Significance of early diagnosis and surgical management in treating Mycobacterium immunogenum-related pyogenic extensor tenosynovitis: a case report.

BACKGROUND: Non-tuberculous mycobacteria (NTM) are environmental organisms that are increasingly contributing to human infections. Mycobacterium immunogenum, a variant of NTM discovered in 2001, is a rapidly growing mycobacterium that exhibits multidrug resistance. Reports of infections caused by this organism, particularly tenosynovitis in the musculoskeletal system, are limited. CASE PRESENTATION: A 71-year-old female with vesicular pemphigus, undergoing immunosuppressive therapy, presented with a progressively enlarging tumour on the dorsum of her right hand, along with erythematous papules that extended across her right forearm. The specimens of skin tissues and blood cultures revealed the presence of M. immunogenum. Magnetic resonance imaging evaluation led to the diagnosis of pyogenic extensor tenosynovitis. A multidrug regimen, comprising amikacin and clarithromycin, was initiated, followed by synovectomy. The patient underwent a course of 180 days of antimicrobial therapy and demonstrated no signs of disease recurrence one year after treatment completion. CONCLUSION: Early diagnosis and surgical intervention are crucial to prevent the adverse prognostic implications of pyogenic extensor tenosynovitis caused by M. immunogenum. Effective management requires precise microbial identification and susceptibility testing, necessitating collaborative engagement with microbiological laboratories.

Monocytic myeloid-derived suppressor cells as an immune indicator of early diagnosis and prognosis in patients with sepsis.

BACKGROUND: Immunosuppression is a leading cause of septic death. Therefore, it is necessary to search for biomarkers that can evaluate the immune status of patients with sepsis. We assessed the diagnostic and prognostic value of low-density neutrophils (LDNs) and myeloid-derived suppressor cells (MDSCs) subsets in the peripheral blood mononuclear cells (PBMCs) of patients with sepsis. METHODS: LDNs and MDSC subsets were compared among 52 inpatients with sepsis, 33 inpatients with infection, and 32 healthy controls to investigate their potential as immune indicators of sepsis. The percentages of LDNs, monocytic MDSCs (M-MDSCs), and polymorphonuclear MDSCs (PMN-MDSCs) in PBMCs were analyzed. Sequential organ failure assessment (SOFA) scores, C-reactive protein (CRP), and procalcitonin (PCT) levels were measured concurrently. RESULTS: The percentages of LDNs and MDSC subsets were significantly increased in infection and sepsis as compared to control. MDSCs performed similarly to CRP and PCT in diagnosing infection or sepsis. LDNs and MDSC subsets positively correlated with PCT and CRP levels and showed an upward trend with the number of dysfunctional organs and SOFA score. Non-survivors had elevated M-MDSCs compared with that of patients who survived sepsis within 28 days after enrollment. CONCLUSIONS: MDSCs show potential as a diagnostic biomarker comparable to CRP and PCT, in infection and sepsis, even in distinguishing sepsis from infection. M-MDSCs show potential as a prognostic biomarker of sepsis and may be useful to predict 28-day hospital mortality in patients with sepsis.

Proposing the potential of utilizing the CAT score for early detection of COPD in asymptomatic patients, shifting towards a patient-centered approach: A review.

Chronic obstructive pulmonary disease (COPD) constitutes a significant public health challenge, with delayed diagnosis and underdiagnosis being pervasive issues. The United States Preventive Service Task Force recommends restricting COPD screening to symptomatic smokers, a focus that has exhibited limitations, leading to delayed diagnoses, and imposing a substantial burden on patients, their families, and the healthcare system. This paper explores an alternative approach, highlighting the potential utility of the COPD assessment test (CAT) score as a prescreening tool. A CAT score of 10 or higher could serve as an appropriate threshold for further diagnostic procedures, given its robust correlation with pulmonary function test parameters and is valuable capacity to quantify patients' symptoms. The utilization of CAT as a prescreening tool in primary care signifies a transition towards a more patient-centered and comprehensive approach to COPD diagnosis and care.

Early detection of emerging viral variants through analysis of community structure of coordinated substitution networks.

