Visibility of esophageal squamous cell carcinoma under iodine staining on texture and color enhancement imaging.

Objective: Iodine staining on white light imaging (WLI) is the gold standard for detecting and demarcating esophageal squamous cell carcinoma (ESCC). We examined the effects of texture and color enhancement imaging (TXI) on improving the endoscopic visibility of ESCC under iodine staining. Methods: Twenty ESCC lesions that underwent endoscopic submucosal dissection were retrospectively included. The color difference between ESCC and the surrounding mucosa (ΔEe) on WLI, TXI, and narrow-band imaging was assessed, and ΔEe under 1% iodine staining on WLI and TXI. Furthermore, the visibility grade determined by endoscopists was evaluated on each imaging. Result: The median ΔEe was greater on TXI than on WLI (14.53 vs. 10.71, respectively; p < 0.005). Moreover, the median ΔEe on TXI under iodine staining was greater than the median ΔEe on TXI and narrow-band imaging (39.20 vs. 14.53 vs. 16.42, respectively; p < 0.005 for both). A positive correlation in ΔEe under iodine staining was found between TXI and WLI (correlation coefficient = 0.61, p < 0.01). Moreover, ΔEe under iodine staining on TXI in each lesion was greater than the corresponding ΔEe on WLI. The visibility grade assessed by endoscopists on TXI was also significantly greater than that on WLI under iodine staining (p < 0.01). Conclusions: The visibility of ESCC after iodine staining was greater on TXI than on WLI.

Utilizing machine learning to facilitate the early diagnosis of posterior circulation stroke.

BACKGROUND: Posterior Circulation Syndrome (PCS) presents a diagnostic challenge characterized by its variable and nonspecific symptoms. Timely and accurate diagnosis is crucial for improving patient outcomes. This study aims to enhance the early diagnosis of PCS by employing clinical and demographic data and machine learning. This approach targets a significant research gap in the field of stroke diagnosis and management. METHODS: We collected and analyzed data from a large national Stroke Registry spanning from January 2014 to July 2022. The dataset included 15,859 adult patients admitted with a primary diagnosis of stroke. Five machine learning models were trained: XGBoost, Random Forest, Support Vector Machine, Classification and Regression Trees, and Logistic Regression. Multiple performance metrics, such as accuracy, precision, recall, F1-score, AUC, Matthew's correlation coefficient, log loss, and Brier score, were utilized to evaluate model performance. RESULTS: The XGBoost model emerged as the top performer with an AUC of 0.81, accuracy of 0.79, precision of 0.5, recall of 0.62, and F1-score of 0.55. SHAP (SHapley Additive exPlanations) analysis identified key variables associated with PCS, including Body Mass Index, Random Blood Sugar, ataxia, dysarthria, and diastolic blood pressure and body temperature. These variables played a significant role in facilitating the early diagnosis of PCS, emphasizing their diagnostic value. CONCLUSION: This study pioneers the use of clinical data and machine learning models to facilitate the early diagnosis of PCS, filling a crucial gap in stroke research. Using simple clinical metrics such as BMI, RBS, ataxia, dysarthria, DBP, and body temperature will help clinicians diagnose PCS early. Despite limitations, such as data biases and regional specificity, our research contributes to advancing PCS understanding, potentially enhancing clinical decision-making and patient outcomes early in the patient's clinical journey. Further investigations are warranted to elucidate the underlying physiological mechanisms and validate these findings in broader populations and healthcare settings.

Access to early diagnosis for attention-deficit/hyperactivity disorder among children and adolescents in Mexico City at specialized mental health services.

