Early Diagnosis Research Articles

Two cases of pituitary stalk interruption syndrome: importance of early detection and management

Introduction and importance: Pituitary stalk interruption syndrome (PSIS) is a rare congenital condition affecting the pituitary gland and its stalk, leading to hormonal imbalances. PSIS can present with a wide range of symptoms, including delayed puberty and short stature. Case presentation: This paper discusses two cases of PSIS in patients with a history of growth hormone deficiency. The first case is of a 26-year-old male presenting with fatigue and loss of appetite, while the second case is of a 14-year-old male presenting with delayed puberty. Blood tests revealed hormonal imbalances, and a subsequent MRI confirmed the diagnosis of PSIS. Hormonal supplements were prescribed to manage the condition, and follow-up appointments were scheduled to monitor progress. Clinical discussion: PSIS can present with a wide range of symptoms, and can be diagnosed at different ages. Early diagnosis and management of PSIS are crucial to prevent long-term complications such as short stature, impaired cognitive function, and infertility. The use of hormonal supplements, as seen in both cases, is essential to manage the hormonal imbalances associated with PSIS. Testosterone replacement therapy is used to treat hypogonadism, while thyroxine and hydrocortisone are used to manage hypothyroidism and adrenal insufficiency, respectively. Conclusion: Early diagnosis and management of PSIS through hormonal supplements are crucial to prevent long-term complications. It is essential to monitor patients’ progress through follow-up appointments to ensure optimal management of the condition.

Analyzing electroencephalograph signals for early Alzheimer’s disease detection: deep learning vs. traditional machine learning approaches

Alzheimer’s disease (AD) stands as a progressive neurodegenerative disorder with a significant global public health impact. It is imperative to establish early and accurate diagnoses of AD to facilitate effective interventions and treatments. Recent years have witnessed the emergence of machine learning (ML) and deep learning (DL) techniques, displaying promise in various medical domains, including AD diagnosis. This study undertakes a comprehensive contrast between conventional machine learning methods and advanced deep learning strategies for early AD diagnosis. Conventional ML algorithms like support vector machines, decision trees, and K nearest neighbor have been extensively employed for AD diagnosis through relevant feature extraction from heterogeneous data sources. Conversely, deep learning techniques such as multilayer perceptron (MLP) and convolutional neural networks (CNNs) have demonstrated exceptional aptitude in autonomously uncovering intricate patterns and representations from unprocessed data like EEG data. The findings reveal that while traditional ML methods may perform adequately with limited data, deep learning techniques excel when ample data is available, showcasing their potential for early and precise AD diagnosis. In conclusion, this research paper contributes to the ongoing discourse surrounding the choice of appropriate methodologies for early Alzheimer’s disease diagnosis.

A Novel Peptidome Technology for the Diagnosis of Mild Cognitive Impairment and Alzheimer’s Disease by Selected Reaction Monitoring

Background: With the aging of populations worldwide, Alzheimer’s disease (AD) has become a concern due to its high prevalence and the continued lack of established treatments. Early diagnosis is required as a preventive intervention to modify the disease’s progression. In our previous study, we performed peptidomic analysis of serum samples obtained from AD patients and age-matched healthy subjects to seek peptide biomarker candidates for AD by using BLOTCHIP-MS analysis, and identified four peptides as AD biomarker candidates. Objective: The objective was to validate the serum biomarker peptides to distinguish mild cognitive impairment (MCI) and AD in comparison to cognitively healthy controls using a new peptidome technology, the Dementia Risk Test. Methods: We enrolled 195 subjects with normal cognitive function (NC; n = 70), MCI (n = 55), and AD (n = 70), The concentrations of cognitive impairment marker peptides (Fibrinogen α chain (FAC), Fibrinogen β chain (FBC), Plasma protease C1 inhibitor (PPC1I), α2-HS-glycoprotein (AHSG)) were quantified by using a selected reaction monitoring assay based on liquid chromatography-MS/MS. Results: The present study confirmed that three peptides, FAC, FBC, and PPC1I, were significantly upregulated during the onset of AD. This three-peptide set was both highly sensitive in determining AD (sensitivity: 85.7%, specificity: 95.7%, AUC: 0.900) and useful in distinguishing MCI (sensitivity: 61.8%, specificity: 98.6%, AUC: 0.824) from NC. Conclusions: In this validation study, we confirmed the high diagnostic potential of the three peptides identified in our previous study as candidate serum biomarkers for AD. The Dementia Risk Test may be a powerful tool for detecting AD-related pathological changes.