The emergence of viral variants with altered phenotypes is a public health challenge underscoring the need for advanced evolutionary forecasting methods. Given extensive epistatic interactions within viral genomes and known viral evolutionary history, efficient genomic surveillance necessitates early detection of emerging viral haplotypes rather than commonly targeted single mutations. Haplotype inference, however, is a significantly more challenging problem precluding the use of traditional approaches. Here, using SARS-CoV-2 evolutionary dynamics as a case study, we show that emerging haplotypes with altered transmissibility can be linked to dense communities in coordinated substitution networks, which become discernible significantly earlier than the haplotypes become prevalent. From these insights, we develop a computational framework for inference of viral variants and validate it by successful early detection of known SARS-CoV-2 strains. Our methodology offers greater scalability than phylogenetic lineage tracing and can be applied to any rapidly evolving pathogen with adequate genomic surveillance data.

Ultrasound ability in early diagnosis of metatarsal stress fractures.

Stress fractures are common in young and active individuals, associated with aggressive or repetitive physical activity and their early detection is fundamental to optimise patient care, decrease complications and avoid unnecessary exams. Currently, magnetic resonance imaging is the standard of care for detecting these lesions. Recently, ultrasound has been getting an increasing interest for the detection of stress fractures. In this article, we describe a clinical case that involved a second metatarsal stress fracture diagnosed by ultrasound and review the literature regarding the use of ultrasound in the diagnosis of stress fractures, particularly of the metatarsals.

Quantitative Neuromuscular Monitoring Permits Early Diagnosis of Abnormal Butyrylcholinestrase: Two Case Studies Demonstrating Prevention of Awareness from Premature Awakening.

Administration of succinylcholine to patients with a variant in the butyrylcholinesterase (BChE) gene increases the risk of anesthesia emergence prior to recovery from neuromuscular blockade (NMB). Application of quantitative neuromuscular monitoring (NMM) can identify residual NMB. We present two patients with abnormal BChE gene variants. In the first case, quantitative monitoring was applied too late to prevent awareness, but allowed diagnosis and prevented admission to the intensive care unit. In the second case, monitoring was applied prior to NMB, which enabled early diagnosis and prevented premature awakening from anesthesia. These cases illustrate the importance of quantitative NMM, even in short cases and with short-acting depolarizing agents such as succinylcholine. The clinical implications of this report include a more consistent use of NMM to identify and manage patients with undiagnosed abnormal BChE and to prevent premature anesthesia emergence.

Características epidemiológicas, clínicas y funcionales de los pacientes mayores de 75 años ingresados en un hospital terciario durante la primera ola pandémica por SARS-CoV-2 / Epidemiological, clinical, and functional characteristics of patients older than 75 years admitted to a tertiary hospital during the first wave of the SARS-CoV-2 pandemic

Fundamento: Analizar las características epidemiológicas, clínicas y funcionales de los pacientes ingresados en el Hospital Universitario de Navarra por infección por SARS-CoV-2, así como los factores predictores de mortalidad, durante la primera ola de la pandemia provocada por este virus. Metodología: Estudio observacional y retrospectivo de todos los pacientes hospitalizados mayores de 75 años entre marzo y noviembre de 2020. Se ha obtenido información sobre múltiples variables, entre las que cabe destacar los síndromes geriátricos previos y que han aparecido durante la hospitalización, o los antecedentes médicos considerados relevantes en la infección por SARS-CoV-2. Se ha realizado un análisis descriptivo de los datos, comparaciones según diversos factores de interés y análisis multivariable para analizar los factores asociados a la mortalidad. Resultados: Se obtuvieron datos de un total de 426 pacientes cuya edad media fue de 83,2 años (52,6% varones). El 34,7% fallecieron en el hospital y el 4,5% antes de un mes tras el alta hospitalaria. Los factores relacionados con la mortalidad fueron: peor situación funcional basal, enfermedad renal crónica y fiebre o disnea como formas de presentación. Los síntomas típicos más frecuentes fueron: fiebre, disnea, tos, astenia e hiporexia. Hasta el 42,1% presentaron delirium como síntoma de inicio atípico. Se objetivó un deterioro funcional que no se recuperó al mes de seguimiento (índice de Barthel basal 81,12; 70,08 al alta; 75,55 al mes). Conclusiones: La infección por SARS-CoV-2 ha provocado elevadas tasas de mortalidad en las personas mayores. En este grupo etario, es frecuente la forma de presentación atípica de esta enfermedad y el deterioro funcional durante la hospitalización. En el presente estudio se ha identificado un peor estado funcional previo como predictor de mortalidad. Son necesarios más estudios que evalúen el impacto que la enfermedad y la hospitalización tienen en el paciente mayor...(AU)