BACKGROUND: In Mexico, this pioneering research was undertaken to assess the accessibility of timely diagnosis of Dyads [Children and adolescents with Attention Deficit Hyperactivity Disorder (ADHD) and their primary caregivers] at specialized mental health services. The study was conducted in two phases. The first phase involved designing an "Access Pathway" aimed to identify barriers and facilitators for ADHD diagnosis; several barriers, with only the teacher being identified as a facilitator. In the second phase, the study aimed to determine the time taken for dyads, to obtain a timely diagnosis at each stage of the Access Pathway. As well as identify any disparities based on gender and socioeconomic factors that might affect the age at which children can access a timely diagnosis. METHOD: In a retrospective cohort study, 177 dyads participated. To collect data, the Acceda Survey was used, based on the robust Conceptual Model Levesque, 2013. The survey consisted of 48 questions that were both dichotomous and polytomous allowing the creation of an Access Pathway that included five stages: the age of perception, the age of search, the age of first contact with a mental health professional, the age of arrival at the host hospital, and the age of diagnosis. The data was meticulously analyzed using a comprehensive descriptive approach and a nonparametric multivariate approach by sex, followed by post-hoc Mann-Whitney's U tests. Demographic factors were evaluated using univariable and multivariable Cox regression analyses. RESULTS: 71% of dyads experienced a late, significantly late, or highly late diagnosis of ADHD. Girls were detected one year later than boys. Both boys and girls took a year to seek specialized mental health care and an additional year to receive a formal specialized diagnosis. Children with more siblings had longer delays in diagnosis, while caregivers with formal employment were found to help obtain timely diagnoses. CONCLUSIONS: Our findings suggest starting the Access Pathway where signs and symptoms of ADHD are detected, particularly at school, to prevent children from suffering consequences. Mental health school-based service models have been successfully tested in other latitudes, making them a viable option to shorten the time to obtain a timely diagnosis.

Differentiating Prodromal Dementia with Lewy Bodies from Prodromal Alzheimer's Disease: A Pragmatic Review for Clinicians.

This pragmatic review synthesises the current understanding of prodromal dementia with Lewy bodies (pDLB) and prodromal Alzheimer's disease (pAD), including clinical presentations, neuropsychological profiles, neuropsychiatric symptoms, biomarkers, and indications for disease management. The core clinical features of dementia with Lewy bodies (DLB)-parkinsonism, complex visual hallucinations, cognitive fluctuations, and REM sleep behaviour disorder are common prodromal symptoms. Supportive clinical features of pDLB include severe neuroleptic sensitivity, as well as autonomic and neuropsychiatric symptoms. The neuropsychological profile in mild cognitive impairment attributable to Lewy body pathology (MCI-LB) tends to include impairment in visuospatial skills and executive functioning, distinguishing it from MCI due to AD, which typically presents with impairment in memory. pDLB may present with cognitive impairment, psychiatric symptoms, and/or recurrent episodes of delirium, indicating that it is not necessarily synonymous with MCI-LB. Imaging, fluid and other biomarkers may play a crucial role in differentiating pDLB from pAD. The current MCI-LB criteria recognise low dopamine transporter uptake using positron emission tomography or single photon emission computed tomography (SPECT), loss of REM atonia on polysomnography, and sympathetic cardiac denervation using meta-iodobenzylguanidine SPECT as indicative biomarkers with slowing of dominant frequency on EEG among others as supportive biomarkers. This review also highlights the emergence of fluid and skin-based biomarkers. There is little research evidence for the treatment of pDLB, but pharmacological and non-pharmacological treatments for DLB may be discussed with patients. Non-pharmacological interventions such as diet, exercise, and cognitive stimulation may provide benefit, while evaluation and management of contributing factors like medications and sleep disturbances are vital. There is a need to expand research across diverse patient populations to address existing disparities in clinical trial participation. In conclusion, an early and accurate diagnosis of pDLB or pAD presents an opportunity for tailored interventions, improved healthcare outcomes, and enhanced quality of life for patients and care partners.

Diagnosing Parkinson's disease and monitoring its progression: Biomarkers from combined GC-TOF MS and LC-MS/MS untargeted metabolomics.

