this minireview, we aim to report the latest progress in aptasensors (including colorimetric, fluorescence, electrochemical and electrochemiluminescence, etc) for detecting NDs protein biomarkers, summarize principles of their functions along with challenges in the way to diagnose NDs and emphasize their future perspectives.

The high morbidity and mortality rate of individuals with human immunodeficiency virus (HIV) / acquired immunodeficiency syndrome (AIDS) coinfected with Mycobacterium tuberculosis (MTB) is a tough challenge for current global tuberculosis prevention and control efforts. HIV/MTB coinfection is more complex than a single infection, and the interaction between the two diseases aggravates the deterioration caused by the disease, resulting in increased hospitalizations and deaths. Rapid screening and early diagnosis facilitate the timely initiation of anti-tuberculosis treatment in HIV/MTB coinfected individuals, thereby reducing transmission and the incidence of adverse prognoses. To date, pathogenic detection has remained the gold standard for diagnosing tuberculosis, but its sensitivity and specificity are greatly affected by the body's immune status, which limits its application in the diagnosis of HIV/MTB coinfection. Recently, immunology and molecular detection technology has developed rapidly. New detection technologies, such as interferon-γ release assays, interferon-gamma inducible protein 10, and GeneXpert MTB/RIF assay have overcome the limitations of traditional detection methods, significantly improved the sensitivity and specificity of tuberculosis diagnosis, and brought new hope to the detection of HIV/MTB coinfection. In this article, the principle, scope of application, and latest research progress of relevant detection methods are reviewed to provide a reference for the early diagnosis of HIV/MTB coinfection.

Resumen: Objetivo: Analizar la concordancia entre un hemoglobinómetro con muestra de sangre capilar y el laboratorio clínico con muestra de sangre venosa. Diseño: Estudio transversal de concordancia. Emplazamiento: Centro de Atención Primaria Bufalà-Canyet y Centro de Atención Primaria Dalt la Vila, Badalona, Barcelona. Participantes: Selección aleatoria de los participantes que acudían a analítica de rutina. Mayores de 18 años. Intervenciones y mediciones principales: Se recogieron el sexo, la edad y el motivo de la analítica de la historia clínica, se realizó extracción de sangre venosa y, de forma simultánea, la extracción de sangre capilar. Resultados: Se incluyen 120 individuos, con una media de edad de 58,9 años. El gráfico de Bland-Almant mostró diferencias dentro de los intervalos de confianza para hemoglobina y hematocrito. La media de las diferencias de los valores del hemoglobinómetro Verio Q Red respecto a los del laboratorio clínico, para la hemoglobina fue de −0,42 mg/dl, y para el hematocrito, de −1,25%. El coeficiente de correlación intraclase mostró concordancia excelente para hemoglobina y hematocrito entre el hemoglobinómetro Verio Q Red y el laboratorio clínico. La correlación de Pearson para hemoglobina fue 0,737, y para el hematocrito, 0,787. Conclusiones: El hemoglobinómetro Verio Q es una herramienta válida para el diagnóstico precoz de las anemias y puede ser de gran utilidad en las consultas de atención primaria. Abstract: Objective: To analyze the concordance between a hemoglobinometer with capillary blood sample and the clinical laboratory with a venous blood sample. Design: Cross-sectional concordance study. Location: Care Centre Primary Bufalà-Canyet Primary and Care Centre Primary Dalt la Vila Primary Care Center, Badalona, Barcelona. Participants: Random selection of participants who attended routine blood tests. Over 18 years old. No abandonment or loss was obtained. Main interventions and measurements: Sex, age, and reason for the blood test were collected from the medical history. Venous blood is drawn, and simultaneously, capillary blood is collected. Results: 120 individuals are included, with an average age of 58.9 years. The Bland-Almant graph showed differences within the confidence intervals for hemoglobin and hematocrit. The mean differences between the values of the Verio Q Red hemoglobinometer and those of the clinical laboratory were −0.42 mg/dl for hemoglobin and −1.25% for hematocrit. The intraclass correlation coefficient showed excellent correlation for hemoglobin and hematocrit between the Verio Q Red hemoglobinometer and the clinical laboratory. Pearson's correlation for hemoglobin was 0.737 and for hematocrit 0.787. Conclusions: The Verio Q hemoglobinometer is a valid tool for the early diagnosis of anemias and can be very useful in primary care consultations.

The current existing classifiers for distinguishing malignant from benign pulmonary nodules is limited by effectiveness or clinical practicality. In our study, we aimed to develop and validate a gene classifier for lung cancer diagnosis. To identify the genes involved in this process, we used the weighted gene co-expression network analysis to analyze gene expression datasets from Gene Expression Omnibus (GEO). We identified the three most relevant modules associated with malignant nodules and performed functional enrichment analysis on them. The results indicated significant involvement in metabolic, immune-related, cell cycle, and viral-related processes. All three modules showed enrichment in metabolic reprogramming pathways. Based on these genes, we intersected genes from the three modules with metabolic reprogramming-related genes and further intersected with differentially expressed genes to get 78 genes. After machine learning algorithms and manual selection, we finally got a nine-gene classifier consisting of SEC24D, RPSA, PSME3, PSMD8, PSMB7, NCOA1, MED12, LPCAT1, and AKR1C3. Our developed and validated classifier-based model demonstrated good discrimination, with an area under the curve (AUC) of 0.763 in the development cohort, 0.744 in the internal validation cohort, and 0.718 in the external validation cohort, and outperformed previous clinical models. Moreover, the addition of nodule size improved the predictive capability of the classifier. We further verify the expression of the gene in the classifier using TCGA lung cancer samples and found eight of the genes showed significant differential expression in lung adenocarcinoma while all nine genes showed significant differential expression in lung squamous carcinoma. Our findings underscore the significance of metabolic reprogramming pathways in patients with malignant pulmonary nodules, and our gene classifier can assist clinicians in differentiating benign from malignant pulmonary nodules in clinical settings.

Adaptive Optics (AO) has emerged as a revolutionary technology in ophthalmology, offering an unprecedented view into the eye's microstructures with high-resolution imaging capabilities. Originally developed for astronomy, AO technology has been adapted to correct the eye’s optical aberrations, enabling the visualization of individual cellular structures such as photoreceptors, retinal pigment epithelium cells, and capillaries within the retinal vasculature. This review provides a comprehensive overview of AO, discussing its historical background, technical principles, and incorporation into existing systems. We explore its transformative impact on ophthalmic research and clinical practice, highlighting its role in enhancing our understanding of ocular physiology, disease progression, and response to therapies. The clinical applications of AO, including early disease detection and monitoring, are examined alongside the patient experience and economic considerations. Despite its potential, AO's widespread adoption is currently limited by factors such as high costs, technical complexity, and patient cooperation challenges. We discuss these barriers and the innovative solutions emerging to overcome them, including system simplification, expanded fields of view, and advanced image analysis techniques. The article concludes by reflecting on the promising future of AO, with its evolving role in disease management, surgical planning, and systemic disease monitoring. As AO technology continues to advance, it promises to reshape the landscape of ophthalmic care, offering deeper insights into eye health and more precise patient care.