Background: The objective of the present study is to analyze the epidemiological, clinical and functional characteristics of patients admitted to the University Hospital of Navarra due to SARS-CoV-2 infection, as well as the predictors of mortality, during the first wave of the pandemic caused by this virus. Methodology: An observational, retrospective study was performed, including all hospitalized patients older than 75 years. Information has been obtained on multiple variables, among which it is worth mentioning previous geriatric syndromes or those that have appeared during hospitalization, or past medical history considered relevant in SARS-CoV-2 infection. A descriptive analysis of the data, comparisons according to various factors of interest and multivariate analysis to analyze factors associated with mortality were carried out. Results: Data have been obtained from a total of 426 patients with a mean age of 83.2 years (52.6% men). 34.7% died in hospital and 4.5% within 1 month after hospital discharge. The factors related to mortality were: worse baseline functional status, chronic kidney disease, and fever or dyspnea as forms of presentation. The most frequent typical symptoms were: fever, dyspnea, cough, asthenia and hyporexia. Up to 42.1% presented delirium as a symptom of atypical onset. We observed a functional deterioration that was not recover after a month of follow-up (baseline Barthel index 81.12; 70.08 at discharge; 75.55 after a month). Conclusions: SARS-CoV-2 infection has caused high mortality rates in older adults. In this age group, the atypical presentation of this disease and functional deterioration during hospitalization are frequent. In the present study, a worse previous functional status has been identified as a predictor of mortality. More studies are needed to evaluate the impact that the disease and hospitalization have on the older patient...(AU)

Manejo de la trombosis venosa cerebral en España: estudio descriptivo MOTIVATE / Management of cerebral venous thrombosis in Spain: MOTIVATE descriptive study

Introducción: La trombosis venosa cerebral (TVC) es una causa poco común de ictus que afecta principalmente a adultos jóvenes. Un diagnóstico precoz y preciso puede reducir la tasa y gravedad de las complicaciones. Objetivo: Analizar las características clínicas, manejo y tratamiento de la TVC en diferentes centros de nuestro país. Métodos: Estudio descriptivo retrospectivo multicéntrico de pacientes hospitalizados por TVC entre 2008 y 2017 en 11 centros sanitarios en nuestro país. Resultados: Se incluyeron 256 pacientes, edad media 49,8 ± 18,7 años y el 51% fueron mujeres. Los síntomas más frecuentes fueron: cefalea (73%), déficits focales (50%), crisis epilépticas (33%) y encefalopatía (21%). Las localizaciones más frecuentes fueron: seno longitudinal superior (12,5%), transverso (10,9%) y afectación de dos o más senos o venas (66,4%). La etiología conocida más frecuente fue la trombofilia (24%), siendo la mutación de la protrombina G20210A la más común (19%). El 46% fue tratado con antitrombóticos durante 3-6 meses, el 21% durante un año y un 22,6% de los pacientes requirieron anticoagulación indefinida. En un 5% de los sujetos fue preciso terapia endovascular y un 33% requirió neurocirugía. En relación al pronóstico, el 75% fueron independientes a los 3 meses con una puntuación en la escala de Rankin modificada (mRS) ≤ 2 y la presencia de papiledema (p = 0,03), déficit focal (p = 0,001) y encefalopatía (p < 0,001) se relacionaron significativamente con mal pronóstico (mRS > 3). La tasa de mortalidad intrahospitalaria fue del 4,3% y el 6,3% de los pacientes fallecieron a los 3 meses. Conclusión:La diversidad de factores de riesgo y la presentación variable suponen un desafío en el diagnóstico y tratamiento de la TVC. Para mejorar el pronóstico y reducir la mortalidad es fundamental la instauración de protocolos en el manejo de esta patología.(AU)