Parkinson's disease (PD) is a prevalent neurodegenerative disorder with a poorly understood etiology. An accurate diagnosis of idiopathic PD remains challenging as misdiagnosis is common in routine clinical practice. Moreover, current therapeutics focus on symptomatic management rather than curing or slowing down disease progression. Therefore, identification of potential PD biomarkers and providing a better understanding of the underlying disease pathophysiology are urgent. Herein, hydrophilic interaction liquid chromatography-mass spectrometry (LC-MS/MS) and gas chromatography-mass spectrometry (GC-TOF MS) based metabolomics approaches were used to profile the serum metabolome of 50 patients with different stages of idiopathic PD (early, mid and advanced) and 45 age-matched controls. Levels of 57 metabolites including cysteine-S-sulfate and N-acetyl tryptophan were significantly higher in patients with PD compared to controls, with lower amounts of additional 51 metabolites including vanillic acid, and N-acetylaspartic acid. Xanthines, including caffeine and its downstream metabolites, were lowered in patients with PD relative to controls indicating a potential role caffeine and its metabolites against neuronal damage. Seven metabolites, namely cysteine-S-sulfate, 1-methylxanthine, vanillic acid, N-acetylaspartic acid, 3-N-acetyl tryptophan, 5-methoxytryptophol, and 13-HODE yielded a ROC curve with a high classification accuracy (AUC 0.977). Comparison between different PD stages showed that cysteine-S-sulfate levels were significantly increasing with the advancement of PD stages while LPI 20:4 was significantly decreasing with disease progression. Our findings provide new biomarker candidates to assist in the diagnosis of PD and monitor its progression. Unusual metabolites like cysteine-S-sulfate might point to therapeutic targets that could enhance the development of novel PD treatments, such as NMDA antagonists.

Establishment of a prognostic risk model for osteosarcoma and mechanistic investigation.

Objective: To investigate the immune mechanism of osteosarcoma (OS)-specific markers to mitigate bone destruction in the aggressive OS, prone to recurrence and metastasis. Methods: Gene expression patterns from the Gene Expression Omnibus (GEO) database (GSE126209) were analyzed using weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, least absolute shrinkage and selection operator (LASSO) modeling, and survival analysis to identify charged multivesicular body protein 4C (CHMP4C). Subsequently, its role in regulating the immune system and immune cell infiltration was explored. CHMP4C expression and signaling molecules in OS were assessed in osteosarcoma cell lines (MG63, U2OS, HOS) and hFOB1.19 cells using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and immunofluorescence staining. The impact of CHMP4C upregulation and interference on OS-related signaling molecules in MG63 cells was studied. Functional validation of CHMP4C in MG63 OS cells was confirmed through cell counting Kit-8 (CCK-8), transwell, and colony formation assays. In vivo experiments were conducted using Specific Pathogen Free (SPF)-grade male BALB/C nude mice for OS xenograft studies. Results: Based on the gene expression profiles analysis of six osteosarcoma samples and six normal tissue samples, we identified 1,511 upregulated DEGs and 5,678 downregulated DEGs in normal tissue samples. A significant positive correlation between the "yellow-green" module and OS was found through WGCNA analysis. Expression levels of CHMP4C, phosphorylated Glycogen Synthase Kinase 3ß (p-GSK3ß), and ß-catenin were notably higher in U2OS, HOS, and MG63 OS cells than in hFOB1.19 human osteoblasts. Overexpressing CHMP4C in MG63 OS cells upregulated CHMP4C, p-GSK3ß, and ß-catenin while downregulating GSK3ß, leading to increased proliferation and migration of MG63 cells. Conversely, interrupting CHMP4C had the opposite effect. High expression of CHMP4C significantly accelerated the growth of OS in nude mice, resulting in substantial upregulation of CHMP4C, p-GSK3ß, and ß-catenin expression and suppression of Glycogen Synthase Kinase 3ß (GSK3ß) expression in OS tissues. Conclusion: CHMP4C may serve as a specific immunomodulatory gene for OS. Its activation of the Wnt/ß-catenin signaling pathway, mainly by increasing the phosphorylation echelon of GSK3ß, promotes the invasion and spread of OS.

A three miRNAs panel in paraffin tissue serves as tool for predicting prognosis of renal cell carcinoma.