Budd-Chiari syndrome (BCS) is a life-threatening disease characterized by the partial or complete obstruction of hepatic venous outflow anywhere from the liver to the heart. In China, secondary BCS is rare. We present a case of secondary BCS caused by compression of the suprahepatic inferior vena cava (IVC), mainly due to local bile accumulation in the caudate lobe of the liver. This case highlights the scarcity of secondary BCS worldwide and the importance of point-of-care ultrasound (POCUS) in the diagnosis and treatment, especially in critical and comatose patients. Prompt diagnosis and recanalization with POCUS-guided puncture and drainage help improve patient prognosis.

This paper presents an innovative framework for the automated diagnosis of gastric cancer using artificial intelligence. The proposed approach utilizes a customized deep learning model called MobileNetV2, which is optimized using a Dynamic variant of the Pelican Optimization Algorithm (DPOA). By combining these advanced techniques, it is feasible to achieve highly accurate results when applied to a dataset of endoscopic gastric images. To evaluate the performance of the model based on the benchmark, its data is divided into training (80 %) and testing (20 %) sets. The MobileNetV2/DPOA model demonstrated an impressive accuracy of 97.73 %, precision of 97.88 %, specificity of 97.72 %, sensitivity of 96.35 %, Matthews Correlation Coefficient (MCC) of 96.58 %, and F1-score of 98.41 %. These results surpassed those obtained by other well-known models, such as Convolutional Neural Networks (CNN), Mask Region-Based Convolutional Neural Networks (Mask R–CNN), U-Net, Deep Stacked Sparse Autoencoder Neural Networks (SANNs), and DeepLab v3+, in terms of most quantitative metrics. Despite the promising outcomes, it is important to note that further research is needed. Specifically, larger and more diverse datasets as well as exhaustive clinical validation are necessary to validate the effectiveness of the proposed method. By implementing this innovative approach in the detection of gastric cancer, it is possible to enhance the speed and accuracy of diagnosis, leading to improved patient care and better allocation of healthcare resources.

Background: Early detection of cancer remains an unmet need in clinical practice, and high diagnostic sensitivity and specificity biomarkers are urgently required. Here, we attempted to identify secreted proteins encoded by super-enhancer (SE)-driven genes as diagnostic biomarkers for esophageal squamous cell carcinoma (ESCC). Methods: We conducted an integrative analysis of multiple data sets including ChIP-seq data, secretome data, CCLE data and GEO data to screen secreted proteins encoded by SE-driven genes. Using ELISA, we further identified up-regulated secreted proteins through a small size of clinical samples and verified in a multi-centre validation stage (345 in test cohort and 231 in validation cohort). Receiver operating characteristic curves were used to calculate diagnostic accuracy. Artificial intelligence (AI) method named gradient boosting machine (GBM) were applied for model construction to enhance diagnostic accuracy. Results: Serum EFNA1 and MMP13 were identified, and showed significantly higher levels in ESCC patients compared to normal controls. An integrated Five-Biomarker Panel (iFBPanel) established by combining EFNA1, MMP13, carcino-embryonic antigen, Cyfra21-1 and squmaous cell carcinoma antigen had AUCs of 0.881 and 0.880 for ESCC in test and validation cohorts, respectively. Importantly, the iFBPanel also exhibited good performance in detecting early-stage ESCC patients (0.872 and 0.864). Furthermore, the iFBPanel was further empowered by AI technology which showed excellent diagnostic performance in early-stage ESCC (0.927 and 0.907). Conclusions: Our study suggested that serum EFNA1 and MMP13 could potentially assist ESCC detection, and provided an easy-to-use detection model that might help the diagnosis of early-stage ESCC.

Background: Osteoarthritis (OA) is a common chronic joint disease. This study aimed to investigate possible OA diagnostic biomarkers and to verify their significance in clinical samples. Methods: We exploited three datasets from the Gene Expression Omnibus (GEO) database, serving as the training set. We first determined differentially expressed genes and screened candidate diagnostic biomarkers by applying three machine learning algorithms (Random Forest, Least Absolute Shrinkage and Selection Operator logistic regression, Support Vector Machine-Recursive Feature Elimination). Another GEO dataset was used as the validation set. The test set consisted of RNA-sequenced peripheral blood samples collected from patients and healthy donors. Blood samples and chondrocytes were collected for quantitative real-time PCR to confirm expression levels. Receiver operating characteristic curves were generated for individual and combined biomarkers. Results: In total, 251 DEGs were screened, where B3GALNT1, SCRG1 and ZNF423 were screened by all three algorithms. The area under the curve (AUC) of various biomarkers in our test set did not reach as high as that in public datasets. GRB10 exhibited highest AUC of 0.947 in the training set but 0.691 in our test set, while the favorable combined model comprising B3GALNT1, GRB10, KLF9 and SCRG1 demonstrated an AUC of 0.986 in the training set, 1.000 in the validation set and 0.836 in our test set. Conclusion: We identified a combined model for early diagnosis of OA that includes B3GALNT1, GRB10, KLF9 and SCRG1. This finding offers new avenues for further exploration of mechanisms underlying OA.

Background: In Equatorial Guinea, only 54 % of people living with HIV know their HIV status. There are no confirmatory or molecular diagnostic techniques for early diagnosis or monitoring of infection in the country. Rapid diagnostic tests can induce false-positive diagnoses if used as a confirmatory technique. Our study aimed to identify the challenges of early HIV diagnosis in Equatorial Guinea by analyzing the rate of false positive diagnoses, diagnostic and therapeutic delays, and treatment failures among those on antiretroviral therapy. Methods: From 2019–2022, dried blood from 341 children, adolescents and adults diagnosed in Equatorial Guinea as HIV-positive by rapid diagnostic testing, and from 54 HIV-exposed infants were collected in Bata and sent to Madrid to confirm HIV-infection by molecular (Xpert HIV-1Qual, Cepheid) and/or serological confirmatory assays (Geenius-HIV-1/2, BioRad). HIV diagnostic delay (CD4 1,000RNA-HIV-1-copies/ml) were also studied after viral quantification (XpertVL HIV-1, Cepheid). Results: False-positive diagnoses were identified in 5 % of analysed samples. HIV infection was confirmed in 90.5 % of previously diagnosed patients in Equatorial Guinea and 3.7 % of HIV-exposed children undiagnosed in the field. Two-thirds of each new HIV patient had delayed diagnosis, and one-third had advanced disease. Treatment delay occurred in 28.3 % of patients, being around four times more likely in adolescents/adults than children. More than half (56 %) of 232 treated patients presented treatment failure, being significantly higher in children/adolescents than in adults (82.9 %/90 % vs. 45.6 %, p

Objective: No standardised approach exists to provide advice after urgent suspected cancer (USC) referral when cancer is not found. This study aimed to assess preferences and acceptability of receiving advice after USC referral related to: 1) managing ongoing symptoms, 2) responding to early symptoms of other cancers, 3) cancer screening, 4) reducing risks of future cancer. Methods: 2,541 patients from two English NHS Trusts were mailed a survey 1–3 months after having no cancer found following urgent suspected gastrointestinal or head and neck cancer referral. Participants were asked about: willingness to receive advice; prospective acceptability; preferences related to mode, timing and who should provide advice; and previous advice receipt. Results: 406 patients responded (16.0%) with 397 in the final analyses. Few participants had previously received advice, yet most were willing to. Willingness varied by type of advice: fewer were willing to receive advice about early symptoms of other cancers (88.9%) than advice related to ongoing symptoms (94.3%). Acceptability was relatively high for all advice types. Reducing the risk of future cancer advice was more acceptable. Acceptability was lower in those from ethnic minority groups, and with lower levels of education. Most participants preferred to receive advice from a doctor; with results or soon after; either face to face or via the telephone. Conclusions: There is a potential unmet need for advice after USC referral when no cancer is found. Equitable intervention design should focus on increasing acceptability for people from ethnic minority groups and those with lower levels of education.