Introduction: Cerebral venous thrombosis (CVT) is an uncommon cause of stroke that mainly affects young adults. Early, accurate diagnosis can reduce the rate and severity of complications. Objective: The aim of this study was to analyse the clinical characteristics, management, and treatment of CVT in different centres in Spain. Methods: We conducted a multicentre, retrospective, descriptive study of patients hospitalised due to CVT between 2008 and 2017 at 11 Spanish centres. Results: We included 256 patients, with a mean age (SD) of 49.8 (18.7) years; 51% of patients were women. The most frequent symptoms were headache (73%), focal deficits (50%), epileptic seizures (33%), and encephalopathy (21%). The most frequent localisations were the superior sagittal sinus (12.5%), the transverse sinus (10.9%), and 2 or more sinuses or veins (66.4%). Thrombophilia was the most frequent known aetiology (24%), and was most commonly associated with the prothrombin G20210A mutation (19%). Forty-six percent of patients were treated with antithrombotics for 3-6 months, 21% for one year, and 22.6% required indefinite anticoagulation. Endovascular therapy was performed in 5% of cases, and 33% required neurosurgery. Regarding outcomes, 75% of patients were independent at 3 months (modified Rankin Scale [mRS] score ≤ 2), with papilloedema (P = .03), focal deficits (P = .001), and encephalopathy (P < .001) showing a statistically significant association with poor prognosis (mRS > 3). The in-hospital mortality rate was 4.3%, with a 3-month mortality rate of 6.3%. Conclusion: The diverse risk factors and variable presentation of CVT represent a challenge in the diagnosis and treatment of this condition. To improve prognosis and reduce mortality, it is essential to establish management protocols for this entity.(AU)

Synaptic vesicle glycoprotein 2 A in serum is an ideal biomarker for early diagnosis of Alzheimer's disease.

BACKGROUND: Previous studies have demonstrated that early intervention was the best plan to inhibit the progression of Alzheimer's disease (AD), which relied on the discovery of early diagnostic biomarkers. In this study, synaptic vesicle glycoprotein 2 A (SV2A) was examined to improve the early diagnostic efficiency in AD. METHODS: In this study, biomarker testing was performed through the single-molecule array (Simoa). A total of 121 subjects including cognitively unimpaired controls, amnestic mild cognitive impairment (aMCI), AD and other types of dementia underwent cerebrospinal fluid (CSF) SV2A testing; 430 subjects including health controls, aMCI, AD and other types of dementia underwent serum SV2A, glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL) and p-tau217 testing; 92 subjects including aMCI and AD underwent both CSF SV2A and serum SV2A testing; 115 cognitively unimpaired subjects including APOE ε4 carriers and APOE ε4 non-carriers were tested for serum SV2A, GFAP, NfL and p-tau217. Then, the efficacy of SV2A for the early diagnosis of AD and its ability to identify those at high risk of AD from a cognitively unimpaired population were further analyzed. RESULTS: Both CSF and serum SV2A significantly and positively correlated with cognitive performance in patients with AD, and their levels gradually decreased with the progression of AD. Serum SV2A demonstrated excellent diagnostic efficacy for aMCI, with a sensitivity of 97.8%, which was significantly higher than those of NfL, GFAP, and p-tau217. The SV2A-positive rates ranged from 92.86 to 100% in aMCI cases that were negative for the above three biomarkers. Importantly, of all the biomarkers tested, serum SV2A had the highest positivity rate (81.82%) in individuals at risk for AD. CONCLUSIONS: Serum SV2A was demonstrated to be a novel and ideal biomarker for the early diagnosis of AD, which can effectively distinguish those at high risk of AD in cognitively unimpaired populations.

Metagenomic Next-Generation Sequencing Contributes to the Early Diagnosis of Mixed Infections in Central Nervous System.