Background: Renal cell carcinoma (RCC) stands as the most prevalent form of urogenital cancer. However, there is currently no universally accepted method for predicting the prognosis of RCC. MiRNA holds great potential as a prognostic biomarker for RCC. Methods: A total of 100 cases with complete paraffin specimens and over 5-year follow-up data meeting the requirements were collected. Utilizing the clinical information and follow-up data of the specimens, an information model was developed. The expression levels of eight microRNAs were identified using RT-qPCR. Finally, determine and analyze the clinical application value of these microRNAs as prognostic markers for RCC. Results: Significant differences were observed in the expression of two types of miRNAs (miR-378a-5p, miR-23a-5p) in RCC tissue, and three types of miRNAs (miR-378a-5p, miR-642a-5p, miR-23a-5p) were found to be linked to the prognosis of RCC. Establish biomarker combinations of miR-378a-5p, miR-642a-5p, and miR-23a-5p to evaluate RCC prognosis. Conclusion: The combination of three microRNA groups (miR-378a-5p, miR-642a-5p, and miR-23a-5p) identified in paraffin section specimens of RCC in this study holds significant potential as biomarkers for assessing RCC prognosis.

Towards early diagnosis of Alzheimer's disease: advances in immune-related blood biomarkers and computational approaches.

Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. Here, we give a background on recent advances in research on brain-immune system cross-talk in Alzheimer's disease and review machine learning approaches, which can combine multiple biomarkers with further information (e.g. age, sex, APOE genotype) into predictive models supporting an earlier diagnosis. In addition, mechanistic modeling approaches, such as agent-based modeling open the possibility to model and analyze cell dynamics over time. This review aims to provide an overview of the current state of immune-system related blood-based biomarkers and their potential for the early diagnosis of Alzheimer's disease.

Applications of artificial intelligence-powered prenatal diagnosis for congenital heart disease.

Artificial intelligence (AI) has made significant progress in the medical field in the last decade. The AI-powered analysis methods of medical images and clinical records can now match the abilities of clinical physicians. Due to the challenges posed by the unique group of fetuses and the dynamic organ of the heart, research into the application of AI in the prenatal diagnosis of congenital heart disease (CHD) is particularly active. In this review, we discuss the clinical questions and research methods involved in using AI to address prenatal diagnosis of CHD, including imaging, genetic diagnosis, and risk prediction. Representative examples are provided for each method discussed. Finally, we discuss the current limitations of AI in prenatal diagnosis of CHD, namely Volatility, Insufficiency and Independence (VII), and propose possible solutions.

Giant Cell Arteritis Masquerading as Tooth Pain: A Case Report.

Giant cell arteritis (GCA) is a form of vasculitis characterized by symptoms that often lead a patient to consult a general dentist. Its rarity in the dental setting and serious life-altering effects make it a formidable diagnosis. We discuss a case of a 60-year-old female with GCA presenting with primary symptoms of excruciating tooth and jaw pain on the left side. We also report secondary symptoms of headache and partial vision loss and engage in a review of the relevant literature. Jaw pain, unexplained toothache, or tissue necrosis in patients aged over 50 years can be misdiagnosed as joint arthritis or temporomandibular disease (TMD), which could lead to severe consequences. Accurately diagnosing this ophthalmic emergency is critical for implementing therapy promptly and preventing ischemic complications. Dentists should maintain a high index of suspicion about its signs and symptoms, which will aid in making an early diagnosis and prompt referral.

Clinical features and risk factors for primary Sjögren's syndrome combined with interstitial lung disease: a retrospective study.