Odontogenic keratocysts (OKCs) are a common presentation in almost all patients with nevoid basal cell carcinoma syndrome (NBCCS) or Gorlin syndrome, irrespective of race. In most patients with NBCCS, OKC presents as multiple lesions affecting the jaws which makes it a signpost for the investigation of patients with the syndrome. In approximately 40% of pediatric patients, the initial presentation is that of a single OKC, which may often result in missing the diagnosis of NBCCS. This is particularly common in patients without clinically apparent NBCCS-related manifestations. This review examines the clinicopathological features that clinicians and oral pathologists may look for in pediatric patient with OKC and OKC surgical specimens that may serve as indicators for the diagnosis of NBCCS. Although these features do not diagnose NBCCS by themselves, they may significantly help in initiating the diagnostic process at an early stage with an obvious benefit to the child and relatives.

Obstructed hemivagina and ipsilateral renal agenesis syndrome is a rare complex of structural abnormalities of female urogenital tract. Moreover, delayed treatment of this condition can reportedly lead to pelvic infection, endometriosis, and infertility. We present a rare case of obstructed hemivagina and ipsilateral renal agenesis syndrome wherein appropriate treatment was initiated early due to the identification of left renal agenesis. A 14-year-old girl visited the gynecology department due to worsening left lower abdominal pain after menarche. She was diagnosed with renal dysplasia in the pediatric department at the ages of 3 and 9 years, and it was recommended that she visits the gynecology department right after menarche. She experienced her first menarche at the age of 14 years. Imaging studies revealed uterine didelphys with occlusion of the left vagina and formation of hematocolpos. Additionally, hematomas were observed in the left uterine cavity and the left fallopian tube; left renal agenesis was confirmed. Based on these findings, we diagnosed this patient with obstructed hemivagina and ipsilateral renal agenesis syndrome and performed a surgical incision and excision of the closed left vaginal wall, resulting in the disappearance of the hematocolpos. Approximately 2 years postoperatively, the patient has remained recurrence-free. Early diagnosis and treatment are crucial for preserving fertility in patients with obstructed hemivagina and ipsilateral renal agenesis syndrome, and early referral to the obstetrics and gynecology department is recommended if renal dysplasia is detected during childhood.

Objective: This study postulates that distinct patterns in salivary gene expression clusters among HNSCC patients provide valuable insights into diagnostic and prognostic markers for the disease. The aim is to identify critical genes and their protein-protein interactions through bio-computational analyses, contributing to a deeper understanding of HNSCC progression. Ultimately, the hypothesis anticipates the discovery of novel molecular signatures in saliva, potentially enhancing early detection and prognostic precision in head and neck squamous cell carcinoma. Materials and methods: A comprehensive keyword search was conducted to extract 74 salivary gland secretion genes from the Human Protein Atlas. GeneMANIA and STRING databases were employed to analyse protein-protein interactions and predict functional partners. UALCAN facilitated the visualization of salivary gland secretion gene expression patterns in HNSCC, providing insights into survival analysis. Results: In HNSCC compared to normal tissue, CHRM3, GYLTL1B, MMP3, and WDR91 were identified as statistically upregulated genes, while the downregulated genes included AGFG2, CCDC64B, CD24, CRISP3, CXCL17, ELF5, KRT7, MANSC1, MYO5C, PRR4, SCGB3A1, SH3YL1, TCN1, TRPV6, and ZG16B. Kaplan–Meier survival curve analysis revealed that high expression of salivary genes such as AGFG2, AMY1A, CCDC64B, CCL28, C4ORF7, GYLTL1B, TMEM211, LMX1B, ODAM, SCGB3A1, and SLC26A9 was significantly associated with poorer overall survival outcomes in HNSCC patients. Conclusion: This study identifies distinct salivary gene expression patterns in HNSCC, highlighting potential diagnostic and prognostic markers. Specific upregulated genes, such as CHRM3 and GYLTL1B, and downregulated genes, including AGFG2 and CCDC64B, offer insights into HNSCC progression. Elevated expression of particular genes correlates with poorer overall survival, emphasising their prognostic significance.

Summary: Background: Bone metastasis significantly impact the prognosis of non-small cell lung cancer (NSCLC) patients, reducing their quality of life and shortening their survival. Currently, there are no effective tools for the diagnosis and risk assessment of early bone metastasis in NSCLC patients. This study employed machine learning to analyze serum indicators that are closely associated with bone metastasis, aiming to construct a model for the timely detection and prognostic evaluation of bone metastasis in NSCLC patients. Methods: The derivation cohort consisted of 664 individuals with stage IV NSCLC, diagnosed between 2015 and 2018. The variables considered in this study included age, sex, and 18 specific serum indicators that have been linked to the occurrence of bone metastasis in NSCLC. Variable selection used multivariate logistic regression analysis and Lasso regression analysis. Six machine learning methods were utilized to develop a bone metastasis diagnostic model, assessed with Area Under the Curve (AUC), Decision Curve Analysis (DCA), sensitivity, specificity, and validation cohorts. External validation used 113 NSCLC patients from the Medical Alliance (2019–2020). Furthermore, a prospective validation study was conducted on a cohort of 316 patients (2019–2020) who were devoid of bone metastasis, and followed-up for at least two years to assess the predictive capabilities of this model. The model's prognostic value was evaluated using Kaplan–Meier survival curves. Findings: Through variable selection, 11 serum indictors were identified as independent predictive factors for NSCLC bone metastasis. Six machine learning models were developed using age, sex, and these serum indicators. A random forest (RF) model demonstrated strong performance during the training and internal validation cohorts, achieving an AUC of 0.98 (95% CI 0.95–0.99) for internal validation. External validation further confirmed the RF model’s effectiveness, yielding an AUC of 0.97 (95% CI 0.94–0.99). The calibration curves demonstrated a high level of concordance between the anticipated risk and the observed risk of the RF model. Prospective validation revealed that the RF model could predict the occurrence of bone metastasis approximately 10.27 ± 3.58 months in advance, according to the results of the SPECT. An online computing platform (https://bonemetastasis.shinyapps.io/shiny_cls_1model/) for this RF model is publicly available and free-to-use by doctors and patients. Interpretation: This study innovatively employs age, gender, and 11 serological markers closely related to the mechanism of bone metastasis to construct an RF model, providing a reliable tool for the early screening and prognostic assessment of bone metastasis in NSCLC patients. However, as an exploratory study, the findings require further validation through large-scale, multicenter prospective studies. Funding: This work is supported by the National Natural Science Foundation of China (NO.81974315); Shanghai Municipal Science and Technology Commission Medical Innovation Research Project (NO.20Y11903300); Shanghai Municipal Health Commission Health Industry Clinical Research Youth Program (NO.20204Y034).