Central nervous system (CNS) infections represent a challenge due to the complexities associated with their diagnosis and treatment, resulting in a high incidence rate and mortality. Here, we presented a case of CNS mixed infection involving Candida and human cytomegalovirus (HCMV), successfully diagnosed through macrogenomic next-generation sequencing (mNGS) in China. A comprehensive review and discussion of previously reported cases were also provided. Our study emphasizes the critical role of early pathogen identification facilitated by mNGS, underscoring its significance. Notably, the integration of mNGS with traditional methods significantly enhances the diagnostic accuracy of CNS infections. This integrated approach has the potential to provide valuable insights for clinical practice, facilitating early diagnosis, allowing for treatment adjustments, and ultimately, improving the prognosis for patients with CNS infections.

Target product profiles: leprosy diagnostics.

The World Health Organization (WHO) aims to reduce new leprosy cases by 70% by 2030, necessitating advancements in leprosy diagnostics. Here we discuss the development of two WHO's target product profiles for such diagnostics. These profiles define criteria for product use, design, performance, configuration and distribution, with a focus on accessibility and affordability. The first target product profile outlines requirements for tests to confirm diagnosis of leprosy in individuals with clinical signs and symptoms, to guide multidrug treatment initiation. The second target product profile outlines requirements for tests to detect Mycobacterium leprae or M. lepromatosis infection among asymptomatic contacts of leprosy patients, aiding prophylactic interventions and prevention. Statistical modelling was used to assess sensitivity and specificity requirements for these diagnostic tests. The paper highlights challenges in achieving high specificity, given the varying endemicity of M. leprae, and identifying target analytes with robust performance across leprosy phenotypes. We conclude that diagnostics with appropriate product design and performance characteristics are crucial for early detection and preventive intervention, advocating for the transition from leprosy management to prevention.

L'Organisation mondiale de la Santé (OMS) vise à réduire le nombre de nouveaux cas de lèpre de 70% d'ici 2030, ce qui nécessite un meilleur diagnostic de la maladie. Dans le présent document, nous évoquons le développement de deux profils de produit cible établis par l'OMS à cette fin. Ces profils définissent des critères en matière d'utilisation, de conception, de performances, de configuration et de distribution du produit, en accordant une attention particulière à l'accessibilité et à l'abordabilité. Le premier profil de produit cible décrit les exigences pour les tests servant à confirmer le diagnostic de la lèpre chez les individus qui présentent des signes cliniques et des symptômes, afin d'orienter l'instauration d'un traitement à base de plusieurs médicaments. Le second profil de produit cible décrit les exigences pour les tests servant à détecter une infection à Mycobacterium leprae ou M. lepromatosis parmi les contacts asymptomatiques de patients lépreux, ce qui contribue à l'adoption de mesures prophylactiques et à la prévention. Nous avons eu recours à une modélisation statistique pour évaluer les exigences de sensibilité et de spécificité de ces tests diagnostiques. Cet article met en évidence les obstacles à l'atteinte d'un niveau élevé de spécificité en raison de l'endémicité variable de M. leprae, et à l'identification d'analytes cibles offrant de bons résultats chez les phénotypes lépreux. Nous concluons qu'un diagnostic reposant sur des caractéristiques de performance et de conception appropriées est essentiel pour détecter rapidement la maladie et intervenir en amont, et nous plaidons pour une prévention plutôt qu'une gestion de la lèpre.