Objective: To analyze the clinical characteristics of primary Sjögren's syndrome (pSS) combined with interstitial lung disease (ILD), so as to provide a theoretical basis for the early diagnosis, treatment and prevention of PSS-ILD. Methods: From October 2017 to January 2022, patients with pSS who were admitted to the Department of Rheumatology at Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine were included in this retrospective study. Patients were divided into the pSS-ILD (102 cases) and pSS-non-ILD groups (154 cases) based on the presence or absence of ILD on high-resolution computed tomography (HRCT). Demographics information, clinical symptoms, laboratory indicators and HRCT features were compared, and the logistic regression analysis was utilized to identify the risk factors. Results: A total of 256 patients were included. Patients with pSS-ILD were more often female, and their age and disease duration were significantly higher than those in the pSS-non-ILD group (p < 0.05). The HRCT imaging classification included ground glass-like shadow (78.4%) and patchy solid shadow (17.6%), and Non-specific interstitial pneumonitis (NSIP) (72.5%) was the predominant typology. Regarding the laboratory indexes, the positive rates of erythrocyte sedimentation rate, C-reactive protein, white blood cell count, neutrophil/lymphocyte ratio, triglycerides, total cholesterol, and anti-SS-A52 antibodies were significantly higher in the pSS-ILD patients than in the pSS-non-ILD group, while the positive rates of anti-synaptic antibodies were lower than in the pSS-non-ILD group, and the differences between two groups were statistically significant (p < 0.05). Logistic regression showed that age >60 years, longer duration of disease, higher triglycerides, and cholesterol were risk factors for pSS-ILD patients. Conclusion: The clinical features of pSS-ILD patients were xerophthalmia, cough and shortness of breath, and HRCT can help to diagnose the disease at an early stage. Age over 60 years, chronic course of disease, and elevated lipid levels are risk factors for ILD in pSS patients, and the relationship between autoimmune antibody levels and the occurrence of ILD needs to be further confirmed in follow-up studies with large sample sizes. These findings have the potential to provide useful information for early diagnosis, treatment, and prevention of the development of pSS-ILD.

Diagnostic Performance of Ultrasonography for Identification of Small Bowel Obstruction; a Systematic Review and Meta-analysis.

Introduction: Small bowel obstruction (SBO) is known as a common cause of acute abdominal complaints in the emergency department (ED). The modality of choice for the diagnosis of SBO has not yet been established. This systematic review and meta-analysis aimed to investigate the accuracy of ultrasonography for the diagnosis of SBO. Methods: Systematic search was performed on five electronic databases including Medline, Scopus, Web of Sciences, Embase, and Cochrane Library, and the retrieval period was from the inception of each database to November 2023. The quality of the included studies were investigated using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). The pooled values of diagnostic characteristics for ultrasonography were estimated using meta-Disc and Stata statistical software. Results: Twenty-one studies with a total of 1977 patients were included in the meta-analysis. The pooled estimate for sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the summary ROC curve of ultrasonography for diagnosing SBO were 0.93 (95% CI: 0.91-0.95), 0.8 (95% CI: 0.77-0.83), 5.69 (95% CI: 3.64-8.89), 0.1 (95% CI: 0.07-0.16), 83.51 (95% CI: 18.12-182.91) and 0.96, respectively. Conclusion: The findings of this meta-analysis showed that the utilization of ultrasonography holds promise as a diagnostic imaging for SBO with high accuracy. However, additional worldwide studies are essential to get more evidence on the value of ultrasonography for the diagnosis of SBO.

miRNA-143 as a potential biomarker in the detection of bladder cancer: a meta-analysis.

Aim: This study aimed to systematically evaluate the value of miRNA-143 in the early detection of bladder cancer (BCa). Methods: CNKI, WanFang, PubMed and Wiley Online Library databases were explored according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol. A random-effects model was used to obtain pooled sensitivity, specificity and other related indicates. Results: Six studies were included for analysis. The overall pooled sensitivity and specificity were 0.80 (95% CI: 0.74-0.85) and 0.85 (95% CI: 0.78-0.91), and the area under the curve was 0.88 (95% CI: 0.85-0.91). Coupled with miR-100, it showed better diagnostic power (area under the curve: 0.95). Conclusion: miRNA-143 may serve as a promising noninvasive tool for the early detection of BCa.

Bladder cancer (BCa) is a common and deadly malignant tumor worldwide; however, noninvasive diagnosis can significantly improve the prognosis of patients. Recently, miRNAs have emerged as potential diagnostic biomarkers for BCa. Among them, miRNA-143 has shown promising results in several studies. This meta-analysis aimed to evaluate the overall diagnostic accuracy of miRNA-143 for BCa through a systematic review and meta-analysis of six published articles. Excitingly, the results of this meta-analysis suggest that miRNA-143 has potential diagnostic value in BCa. Particularly, miRNA-143 combined with miRNA-100 maintained better competence. Besides, miRNA-143 in plasma exhibited better diagnostic strength than that in urine. The authors believe that their study provides valuable insights into the use of miRNA-143 as a diagnostic biomarker for BCa.

Muscle fibre morphometric analysis (MusMA) correlates with muscle function and cardiovascular risk prognosis.