The long noncoding RNA MANCR to promote and stabilize triple negative breast cancer.

e13150 Background: Cancer remains a leading cause of death worldwide, despite many advances in cancer treatment over the past decade. Triple Negative Breast Cancer (TNBC) is among the highest with a poor prognosis. Early diagnosis and modern treatment regimens for breast cancer (BC) have led to a high disease-free survival rate. Still, survival decreases significantly for patients with metastatic disease. Long non-coding RNAs are among a recent class of epigenetic regulators that function in the nucleus to support the stability of the cells and maintain the fidelity of chromatin interactions. Our laboratory discovered the long noncoding RNA MANCR (LINC00704) as being upregulated in human BC tumors and this is indicative of a worse survival rate than in patients with low MANCR-expressing tumors. MANCR is aberrantly expressed in TNBC cells, and these cells are highly dependent on MANCR to retain their tumorigenic characteristics. Methods: Our functional in vitro studies in MDA-MB-231 TNBC cells used GapmerRs to knockdown MANCR. For in vivo studies, TNBC cells were injected into the 4th mammary fat pad of mice, allowed initial tumor formation, and then treated the animals with 2 nmol/g of a negative control or MANCR targeting GapmeR. We also identified genome interaction sites at single nucleotide resolution by chromatin isolation by RNA purification (ChIRP)-seq to determine the mechanism of MANCR activity in TNBC cells. Results: We found that MANCR knockdown promotes DNA damage and decreases cell proliferation, migration, anchorage-independent colony formation, transwell invasion, and overall survival. Additionally, targeting MANCR in animals with existing tumors drastically inhibited tumor growth and the end-point tumor mass over time. The MANCR GapmeR treatment also inhibited the ability of TNBC cells to circulate and disseminate to distant organs. After performing ChIRP-seq in the MDA-MB-231 cells, we identified 988 genome-wide binding sites that exhibit MANCR interactions, of which 79% are intergenic and 21% are genic regions. Furthermore, 127 MANCR ChIRP peaks were found to overlap with fragile sites in the genome, indicating MANCR provides stability to these sites. Conclusions: These data suggest that targeting MANCR has therapeutic potential for patients with “MANCR-high” TNBC tumors by disrupting genome stability. Indeed, our in vivo studies demonstrate that “MANCR-high” TNBC tumors require MANCR to rapidly grow and promote disease progression. Lastly, many of the MANCR-chromatin interactions identified were found in intergenic regions and overlap with fragile sites within the genome. Collectively these data strongly indicate that MANCR stabilizes the TNBC genome, and disrupting genome stability by targeting MANCR has therapeutic potential.

Automatic detection of epilepsy from EEGs using a temporal convolutional network with a self-attention layer

BackgroundOver 60% of epilepsy patients globally are children, whose early diagnosis and treatment are critical for their development and can substantially reduce the disease’s burden on both families and society. Numerous algorithms for automated epilepsy detection from EEGs have been proposed. Yet, the occurrence of epileptic seizures during an EEG exam cannot always be guaranteed in clinical practice. Models that exclusively use seizure EEGs for detection risk artificially enhanced performance metrics. Therefore, there is a pressing need for a universally applicable model that can perform automatic epilepsy detection in a variety of complex real-world scenarios.MethodTo address this problem, we have devised a novel technique employing a temporal convolutional neural network with self-attention (TCN-SA). Our model comprises two primary components: a TCN for extracting time-variant features from EEG signals, followed by a self-attention (SA) layer that assigns importance to these features. By focusing on key features, our model achieves heightened classification accuracy for epilepsy detection.ResultsThe efficacy of our model was validated on a pediatric epilepsy dataset we collected and on the Bonn dataset, attaining accuracies of 95.50% on our dataset, and 97.37% (A v. E), and 93.50% (B vs E), respectively. When compared with other deep learning architectures (temporal convolutional neural network, self-attention network, and standardized convolutional neural network) using the same datasets, our TCN-SA model demonstrated superior performance in the automated detection of epilepsy.ConclusionThe proven effectiveness of the TCN-SA approach substantiates its potential as a valuable tool for the automated detection of epilepsy, offering significant benefits in diverse and complex real-world clinical settings.