La Organización Mundial de la Salud (OMS) pretende reducir los nuevos casos de lepra en un 70% para 2030, lo que requiere avances en el diagnóstico de la lepra. Aquí se analiza el desarrollo de dos perfiles de productos objetivo de la OMS para este tipo de diagnósticos. Estos perfiles definen los criterios de uso, diseño, rendimiento, configuración y distribución de los productos, centrándose en su accesibilidad y asequibilidad. El primer perfil de producto objetivo describe los requisitos de las pruebas para confirmar el diagnóstico de la lepra en personas con signos y síntomas clínicos, con el fin de orientar el inicio del tratamiento con múltiples fármacos. El segundo perfil de producto objetivo describe los requisitos de las pruebas para detectar la infección por Mycobacterium leprae o M. lepromatosis entre los contactos asintomáticos de los pacientes con lepra, para facilitar las intervenciones profilácticas y la prevención. Se utilizaron modelos estadísticos para evaluar los requisitos de sensibilidad y especificidad de estas pruebas diagnósticas. El artículo destaca las dificultades para lograr una alta especificidad, dada la diferente endemicidad de M. leprae, y para identificar analitos diana con un rendimiento sólido en todos los fenotipos de lepra. Concluimos que los diagnósticos con un diseño de producto y unas características de rendimiento adecuados son fundamentales para la detección precoz y la intervención preventiva, lo que favorece la transición del manejo de la lepra a la prevención.

Chest X-ray evaluation using machine learning to support the early diagnosis of pulmonary TB.

TB is a public health problem, and its diagnosis can be challenging. Among imaging methods, chest X-ray (CXR) is the leading choice for assessing pulmonary TB (PTB). Recent advancements in the field of artificial intelligence have stimulated studies evaluating the performance of machine learning (ML) for medical diagnosis. This study validated a new original Brazilian tool, the XmarTB, applied to CXR images to support the early diagnosis of PTB.An ML model was trained on 3,800 normal images, 3,800 abnormal CXRs without PTB and 1,376 with PTB manifestations from the publicly available TBX11K database.The binary classification can distinguish between normal and abnormal CXR with a sensitivity of 99.4% and specificity of 99.4%. The XmarTB tool had a sensitivity of 98.1% and a specificity of 99.7% in detecting TB cases among CXRs with abnormal CXRs; sensitivity was 96.7% and specificity 98.7% in detecting TB cases among all samples.This diagnostic tool can accurately and automatically detect abnormal CXRs and satisfactorily differentiate PTB from other pulmonary diseases. This tool holds significant promise in aiding the proactive detection of TB cases, providing rapid and accurate support for early diagnosis..

A Multi-Modal Classification Method for Early Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease Using Three Paradigms With Various Task Difficulties.

Alzheimer's Disease (AD) accounts for the majority of dementia, and Mild Cognitive Impairment (MCI) is the early stage of AD. Early and accurate diagnosis of dementia plays a vital role in more targeted treatments and effectively halting disease progression. However, the clinical diagnosis of dementia requires various examinations, which are expensive and require a high level of expertise from the doctor. In this paper, we proposed a classification method based on multi-modal data including Electroencephalogram (EEG), eye tracking and behavioral data for early diagnosis of AD and MCI. Paradigms with various task difficulties were used to identify different severity of dementia: eye movement task and resting-state EEG tasks were used to detect AD, while eye movement task and delayed match-to-sample task were used to detect MCI. Besides, the effects of different features were compared and suitable EEG channels were selected for the detection. Furthermore, we proposed a data augmentation method to enlarge the dataset, designed an extra ERPNet feature extract layer to extract multi-modal features and used domain-adversarial neural network to improve the performance of MCI diagnosis. We achieved an average accuracy of 88.81% for MCI diagnosis and 100% for AD diagnosis. The results of this paper suggest that our classification method can provide a feasible and affordable way to diagnose dementia.

Artificial Intelligence in Early Diagnosis of Preeclampsia.

BACKGROUND: Every day, 810 women die of preventable causes related to pregnancy and childbirth worldwide, and preeclampsia is among the top three causes of maternal deaths. AIM: To develop a diagnostic system with artificial intelligence for the early diagnosis of preeclampsia. METHODS: This retrospective study included pregnant women who were screened for the inclusion criteria on the hospital's database, and the sample consisted of the data of 1158 pregnant women diagnosed with preeclampsia and 9194 pregnant women who were not diagnosed with preeclampsia at Kahramanmaras Necip Fazil City Hospital Gynecology and Pediatrics Additional Service Building, Kahramanmaras/Turkey. The statistical analysis was performed using the Statistical Package for social sciences (SPSS) version 22 for windows. Artificial intelligence models were created using Python, scikit-learn, and TensorFlow. RESULTS: The model achieved 73.7% sensitivity (95% confidence interval (CI): 70.2%-77.1%) and 92.7% specificity (95% CI: 91.7%-93.6%) on the test set. Furthermore, the model had 90.6% accuracy (95% CI: 90.1% - 91.1%) and an area under the curve (AUC) value of 0.832 (95% CI: 0.818-0.846). The significant parameters in predicting preeclampsia in the model were hemoglobin (HGB), age, aspartate transaminase level (AST), alanine transferase level (ALT), and the blood group. CONCLUSION: Artificial intelligence is effective in the prediction and diagnosis of preeclampsia.