Autistic Spectrum Disorder (ASD) is a neurological disease characterized by difficulties with social interaction, communication, and repetitive activities. While its primary origin lies in genetics, early detection is crucial, and leveraging machine learning offers a promising avenue for a faster and more cost-effective diagnosis. This study employs diverse machine learning methods to identify crucial ASD traits, aiming to enhance and automate the diagnostic process. We study eight state-of-the-art classification models to determine their effectiveness in ASD detection. We evaluate the models using accuracy, precision, recall, specificity, F1-score, area under the curve (AUC), kappa, and log loss metrics to find the best classifier for these binary datasets. Among all the classification models, for the children dataset, the SVM and LR models achieve the highest accuracy of 100% and for the adult dataset, the LR model produces the highest accuracy of 97.14%. Our proposed ANN model provides the highest accuracy of 94.24% for the new combined dataset when hyperparameters are precisely tuned for each model. As almost all classification models achieve high accuracy which utilize true labels, we become interested in delving into five popular clustering algorithms to understand model behavior in scenarios without true labels. We calculate Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), and Silhouette Coefficient (SC) metrics to select the best clustering models. Our evaluation finds that spectral clustering outperforms all other benchmarking clustering models in terms of NMI and ARI metrics while demonstrating comparability to the optimal SC achieved by k-means. The implemented code is available at GitHub.

Background: Lung adenocarcinoma is one of the leading causes of cancer-related deaths because of the lack of early specific clinical indicators. MicroRNAs (miRNAs) have become the focus in lung cancer diagnosis. Further studies are required to explore miRNA expression in the serum of lung adenocarcinoma patients and their correlation with therapy and analyse specific messenger RNA targets to improve the specificity and sensitivity of early diagnosis. Methods: The Toray 3D-Gene miRNA array was used to compare the expression levels of various miRNAs in the sera of patients with lung adenocarcinoma and healthy volunteers. Highly expressed miRNAs were selected for further analysis. To verify the screening results, serum and pleural fluid samples were analysed using qRT-PCR. Serum levels of the miRNAs and their correlation with the clinical information of patients with lung adenocarcinoma were analysed. The functions of miRNAs were further analysed using the Kyoto Encyclopedia of Gene and Genomes and Gene Ontology databases. Results: Microarray analysis identified 60 and 50 miRNAs with upregulated and downregulated expressions, respectively, in the serum of patients with lung adenocarcinoma compared to those in healthy individuals. Using qRT-qPCR to detection of miRNAs expression in the serum or pleural effusion of patients with early and advanced lung adenocarcinoma, we found that miR-4433a-3p could be used as a diagnostic marker and therapeutic evaluation indicator for lung adenocarcinoma. Serum of miR-4433a-3p levels significantly correlated with the clinical stage. miR-4433a-3p may be more suitable than other tumour markers for the early diagnosis and evaluation of therapeutic effects in lung adenocarcinoma. miR-4433a-3p may affect tumour growth and metastasis by acting on target genes (PIK3CD, UBE2J2, ICMT, PRDM16 and others) and regulating tumour-related signalling pathways (MAPK signal pathway, Ras signalling pathway and others). Conclusion: miR-4433a-3p may serve as a biomarker for the early diagnosis of lung adenocarcinoma and monitoring of therapeutic effects.

Biliary atresia is a rare infant disease that predisposes patients to liver transplantation and death if not treated in time. However, early diagnosis is challenging because the clinical manifestations and laboratory tests of biliary atresia overlap with other cholestatic diseases. Therefore, it is very important to develop a simple, safe and reliable method for the early diagnosis of biliary atresia. Herein, a novel NIR-II fluorescence probe, HZL2, with high quantum yield, excellent biocompatibility, low cytotoxicity and rapid excretion through the liver and gallbladder was developed based on the oil/water partition coefficient and permeability. A simple fecal sample after injection of HZL2 can be used to efficiently identify the success of the mouse model of biliary atresia for the first time, allowing for an early diagnosis of the disease. This study not only developed a simple and safe method for the early diagnosis of biliary atresia with great potential in clinical translation but also provides a research tool for the development of pathogenesis and therapeutic medicines for biliary atresia.

Background: The use of central venous catheters (CVC) is associated with higher morbidity and mortality, related to infectious complications, contributing to poorer clinical outcomes and increased healthcare costs. According to the literature, the incidence of local infections related to CVC for hemodialysis is highly variable. This variability is related to differences in definitions of catheter-related infections. Objective: To identify signs and symptoms for determining local infections (exit site and tunnel tract infections) used in the literature in tunnelled and nontunnelled CVC for hemodialysis. Design: Systematic review Methods: Structured electronic searches were conducted in five electronic databases, from 1 January 2000–31 August 2022, using key words and specific vocabulary, as well as manual searches in several journals. Additionally, vascular access clinical guidelines and infection control clinical guidelines were reviewed. Results: After validity analysis, we selected 40 studies and seven clinical guidelines. The definitions of exit site infection and tunnel infection used in the different studies were heterogeneous. Among the studies, seven (17,5 %) used the definitions of exit site and tunnel infection based on a clinical practice guideline. Three of the studies (7.5 %) used the Twardowski scale definition of exit site infection or a modification. The remaining 30 studies (75 %) used different combinations of signs and symptoms. Conclusions: Definitions of local CVC infections are highly heterogeneous in the revised literature. It is necessary to establish a consensus regarding the definitions of hemodialysis CVC exit site and tunnel infections. Registration: PROSPERO (CRD42022351097).

Introduction: Sport-specific adaptations of the glenohumeral joint may arise in adolescent overhead athletes who begin high-performance sports early in life. Research mainly addresses overuse injuries, leaving gaps in prevention, with adults studied more than youths. Objective: This study aims to investigate sport-adaptations of the glenohumeral joint in asymptomatic adolescent volleyball players to identify potential shoulder injury risk factors. Design: Observational study. Setting: Clinical screening campaign conducted at the Physical Medicine and Rehabilitation Unit of Policlinic Hospital in Catania, Italy. Participants: Forty asymptomatic under-16 athletes were evaluated. Interventions: Shoulder internal rotation (IR) and external rotation (ER), range of motion (ROM), total-rotation ROM, glenohumeral IR deficit (GIRD), general joint laxity using Beighton score, apprehension, relocation, O'Brian tests, and ultrasound (US) glenohumeral distance were tested bilaterally. Variables such as the player's position, the age they began the sport, limb dominance, weight, and height were also considered. Results: The median US glenohumeral distance was at 0.42 ± 0.26 cm, which is consistent with the range found in non-dislocated shoulders of a healthy non-athletic population. The ER ROM was significantly greater in the dominant shoulder than the contralateral one (P = 0.0001), and there was a significant correlation between the ER ROM of attackers and their US glenohumeral distance (P = 0.0413). Furthermore, shoulder IR ROM and US glenohumeral distance were not significantly different between the dominant and contralateral limbs (P = 0.05). None of the athletes presented GIRD. Other tests, including the Beighton score, apprehension, and relocation tests, yielded no significant differences between the dominant and contralateral limbs. Conclusions: Despite an increased shoulder ER in the dominant limb, the glenohumeral joint remains stable, suggesting that greater ROM in ER does not equate to instability in overhead athletes without hyperlaxity. Nevertheless, increased ER impacts glenohumeral distance in attacker volleyball players. This finding suggests that the shoulder morphological adaptation process starts early in attackers.