Morphometry of striated muscle fibres is critical for monitoring muscle health and function. Here, we evaluated functional parameters of skeletal and cardiac striated muscle in two experimental models using the Morphometric Analysis of Muscle Fibre tool (MusMA). The collagen-induced arthritis model was used to evaluate the function of skeletal striated muscle and the non-alcoholic fatty liver disease model was used for cardiac striated muscle analysis. After euthanasia, we used haeamatoxylin and eosin stained sections of skeletal and cardiac muscle to perform muscle fibre segmentation and morphometric analysis. Morphometric analysis classified muscle fibres into six subpopulations: normal, regular hypertrophic, irregular hypertrophic, irregular, irregular atrophic and regular atrophic. The percentage of atrophic fibres was associated with lower walking speed (p = 0.009) and lower body weight (p = 0.026), respectively. Fibres categorized as normal were associated with maximum grip strength (p < 0.001) and higher march speed (p < 0.001). In the evaluation of cardiac striated muscle fibres, the percentage of normal cardiomyocytes negatively correlated with cardiovascular risk markers such as the presence of abdominal adipose tissue (p = .003), miR-33a expression (p = .001) and the expression of miR-126 (p = .042) Furthermore, the percentage of atrophic cardiomyocytes correlated significantly with the Castelli risk index II (p = .014). MusMA is a simple and objective tool that allows the screening of striated muscle fibre morphometry, which can complement the diagnosis of muscle diseases while providing functional and prognostic information in basic and clinical research.

Dengue: A review of laboratory diagnostics in the vaccine age.

Background. Dengue is an important arboviral infection of considerable public health significance. It occurs in a wide global belt within a variety of tropical regions. The timely laboratory diagnosis of Dengue infection is critical to inform both clinical management and an appropriate public health response. Vaccination against Dengue virus is being introduced in some areas.Discussion. Appropriate diagnostic strategies will vary between laboratories depending on the available resources and skills. Diagnostic methods available include viral culture, the serological detection of Dengue-specific antibodies in using enzyme immunoassays (EIAs), microsphere immunoassays, haemagglutination inhibition or in lateral flow point of care tests. The results of antibody tests may be influenced by prior vaccination and exposure to other flaviviruses. The detection of non-structural protein 1 in serum (NS1) has improved the early diagnosis of Dengue and is available in point-of-care assays in addition to EIAs. Direct detection of viral RNA from blood by PCR is more sensitive than NS1 antigen detection but requires molecular skills and resources. An increasing variety of isothermal nucleic acid detection methods are in development. Timing of specimen collection and choice of test is critical to optimize diagnostic accuracy. Metagenomics and the direct detection by sequencing of viral RNA from blood offers the ability to rapidly type isolates for epidemiologic purposes.Conclusion. The impact of vaccination on immune response must be recognized as it will impact test interpretation and diagnostic algorithms.

[Undiagnosed arterial hypertension in a Latin American country, 2019 to 2021]. / Hipertensión arterial no diagnosticada en un país de Latinoamérica, 2019 a 2021.

Objective: To determine the factors associated with undiagnosed hypertension. Method: A quantitative, observational, retrospective, cross-sectional and analytical study was carried out in people aged 15 years and over included in the Demographic and Family Health Survey from 2019 to 2021 in Peru. A statistical analysis was carried out using the corrected F test, and crude and adjusted prevalence ratio (aPR), with a 95% confidence interval (95%CI) for inferential analysis, through Poisson regression with robust variance. Likewise, the CSPLAN analysis was carried out for complex samples according to the sample design and taking into account the weighting factor. Results: In the multivariate analysis, a significant association was found between the factors male sex (aPR: 1.22; 95%CI: 1.19-1.26), age from 30 to 49 years (aPR: 0.94; 95%CI: 0.92-0.96), native ethnicity (aPR: 1.07; 95%CI: 1.04-1.10), having health insurance (aPR: 0.91; 95%CI: 0.89-0.93), suffering from some permanent limitation (aPR: 0.83; 95%CI: 0.76-0.91) and diabetes mellitus (aPR: 0.59; 95%CI: 0.55-0.64). No significant association was found with educational level, language, Afro-Peruvian ethnicity, or alcohol or tobacco consumption (p > 0.05). Conclusions: The prevalence of undiagnosed arterial hypertension is high, 69.5%. The associated factors are male sex, native ethnicity, age between 30 and 49 years, having health insurance, suffering from some permanent limitation and having diabetes mellitus.