Development and validation a nomogram prediction model for early diagnosis of bloodstream infections in the intensive care unit.

Purpose: This study aims to develop and validate a nomogram for predicting the risk of bloodstream infections (BSI) in critically ill patients based on their admission status to the Intensive Care Unit (ICU). Patients and methods: Patients' data were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database (training set), the Beijing Friendship Hospital (BFH) database (validation set) and the eICU Collaborative Research Database (eICU-CRD) (validation set). Univariate logistic regression analyses were used to analyze the influencing factors, and lasso regression was used to select the predictive factors. Model performance was assessed using area under receiver operating characteristic curve (AUROC) and Presented as a Nomogram. Various aspects of the established predictive nomogram were evaluated, including discrimination, calibration, and clinical utility. Results: The model dataset consisted of 14930 patients (1444 BSI patients) from the MIMIC-IV database, divided into the training and internal validation datasets in a 7:3 ratio. The eICU dataset included 2100 patients (100 with BSI) as the eICU validation dataset, and the BFH dataset included 419 patients (21 with BSI) as the BFH validation dataset. The nomogram was constructed based on Glasgow Coma Scale (GCS), sepsis related organ failure assessment (SOFA) score, temperature, heart rate, respiratory rate, white blood cell (WBC), red width of distribution (RDW), renal replacement therapy and presence of liver disease on their admission status to the ICU. The AUROCs were 0.83 (CI 95%:0.81-0.84) in the training dataset, 0.88 (CI 95%:0.88-0.96) in the BFH validation dataset, and 0.75 (95%CI 0.70-0.79) in the eICU validation dataset. The clinical effect curve and decision curve showed that most areas of the decision curve of this model were greater than 0, indicating that this model has a certain clinical effectiveness. Conclusion: The nomogram developed in this study provides a valuable tool for clinicians and nurses to assess individual risk, enabling them to identify patients at a high risk of bloodstream infections in the ICU.

Molecular Engineering of Activatable NIR-II Hemicyanine Reporters for Early Diagnosis and Prognostic Assessment of Inflammatory Bowel Disease.

Molecular imaging in the second near-infrared window (NIR-II) provides high-fidelity visualization of biopathological events in deep tissue. However, most NIR-II probes produce "always-on" output and demonstrate poor signal specificity toward biomarkers. Herein, we report a series of hemicyanine reporters (HBCs) with tunable emission to NIR-II window (715-1188 nm) and structurally amenable to constructing activatable probes. Such manipulation of emission wavelengths relies on rational molecular engineering by integrating benz[c,d]indolium, benzo[b]xanthonium, and thiophene moieties to a conventional hemicyanine skeleton. In particular, HBC4 and HBC5 possess bright and record long emission over 1050 nm, enabling improved tissue penetration depth and superior signal to background ratio for intestinal tract mapping than NIR-I fluorophore HC1. An activatable inflammatory reporter (AIR-PE) is further constructed for pH-triggered site-specific release in colon. Due to minimized background interference, oral gavage of AIR-PE allows clear delineation of irritated intestines and assessment of therapeutic responses in a mouse model of inflammatory bowel disease (IBD) through real-time NIRF-II imaging. Benefiting from its high fecal clearance efficiency (>90%), AIR-PE can also detect IBD and evaluate the effectiveness of colitis treatments via in vitro optical fecalysis, which outperforms typical clinical assays including fecal occult blood testing and histological examination. This study thus presents NIR-II molecular scaffolds that are not only applicable to developing versatile activatable probes for early diagnosis and prognostic monitoring of deeply seated diseases but also hold promise for future clinical translations.