Alzheimer's disease (AD) is the leading cause of dementia and is rapidly becoming one of the most costly, fatal diseases, which is typically discovered in the late stage of molecular pathology, at which point medication intervention is irreversible. As a result, there is an urgent need for a low-cost, least-invasive way of screening cognitive impairment, with the goal of identifying persons at risk of AD. Mild cognitive impairment (MCI) has been described as a transitional state between normal cognitive aging and AD. Early detection and timely tracking of MCI can to some extent prevent the progression towards AD. We found a population in Northwestern China has a comparatively high prevalence of MCI. Continued education, consistent exercise, and a secure financial situation can all help older people maintain cognitive function. Due to the critical role of circulating microRNAs in intercellular signaling and the perturbations thereof, their investigation has assumed paramount significance in elucidating various pathological conditions. Numerous investigations have substantiated the significance of circulating miRNAs specifically in MCI. Here, we evaluated miR-483-5p (Area Under the Curve (AUC) is 0.901, sensitivity 79.2 % and specificity 100 %) and miR-502-5p (AUC is 0.872, sensitivity 79.2 % and specificity 83.3 %), which were derived from plasma exosomes and maintained at high levels in elderly people with MCI, could be employed as promising noninvasive biomarkers.

Diabetes mellitus, a chronic metabolic disorder, continues to be a major public health issue around the world. It is estimated that one in every two diabetics is undiagnosed. Early diagnosis and management of diabetes can also prevent or delay the onset of complications. With the help of a variety of machine learning and deep learning models, stacking algorithms, and other techniques, our study's goal is to detect diseases early. In this study, we propose two stacking-based models for diabetes disease classification using a combination of the PIMA Indian diabetes dataset, simulated data, and additional data collected from a local healthcare facility. We use both the classical and deep neural network stacking ensemble methods to combine the predictions of multiple classification models and improve classification accuracy and robustness. In the evaluation protocol, we used both the train-test and cross-validation (CV) techniques to validate our proposed model. The highest accuracy is obtained by stacking ensemble with three NN architectures, resulting in an accuracy of 95.50 %, precision of 94 %, recall of 97 %, and f1-score of 96 % using 5-fold CV on simulation study. The stacked accuracy obtained from ML algorithms for the Pima Indian Diabetes dataset is 75.03 % using the train-test split protocol, while the accuracy obtained from the CV protocol is 77.10 % on the stacked model. The range of performance scores that outperformed the CV protocol 2.23 %–12 %. Our proposed method achieves a high accuracy range from 92 % to 95 %, precision, recall, and F1-score ranges from 88 % to 96 % using classical and deep neural network (NN)-based stacking method on the primary dataset. The proposed dataset and ensemble method could be useful in the early detection and treatment of diabetes, as well as in the advancement of machine learning and data analysis techniques in the healthcare industry.

Background: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease involving multi-system and multi-organ dysfunction, and is easily misdiagnosed early in the disease course. We aimed to accurately predict early SLE nomogram to provide a reference basis for the early clinical diagnosis of SLE. Methods: We retrospectively analyzed 167 patients who were first diagnosed with SLE at Fengxian District Central Hospital, Shanghai, between March 2017 and October 2022. Three groups of 129 physically healthy subjects, 67 patients with rheumatoid arthritis, and 40 patients with rashes were selected as controls during the same period. Patients with SLE and control group were randomly divided into training (n = 217) and validation (n = 141) group. Univariate and multivariate analyses were used to identify independent risk factors for early SLE diagnosis. The independent risk factors for diagnosis were used to construct a nomogram to predict early SLE. Results: Based on the training group, three variables were identified as independently influencing early SLE: platelets (odds ratio OR = 0.993, P = 0.047), albumin (OR = 0.833, P = 0.007), and complement component 1q (OR = 0.956, P = 0.000). The precision of the nomogram was assessed using C-index values and calibration plot diagrams. The C-index values were 0.929 for training group and 0.898 for validation group. Both the training group and validation group calibration curves showed good predicted outcomes. Conclusion: The construction of a nomogram can accurately predict the risk of early SLE. The model showed good discriminatory power and calibration for use in the diagnosis of SLE, providing a visual tool and reference basis for the early diagnosis of SLE.

COVID-19 is one of the most chronic and serious infections of recent years due to its worldwide spread. Determining who was genuinely affected when the disease spreads more widely is challenging. More than 60% of affected individuals report having a dry cough. In many recent studies, diagnostic models were developed using coughing and other breathing sounds. With the development of technology, body sounds are now collected using digital techniques for respiratory and cardiovascular tests. Early research on identifying COVID-19 utilizing speech and diagnosing signs yielded encouraging findings. The gathering of extensive, multi-group, airborne acoustical sound data is used in the developed framework to conduct an efficient assessment to test for COVID-19. An effective classification model is created to assess COVID-19 utilizing deep learning methods. The MIT-Covid-19 dataset is used as the input, and the Weiner filter is used for pre-processing. Following feature extraction done by Mel-frequency cepstral coefficients, the classification is performed using the CNN-LSTM approach. The study compared the performance of the developed framework with other techniques such as CNN, GRU, and LSTM. Study results revealed that CNN-LSTM outperformed other existing approaches by 97.7%.

We report a patient with anorexia nervosa without bronchiectasis and cystic fibrosis who developed acute pneumonia caused by Exophiala dermatitidis (E. dermatitidis). The black fungus found in multiple sputum cultures was determined to be E. dermatitidis using mass spectrometry and identified using genetic analysis. Although the initiation of antifungal therapy was late, the pneumonia gradually improved with long-term treatment. This case highlights the need for early diagnosis and effective long-term treatment of the fungal etiologic agent.

Mycoplasma contamination in cell/tissue cultures significantly affects cell characteristics, susceptibility to infectious pathogens, and drug reactions without obvious morphological changes and noticeable changes in cell growth rates. Although the technology for screening mycoplasma is essential, conventional technologies have limitations in the selection of a quick and easy manner. Here, we introduce an innovative screening system for mycoplasma based on electrochemical methods. In the electrochemical screening, a newly designed electrode, consisting of a sandwich-DNA hybridization for high selectivity and a nano-amplifier to improve the sensitivity, has implemented a realistic screening system by presenting detection capability at extremely low (ng/μL) levels and detection potential in real samples. Therefore, this system offers rapid screening, data accuracy, temporal effectiveness, and affordable-and-portable installation, enabling timely elimination of Mycoplasma contamination and facilitating progress in biomedical and pharmaceutical research.

Background: Psoriatic arthritis (PsA), an immune-mediated chronic inflammatory skin and joint disease, affects approximately 0.27% of the adult population, and 20% of patients with psoriasis. Up to 10% of psoriasis patients are estimated for having undiagnosed PsA. Early diagnosis and treatment can prevent irreversible joint damage, disability and deformity. Questionnaires for screening to identify undiagnosed PsA patients require patient and physician involvement. Objective: To evaluate a proprietary machine learning tool (PredictAI™) developed for identification of undiagnosed PsA patients 1–4 years prior to the first time that they were suspected of having PsA (reference event). Methods: This retrospective study analyzed data of the adult population from Maccabi Healthcare Service between 2008 and 2020. We created 2 cohorts: The general adult population (“GP Cohort”) including patients with and without psoriasis and the Psoriasis cohort (“PsO Cohort”) including psoriasis patients only. Each cohort was divided into two non-overlapping train and test sets. The PredictAI™ model was trained and evaluated with 3 years of data predating the reference event by at least one year. Receiver operating characteristic (ROC) analysis was used to investigate the performance of the model, built using gradient boosted trees, at different specificity levels. Results: Overall, 2096 patients met the criteria for PsA. Undiagnosed PsA patients in the PsO cohort were identified with a specificity of 90% one and four years before the reference event, with a sensitivity of 51% and 38%, and a PPV of 36.1% and 29.6%, respectively. In the GP cohort and with a specificity of 99% and for the same time windows, the model achieved a sensitivity of 43% and 32% and a PPV of 10.6% and 8.1%, respectively. Conclusions: The presented machine learning tool may aid in the early identification of undiagnosed PsA patients, and thereby promote earlier intervention and improve patient outcomes.