Objetivo: Determinar los factores asociados a hipertensión arterial no diagnosticada. Método: Estudio de tipo cuantitativo, observacional, retrospectivo, transversal y analítico, en personas de 15 y más años de edad contenidas en la Encuesta Demográfica y Salud Familiar de 2019 a 2021 en Perú. Se realizó un análisis estadístico haciendo uso de la prueba F corregida y la razón de prevalencia cruda y ajustada (RPa), con un intervalo de confianza del 95% (IC95%) para el análisis inferencial, a través de regresión de Poisson con varianza robusta. Asimismo, se realizó el análisis CSPLAN para muestras complejas de acuerdo con el diseño de la muestra y teniendo en cuenta el factor de ponderación. Resultados: En el análisis multivariado se halló una asociación significativa de los factores sexo masculino (RPa: 1.22; IC95%: 1.19-1.26), edad de 30 a 49 años (RPa: 0.94; IC95%: 0.92-0.96), etnia nativa (RPa: 1.07; IC95%: 1.04-1.10), tenencia de un seguro de salud (RPa: 0.91; IC95%: 0.89-0.93), sufrir alguna limitación permanente (RPa: 0.83; IC95%: 0.76-0.91) y diabetes mellitus (RPa: 0.59; IC95%: 0.55-0.64). No se encontró asociación significativa con el nivel de instrucción, el idioma, la etnia afroperuana ni el consumo de alcohol o tabaco (p > 0.05). Conclusiones: La prevalencia de hipertensión arterial no diagnosticada es alta, del 69.5%. Los factores asociados son el sexo masculino, la etnia nativa, la edad entre 30 y 49 años, la tenencia de un seguro de salud, sufrir alguna limitación permanente y tener diabetes mellitus.

Factors associated with having previously received a diagnosis of fibromyalgia, chronic fatigue syndrome and irritable bowel syndrome: A cross sectional DanFunD study.

OBJECTIVES: Fibromyalgia, chronic fatigue syndrome, and irritable bowel syndrome are highly prevalent conditions and part of the functional somatic syndromes (FSS) diagnosis, that are classified under the unifying umbrella term functional somatic disorder (FSD). Multiple factors are associated with FSD symptom development; However, few studies have explored these associations in relation to the diagnosis status. This study aims to examine associations with a previously received FSS diagnosis from a physician in participants fulfilling the FSD diagnostic criteria in a population-based sample. METHODS: This research employs a comprehensive observational approach using a cross sectional design with data from the DanFunD part two cohort. Information about received FSS diagnoses was obtained from self-reported questionnaires. Participants fulfilling the FSD diagnostic criteria were identified with both self-reported questionnaires and diagnostic interviews. Validated questionnaires were used to assess the examined factors. RESULTS: 1704 cases fulfilled the diagnostic criteria for an FSD according to questionnaires or interviews in the DanFunD study. In participants fulfilling the diagnostic criteria, having previously received an FSS diagnosis by a physician was strongly associated with female sex, negative illness perceptions and poor health-related quality of life for questionnaire and interview-based diagnoses. Less consistent associations were observed for lower socioeconomic status, anxiety, and adverse life events. CONCLUSION: Previously received FSS diagnoses showed associations with multiple factors with a particular strong association with female sex and poor health related quality of life.

Emergence of Quantum Dots as Innovative Tools for Early Diagnosis and Advanced Treatment of Breast Cancer.