Constructing hierarchical attentive functional brain networks for early AD diagnosis.

Analyzing functional brain networks (FBN) with deep learning has demonstrated great potential for brain disorder diagnosis. The conventional construction of FBN is typically conducted at a single scale with a predefined brain region atlas. However, numerous studies have identified that the structure and function of the brain are hierarchically organized in nature. This urges the need of representing FBN in a hierarchical manner for more effective analysis of the complementary diagnostic insights at different scales. To this end, this paper proposes to build hierarchical FBNs adaptively within the Transformer framework. Specifically, a sparse attention-based node-merging module is designed to work alongside the conventional network feature extraction modules in each layer. The proposed module generates coarser nodes for further FBN construction and analysis by combining fine-grained nodes. By stacking multiple such layers, a hierarchical representation of FBN can be adaptively learned in an end-to-end manner. The hierarchical structure can not only integrate the complementary information from multiscale FBN for joint analysis, but also reduce the model complexity due to decreasing node sizes. Moreover, this paper argues that the nodes defined by the existing atlases are not necessarily the optimal starting level to build FBN hierarchy and exploring finer nodes may further enrich the FBN representation. In this regard, each predefined node in an atlas is split into multiple sub-nodes, overcoming the scale limitation of the existing atlases. Extensive experiments conducted on various data sets consistently demonstrate the superior performance of the proposed method over the competing methods.

A review on the new age methodologies for early detection of Alzheimer's and Parkinson's disease.

BACKGROUNDS: Neurodegenerative diseases (NDDs) such as Alzheimer's (AD) and Parkinson's (PD) are often diagnosed late, impeding effective treatment; therefore, early detection is imperative. Modern methodologies can serve a pivotal role in fulfilling the crucial need for timely detection and intervention in this context. OBJECTIVES: Evaluate early detection's significance and summarize key technologies (biomarkers, neuroimaging, AI/ML, genetics, digital health) for enhanced diagnostic strategies in AD and PD. METHODS: This study employs a focused descriptive review approach, encompassing analysis of peer-reviewed articles and clinical trials from existing literature, to provide a nuanced exploration of the subject matter. FINDINGS: This review underscores the efficacy of non-invasive biomarkers, biosensors and emerging promising technologies for advancing early diagnosis of AD and PD. CONCLUSION: The landscape of early NDD detection has been reshaped by technology, yet challenges persist, encompassing the domains of validation and ethics. A collaborative effort between medical professionals, researchers and technologists is imperative to effectively address and combat NDDs.

Identification of Genetic Variants for Risk Prediction and Early Diagnosis of Age-Related Macular Degeneration in the Taiwanese Population.

Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly worldwide. The prevalence and phenotypes of AMD differ among populations, including between people in Taiwan and other regions. We performed a genome-wide association study to identify genetic variants and to develop genetic models to predict the risk of AMD development and progression in the Taiwanese population. In total, 4039 patients with AMD and 16,488 non-AMD controls (aged ≥ 65 years) were included. We identified 31 AMD-associated variants (p < 5 × 10-8) on chromosome 10q26, surrounding PLEKHA1-ARMS2-HTRA1. Two genetic models were constructed using the clump and threshold method. Model 1 included the single nucleotide polymorphism rs11200630 and showed a 1.31-fold increase in the risk of AMD per risk allele (95% confidence interval (CI) = 1.20-1.43, p < 0.001). In model 2, 1412 single-nucleotide polymorphisms were selected to construct a polygenic risk score (PRS). Individuals with the top 5% PRS had a 1.40-fold higher AMD risk compared with that of individuals with a PRS in the bottom quartile (95% CI = 1.04-1.89, p = 0.025). Moreover, the PRS in the upper quartile was related to a decreased age at AMD diagnosis by 0.62 years (95% CI = -1.15, -0.09, p = 0.023). Both genetic models provide useful predictive power for populations at high risk of AMD, affording a basis for identifying patients requiring close follow-up and early intervention.