Background: Severe sarcopenia may result in severe disability. Early diagnosis is currently the key to enhancing the treatment of sarcopenia, and there is an urgent need for a highly sensitive and dependable tool to evaluate the course of early sarcopenia in clinical practice. This study aims to investigate longitudinally the early diagnosability of magnetic resonance imaging (MRI)-based fat infiltration and blood flow perfusion technology in sarcopenia progression. Methods: 48 Sprague-Dawley rats were randomly assigned into six groups that were based on different periods of dexamethasone (DEX) injection (0, 2, 4, 6, 8, 10 days). Multimodal MRI was scanned to assess muscle mass. Grip strength and swimming exhaustion time of rats were measured to assess muscle strength and function. Immunofluorescence staining for CD31 was employed to assess skeletal muscle capillary formation, and western blot was used to detect vascular endothelial growth factor-A (VEGF-A) and muscle ring finger-1 (MuRF-1) protein expression. Subsequently, we analyzed the correlation between imaging and histopathologic parameters. A receiver operating characteristic (ROC) analysis was conducted to assess the effectiveness of quantitative MRI parameters for discriminating diagnosis in both pre- and post-modeling of DEX-induced sarcopenic rats. Results: Significant differences were found in PDFF, R2* and T2 values on day 2 of DEX-induction compared to the control group, occurring prior to the MRI-CSA values and limb grip strength on day 6 of induction and swimming exhaustion time on day 8 of induction. There is a strong correlation between MRI-CSA with HE-CSA values (r = 0.67; p

Precise diagnosis of early prostate cancer (PCa) is critical for preventing tumor progression. However, the diagnostic outcomes of currently used markers are far from satisfactory due to the low sensitivity or specificity. Here, we identified a diagnostic subpopulation in PCa tissue with the integrating analysis of single-cell and bulk RNA-seq. The representative markers of this subpopulation were extracted to perform intersection analysis with early-PCa-related gene module generated from weighted correlation network analysis (WGCNA). A total of 24 overlapping genes were obtained, the diagnostic roles of which were validated by distinguishing normal and tumorous prostate samples from the public dataset. A least absolute shrinkage and selection operator (LASSO) model was constructed based on these genes and the obtained 24-gene panel showed high sensitivity and specificity for PCa diagnosis, with better identifying capability of PCa than the commercially used gene panel of Oncotype DX. The top two risk factors, TRPM4 and PODXL2, were verified to be highly expressed in early PCa tissues by multiplex immunostaining, and PODXL2 was more sensitive and specific compared to TRPM4 and the pathologically used marker AMACR for early PCa diagnosis, suggesting a novel and promising pathology marker.

Background: it is known that intrauterine growth retardation (IUGR) represents a risk factor for the deterioration of renal function as it can adversely impact on the number of nephrons developed in the kidney during nephrogenesis. An interesting molecule is the Cystatin-C (cyst-C): it is considered to have the potency to detect both glomerular and proximal renal injury. Recently, using a quantitative EIA cyst-C detection kit, we found increased levels of cyst-C in the urine of neonates with IUGR. Since cyst-C molecules can be present in both monomer and/or polymer forms, the purpose of this study is to investigate in which forms this molecule is present in the urine of IUGR neonates by Immunoblot SDS-PAGE in order to verify if the presence or absence of a particular type of cyst-C conformation can give more information about the renal functioning. Methods: urine samples were collected from 64 neonates with IUGR, and 86 healthy controls defined as appropriate for gestational age (AGA). Urinary cyst-C was investigated by the Immunoblot SDS-PAGE. Results: in all urine samples, SDS-PAGE analysis showed a reactivity of the IgG anti cyst-C with a complex of about 70 kDa. The monomer form at 13 KDa appeared in 78% of IUGR neonates and in 12% of AGA neonates. Conclusions: this study revealed the presence of monomer cyst-C in the urine of IUGR neonates, and suggests an insufficient and/or non-compensatory reabsorption by tubular cells. Monomeric cyst-C can be considered an early biochemical marker to identify and to select IUGR neonates who need to be monitored for risk of renal injury.

According to WHO and other key statistics, cancer is a leading cause of death worldwide, accounting for nearly 10 million deaths in 2020. By identifying cancer early, it is more likely to respond to treatment, resulting in a better chance of survival as well as a lower cost of treatment. Early detection of cancer and avoiding delays in treatment can significantly improve the lives of cancer patients. Nanomaterial-based sensors with the ability to extract and identify tumor-specific biomarkers, circulating tumor cells, or extracellular vesicles secreted by the tumor have the potential to diagnose cancer considerably earlier and enhance patient long-term survival. In this review, we provide the application of nanosensors for the early detection of cancer and the recent techniques for clinically diagnosing cancer patients' biomarker levels. Furthermore, the presented works are compared based on their sensitivity and selectivity in the detection of different kinds of cancer biomarkers.

Women with a family history of breast cancer have an increased lifetime risk of the disease. Delay in symptom presentation can lead to poorer outcomes. Low awareness of breast cancer symptoms and help-seeking barriers have been associated with delay in presentation in the general population. Symptom awareness and help-seeking barriers among women at increased risk of breast cancer are unknown. We conducted analysis of survey data which included women with moderate and high risk of breast cancer from 20 secondary and tertiary care clinics in England (n = 408). Women completed a validated survey assessing breast cancer symptom awareness, barriers to help-seeking and anticipated delay in help-seeking. Women recognised an average of 9.1/11 breast cancer symptoms (SD = 2.1). Nipple rash was the least recognised symptom (51.0%). Women educated to at least degree level had higher awareness than those with lower education (β = 0.14, 95% CI 0.13, 0.99, p = 0.011). Women at lower socioeconomic status (SES) had lower awareness than those at higher SES (β = -0.13, 95% CI −1.09, −0.07, p = 0.027). Women reported several anticipated help-seeking barriers (mean = 4.0/11, SD = 2.8). Waiting to see if a symptom will pass was the most commonly reported barrier to help-seeking (71.5%). Most women (376/408; 92.2%) reported that they would seek medical help within 2 weeks of discovering a breast cancer symptom. Interventions to increase awareness of non-lump breast cancer symptoms and reduce help-seeking barriers are needed, with considerations of appropriate reading levels and modalities for women with lower education and SES.

ABSTRACT: Objectives: The incidence of invasive pulmonary aspergillosis (IPA) among patients without immunocompromised host factors (ICHF) has been described extensively. However, its diagnosis remains challenging. To date, no study has statistically confirmed the efficacy of early IPA diagnosis in patients without ICHF. Methods: We conducted a cross-sectional study on mortality from IPA among patients without ICHF, using the Japanese Diagnosis Procedure Combination National Inpatient Database (April 2014-March 2018). The early diagnosis group was defined according to antifungal therapy initiation within 7 days of hospital admission. The delayed diagnosis group was defined according to antifungal therapy initiation between 8 and 28 days of the hospitalization. Associations were estimated using multivariate logistic regression. Results: A total of 423 patients were registered (early diagnosis group, n = 262, 62%). The early diagnosis group had a lower mortality rate (30%) than the delayed diagnosis group (42%). The early diagnosis group that was treated with voriconazole was associated with lower odds of mortality (odds ratio 0.55, 95% confidence interval 0.31-0.99, P = 0.047). An age of ≥65 years and mechanical ventilation were associated with a higher mortality rate. Conclusion: Early diagnosis along with optimal antifungal treatment are crucial for achieving favorable outcomes among patients with IPA without ICHF.