Quantum dots (QDs) semiconducting nanomaterials, have garnered attention due to their distinctive properties, including small size, high luminescence, and biocompatibility. In the context of triple-negative breast cancer (TNBC), notorious for its resistance to conventional treatments, QDs exhibit promising potential for enhancing diagnostic imaging and providing targeted therapies. This review underscores recent advancements in the utilization of QDs in imaging techniques, such as fluorescence tomography and magnetic resonance imaging, aiming at the early and precise detection of tumors. Emphasis is placed on the significance of QD design, synthesis and functionalization processes as well as their use in innovative strategies for targeted drug delivery, capitalizing on their ability to selectively deliver therapeutic agents to cancer cells. As the research in this field advances rapidly, this review covers a classification of QDs according to their composition, the characterization techniques than can be used to determine their properties and, subsequently, emphasizes recent findings in the field of TNBC-targeting, highlighting the imperative need to address challenges, like potential toxicity or methodologies standardization. Collectively, the findings explored thus far suggest that QDs could pave the way for early diagnosis and effective therapy of TNBC, representing a significant stride toward precise and personalized strategies in treating TNBC.

Assessing deep learning reconstruction for faster prostate MRI: visual vs. diagnostic performance metrics.

OBJECTIVE: Deep learning (DL) MRI reconstruction enables fast scan acquisition with good visual quality, but the diagnostic impact is often not assessed because of large reader study requirements. This study used existing diagnostic DL to assess the diagnostic quality of reconstructed images. MATERIALS AND METHODS: A retrospective multisite study of 1535 patients assessed biparametric prostate MRI between 2016 and 2020. Likely clinically significant prostate cancer (csPCa) lesions (PI-RADS ≥ 4) were delineated by expert radiologists. T2-weighted scans were retrospectively undersampled, simulating accelerated protocols. DL reconstruction (DLRecon) and diagnostic DL detection (DLDetect) were developed. The effect on the partial area under (pAUC), the Free-Response Operating Characteristic (FROC) curve, and the structural similarity (SSIM) were compared as metrics for diagnostic and visual quality, respectively. DLDetect was validated with a reader concordance analysis. Statistical analysis included Wilcoxon, permutation, and Cohen's kappa tests for visual quality, diagnostic performance, and reader concordance. RESULTS: DLRecon improved visual quality at 4- and 8-fold (R4, R8) subsampling rates, with SSIM (range: -1 to 1) improved to 0.78 ± 0.02 (p < 0.001) and 0.67 ± 0.03 (p < 0.001) from 0.68 ± 0.03 and 0.51 ± 0.03, respectively. However, diagnostic performance at R4 showed a pAUC FROC of 1.33 (CI 1.28-1.39) for DL and 1.29 (CI 1.23-1.35) for naive reconstructions, both significantly lower than fully sampled pAUC of 1.58 (DL: p = 0.024, naïve: p = 0.02). Similar trends were noted for R8. CONCLUSION: DL reconstruction produces visually appealing images but may reduce diagnostic accuracy. Incorporating diagnostic AI into the assessment framework offers a clinically relevant metric essential for adopting reconstruction models into clinical practice. CLINICAL RELEVANCE STATEMENT: In clinical settings, caution is warranted when using DL reconstruction for MRI scans. While it recovered visual quality, it failed to match the prostate cancer detection rates observed in scans not subjected to acceleration and DL reconstruction.

Mistakes in utilising histopathology for the management of liver disease.

INTRODUCTION: Liver biopsy has become selective due to its invasiveness, potential adverse effects, patient acceptance and cost. Furthermore, the emergence of noninvasive tests (NITs) has challenged the necessity of liver biopsies in specific clinical situations. However, liver biopsy continues to play a crucial role in disease diagnosis, prognosis, and evaluating treatment compliance and response in selected patients. AREAS COVERED: In this narrative review, we discuss the errors and the shortcomings that can occur at various stages, from the initial patient selection for a liver biopsy to the final reporting phase, and strategies to address them. Clinicians and pathologists must take all necessary precautions to mitigate potential shortcomings that could compromise the value of liver biopsies. EXPERT OPINION: The increasing sophistication of NITs offers a safer, more convenient, and potentially more cost-effective approach to diagnosing chronic liver disease, especially for assessing the degree of liver fibrosis. As NITs continue to evolve, liver biopsy will likely transition to a more targeted role, ensuring optimal patient care in the ever-changing field of hepatology. However, liver biopsy will continue to have a pivotal role in assessing acute liver disease where the diagnostic yield of the liver biopsy still outweighs that of NITs.