Brain abscess is a potentially fatal injury that must be treated promptly to avoid complications that require neurosurgery such as intraventricular rupture. Patients with brain abscess may exhibit a multiple variety of nonspecific symptoms, simulating the presence of neurological diseases such as ischemic stroke or intracranial tumor masses. Early radiological diagnosis with adequate subsequent treatment improves the patient's chances of recovery. We report the case of a 48-year-old male patient with brain abscess complicated by an initial rupture into the ventricle. Magnetic resonance imaging with diffusion-weighted images, and apparent diffusion coefficient maps made it possible to diagnose an intraventricular rupture of the abscess with consequent appropriate neurosurgical treatment.

Resumen: El término trastornos del neurodesarrollo agrupa a un conjunto amplio y heterogéneo de condiciones de origen multifactorial, de inicio precoz en la vida, de curso crónico, altamente prevalentes, que determinan déficit de funcionamiento personal, social, académico u ocupacional, con los consiguientes costos personales, familiares y sociales. En este artículo se revisan aspectos de su clasificación, epidemiología, etiología, perfil evolutivo y comorbilidades. Se realiza una revisión crítica y se concluye que si bien las distinciones y clasificaciones actuales son útiles, es relevante considerar sus limitaciones dada la alta comorbilidad y sobreposición fenotípica entre condiciones.Losavancesenelconocimiento, enespecialdesdeenfoquesbasadosenlasneurociencias, traslacionales y transdiagnósticos, proporcionan claras oportunidades de avanzar en desentrañar la heterogeneidad clínica de las actuales categorías, lograr una valoración diagnóstica más precisa y en definitiva, definir orientaciones terapéuticas personalizadas que resulten en la mejor trayectoria de desarrollo. Abstract: The term neurodevelopmental disorders encompasses a broad, heterogeneous and highly prevalent group of conditions of multifactorial origin, early onset in life and chronic course, that determine deficits in personal, social, academic or occupational functioning, with the consequent personal, familial and social burdens. This article reviews aspects of its classification, epidemiology, etiology, evolutive profile and comorbidities. A critical review is made and it is concluded that although current distinctions and classifications are useful, it is relevant to take into consideration their limitations given the high comorbidity and phenotypic overlap that exists amongst conditions. Advances in knowledge, especially those based on neurosciences, translational and transdiagnostic approaches, provide clear opportunities towards advances in unraveling the clinical heterogeneity of the current categories, enabling a more accurate diagnostic assessment and ultimately defining personalized therapeutic options that result in the best developmental trajectory.

Resumen: The autistic spectrum has gained special relevance in today's society, with an increase in its prevalence, greater concern for early diagnosis, therapeutic interventions and the observation of different evolutions. In this article, three evolutionary trajectories are presented, and analyses are made on issues concerning the different psychopathological developments in a mental health framework. The importance of early diagnosis, early individual therapeutic strategies, different evolutionary trajectories and the implications of psychiatric and physical comorbidities are highlighted. Abstract: El espectro autista ha cobrado especial relevancia en la sociedad actual, observándose un aumento de su prevalencia, mayor preocupación por el diagnóstico temprano, las intervenciones terapéuticas y la observación de distintas evoluciones. En el presente artículo se presentan 3 trayectorias evolutivas y se reflexiona en temas concernientes a los distintos desarrollos psicopatológicos en un marco de salud mental; se destaca la importancia del diagnóstico precoz, las estrategias terapéuticas individuales precoces, las distintas trayectorias evolutivas, la implicancia de las comorbilidades psiquiátricas y físicas.

Background: Colorectal cancer (CRC) is the third most prevalent type of cancer for incidence and second for mortality worldwide. Late diagnosis and inconvenient and expensive current diagnostic tools largely contribute to the progress of the disease. The use of chemotherapy in the management of CRC significantly reduces tumor growth, metastasis, and morbidity rates. However, poor solubility, low cellular uptake, nonspecific distribution, multiple drug resistance and unwanted adverse effects are still among the major drawbacks of chemotherapy that limit its clinical significance in the treatment of CRC. Owing to their remarkable advantages over conventional therapies, the use of nanotechnology-based delivery systems especially polymeric nanocarriers (PNCs) has revolutionized many fields including disease diagnosis and drug delivery. Aim of Review: In this review, we shed the light on the current status of using PNCs in the diagnosis and treatment of CRC with a special focus on targeting strategies, surface modifications and safety concerns for different types of PNCs in colonic cancer delivery. Key Scientific Concepts of Review: The review explores the current progress on the use of PNCs in the diagnosis and treatment of CRC with a special focus on the role of PNCs in improvement of cellular uptake, drug targeting and co-delivery of chemotherapeutic agents. Possible toxicity and biocompatibility issues related to the use of PNCs and imitations and future recommendation for the use of those smart carriers in the diagnosis and treatment of CRC are also discussed.

Aiming at clinical translation, we developed an automatic 3D imaging system combining the emerging photoacoustic imaging with conventional Doppler ultrasound for detecting inflammatory arthritis. This system was built with a GE HealthCare (GEHC) Vivid™ E95 ultrasound system and a Universal Robot UR3 robotic arm. In this work, the performance of this system was examined with a longitudinal study utilizing a clinically relevant adjuvant induced arthritis (AIA) murine model. After adjuvant injection, daily imaging of the rat ankle joints was conducted until joint inflammation was obvious based on visual inspection. Processed imaging results and statistical analyses indicated that both the hyperemia (enhanced blood volume) detected by photoacoustic imaging and the enhanced blood flow detected by Doppler ultrasound reflected the progress of joint inflammation. However, photoacoustic imaging, by leveraging the highly sensitive optical contrast, detected inflammation earlier than Doppler ultrasound, and also showed changes that are more statistically significant. This side-by-side comparison between photoacoustic imaging and Doppler ultrasound using the same commercial grade GEHC ultrasound machine demonstrates the advantage and potential value of the emerging photoacoustic imaging for rheumatology clinical care of arthritis.

Objective: This study was aimed at investigating the screening behaviors of the first-degree relatives (FDRs) of Chinese patients with gastric cancer as well as the factors influencing screening behaviors. Methods: In a cross-sectional design, 197 FDRs of patients with gastric cancer were enrolled at the Peking University Cancer Hospital. Four questionnaires were used: a demographic questionnaire, a knowledge questionnaire of risk factors and warning symptoms for gastric cancer, the Gastric Cancer Health Belief Scale, and a questionnaire screening for behavioral motivators and barriers. Logistic regression analysis was performed to determine the factors influencing screening behaviors. Results: Among the 197 FDRs of patients with gastric cancer, 30.96% (61/197) of patients had undergone gastric cancer screening. Among those who participated in gastric cancer screening, the most common screening methods were gastroscopy and the Helicobacter pylori testing, both of which were applied in 63.93% (39/61) of participants, followed by serum tumor marker testing (55.74%, 34/61) and barium meal examination of the upper digestive tract (29.51%, 18/61). The gastric cancer risk factor knowledge score was 9.02 ​± ​3.95, and the gastric cancer warning symptom knowledge score was 4.39 ​± ​1.85. The participants’ knowledge score was moderate, at 13.41 ​± ​5.16. The total health beliefs score was low, at 88.91 ​± ​12.66. Factors independently associated with the screening behaviors of FDRs included educational background, knowledge of gastric cancer risk factors, and health motivation (P ​

Resumen: La disección aórtica aguda (DAA) es una emergencia cardiovascular con una mortalidad operatoria hasta del 30%. Se estima que cerca del 80% de los pacientes mueren antes de llegar a un hospital, y la mortalidad sin intervención quirúrgica alcanza el 60%. La optimización de estos resultados depende de un trabajo mancomunado entre los equipos médicos de los departamentos de urgencias y centros de referencia que cuenten con equipos multidisciplinarios expertos en el manejo de la aorta y la disección aguda.La disección se produce por un desgarro en la íntima de la aorta que permite el flujo de sangre a través de las capas de la aorta, extendiéndose de manera anterógrada y retrograda con el consecuente compromiso de los vasos secundarios. El diagnóstico temprano y certero depende de un alto grado de sospecha clínica en asociación con algoritmos para la utilización de exámenes invasivos y no invasivos que permitan establecer el diagnóstico definitivo. Los dos sistemas de clasificación de la DAA más utilizados son la de DeBakey y la de Stanford. De acuerdo con ellos, se define el manejo médico inicial, el cual busca controlar el dolor, la presión arterial y la frecuencia cardíaca, con el fin de frenar la progresión de la disección, mientras el paciente es trasladado a un centro de referencia o sometida a una intervención quirúrgica definitiva.Este artículo provee una revisión del enfoque diagnóstico y principios terapéuticos de la disección aórtica para médicos, enfermeras y estudiantes. Abstract: Acute aortic dissection is a cardiovascular emergency with an operative mortality of up to 30%. It is estimated that about 80% of patients die before reaching a hospital, and mortality without surgical intervention reaches 60%. The optimization of these results depends on the collaboration between the medical teams of the emergency departments and reference centers that have multidisciplinary teams specializing in the management of the aorta and acute dissection.The dissection is produced by a tear in the intima of the aorta that allows blood to flow in between the layers of the aorta, extending in an anterograde and retrograde fashion, with the consequent involvement of branch vessels. A timely and accurate diagnosis depends on a high degree of clinical suspicion together with algorithms for the use of invasive and non-invasive tests that ultimately help establish a definitive diagnosis. The two classification systems most commonly used, are DeBakey's and Stanford's. Depending on the classification of the dissection, initial medical management is defined, seeking to control pain, blood pressure and heart rate in order to limit progression of the dissection, while the patient is transferred to a reference center or undergoes definitive surgical intervention.This article provides a review of the diagnostic approach and therapeutic principles of aortic dissection for physicians, nurses, and students.

Purpose: To investigate the clinical value of urine interleukin-18 (IL-8), neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule-1 (KIM-1) for the early diagnosis of acute kidney injury (AKI) in patients with ureteroscopic lithotripsy (URL) related urosepsis. Methods: A retrospective study was carried out in 157 patients with urosepsis after URL. The patients were divided into AKI group and non-AKI group according to the Kidigo guideline and urine IL-8, NGAL and KIM-1 levels were detected by enzyme-linked immunosorbent assay at 0, 4, 12, 24 and 48 h after the surgery. Receiver operating characteristic curve (ROC) was used to evaluate the diagnostic value of these three biomarkers for postoperative AKI. Results: The level of urine IL-8, NGAL and KIM-1 in AKI group was significantly higher than that in non-AKI group at 4, 12, 24 and 48 h (p

Zoonotic diseases, transmitted between humans and animals, pose a substantial threat to global public health. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the fight against diseases. This comprehensive review discusses the innovative applications of AI in the management of zoonotic diseases, including disease prediction, early diagnosis, drug development, and future prospects. AI-driven predictive models leverage extensive datasets to predict disease outbreaks and transmission patterns, thereby facilitating proactive public health responses. Early diagnosis benefits from AI-powered diagnostic tools that expedite pathogen identification and containment. Furthermore, AI technologies have accelerated drug discovery by identifying potential drug targets and optimizing candidate drugs. This review addresses these advancements, while also examining the promising future of AI in zoonotic disease control. We emphasize the pivotal role of AI in revolutionizing our approach to managing zoonotic diseases and highlight its potential to safeguard the health of both humans and animals on a global scale.

Resumen: Objetivo: Diseñar y validar una escala de nomofobia en escolares de 9 a 13 años. Diseño: Estudio observacional descriptivo transversal. Emplazamiento: El estudio piloto se realizó en diciembre de 2019 con alumnos de un colegio de Ávila y otro de Madrid. El estudio de campo se realizó los meses de enero-marzo de 2020, recogiendo datos de 592 alumnos de un colegio de Badajoz y 3 de Ávila. Participantes: Alumnos de ambos sexos de 4.o, 5.o y 6.o de Educación Primaria y de 1.o y 2.o de Educación Secundaria Obligatoria, de colegios públicos y concertados. Método: El estudio se hizo en 2 fases. Fase I: elaboración de un cuestionario de 40 ítems con el asesoramiento de un panel de expertos. Este cuestionario se pasó, dentro del estudio piloto, a 312 niños matriculados en Educación Primaria o Educación Secundaria Obligatoria. Tras analizar los resultados, se modificó la escala de 40 ítems para mejorar la comprensión de los escolares y se realizó un estudio de campo con 592 participantes. Fase II: consistió en un análisis factorial exploratorio realizado por el método de componentes principales que proporcionó la distribución de los ítems en 7 componentes. Tras el análisis factorial confirmatorio se definió la escala definitiva de 32 ítems. Resultados: Se demostró que esta escala produce puntuaciones válidas y confiables. Conclusiones: La validación de esta escala será útil para identificar a niños con riesgo de nomofobia, permitiendo a educadores, padres y profesionales de la salud detectarla precozmente para prevenir un uso patológico del teléfono móvil. Abstract: Objective: To design and validate a nomophobia scale in children aged 9–13 years. Design: Cross-sectional descriptive observational study. Site: The pilot study was carried out in December 2019 with students from a school in Ávila and another in Madrid. The field study was carried out during the months of January to March 2020, collecting data from 592 students from a school in Badajoz and 3 from Ávila. Participants: Students of both sexes from 4th, 5th and 6th of Primary Education and from 1st and 2nd of Compulsory Secondary Education, from public and private schools. Method: The study has been divided in 2 phases. Phase I: consisted of the preparation of a 40-item questionnaire with the advice of a panel of experts. This questionnaire was passed, within the pilot study, to 312 children enrolled in Primary Education or Compulsory Secondary Education. After analyzing the results, the 40-item scale was modified to improve the understanding of the schoolchildren and a field study was carried out with 592 participants. Phase II: consisted of an exploratory factorial analysis carried out using the principal components method, which provided the distribution of the items in 7 components. After the confirmatory factorial analysis, the final 32-item scale was defined. Results: This scale was shown to produce valid and reliable scores. Conclusions: The validation of this scale will be useful to identify children at risk of nomophobia, allowing educators, parents and health professionals to detect this phenomenon early in order to prevent pathological smartphone use.