Neglected Developmental Hip Dysplasia Treated With External Iliofemoral Distraction, Open Reduction, and Pelvic Osteotomy: Eleven-Year Follow-Up.

Neglected untreated developmental hip dysplasia can result in severe deformities and functional disabilities. This report describes the treatment and 11-year follow-up of a patient who underwent the procedure in two stages. At the time of treatment, the patient was 7 years of age and was diagnosed with acetabular dysplasia and neglected right hip dysplasia. A stepwise treatment approach, including gradual distraction of the iliofemoral joint, open reduction, and pelvic osteotomy, was performed. Subsequent postoperative rehabilitation and regular follow-up assessments were performed over 11 years. The long-term results indicated notable improvements in hip joint congruency, functional range of motion, and overall quality of life. Early diagnosis and appropriate intervention are crucial for patients with developmental hip dysplasia, and the treatment methods described here are effective.

Detection of chronic kidney disease using binary whale optimization algorithm

Chronic kidney disease (CKD), a medical illness, is characterized by a steady deterioration in kidney function. A disease's ability to be prevented and effectively significantly treated depends on early diagnosis. The addition of filter feature selection to the machine learning algorithm has been done to detect CKD. However, the quality of its feature subset is not optimal. Wrapper feature selection can improve the quality of these feature subsets. Therefore, we proposed wrapper feature selection and binary whale optimization algorithm (BWOA) to enhance the accuracy of early CKD detection. We also make data improvements to improve accuracy, namely the preprocessing process with the median and modus techniques. We used a public dataset of 250 medical records of kidney sufferers and 150 completely healthy people. There are 24 features in this dataset. The test results showed that adding BWOA feature selection can increase accuracy. The proposed method produced an accuracy of 100%. Further research on these methods can be used to develop expert systems for early detection of CKD.

PEDIATRIC OCULAR TRAUMA MANAGEMENT: A CASE STUDY OF EFFECTIVE INTRA-ORBITAL FOREIGN BODY IOFB EXTRACTION

Highlights: The rare nature of Orbitocranial Injury with Intraorbital Foreign Body (IOFB). Utilizing a 3D Head CT Scan to ascertain the precise location of the IOFB and its impact on anatomical structures is pivotal in shaping immediate treatment strategies, thereby reducing the potential for complications. Abstract: Introduction: Penetrating intraorbital foreign body (IOFB) injuries are a rare form of traumatic injury. Recognized as a surgical emergency, meticulous history-taking and a thorough examination are essential for determining the trauma's mechanism and cause, facilitating accurate diagnosis and effective disease management. Case Illustration: In the emergency room, a 5-year-old female patient was presented with a craniofacial injury resulting from an IOFB. An evacuation procedure, conducted through a subsidiary incision, was performed to extract the foreign body. Interestingly, the object removed from the orbital cavity was a plastic foreign body, initially suspected to be a fractured orbital bone. Discussion: The clinical presentation, management, and outcomes of orbital foreign body injuries can differ based on the material composition of the foreign body. In cases of intraorbital foreign body trauma, the primary treatment goal is foreign body removal. Conclusion: Precise and accurate initial management in the emergency department is crucial. Early diagnosis, aided by a 3D head CT scan, is instrumental in locating the IOFB and identifying the involved anatomical structures. This, in turn, enables timely treatment decisions to minimize the risk of complications.

Survival outcome prediction of breast carcinomas on whole-slide histopathology images using deep learning.

1070 Background: Breast cancer is the most common cancer among women with 2 million new cases and 627,000 deaths in 2020. Early diagnosis and effective treatment are crucial for improved outcomes. Prognosis mainly depends on histopathological features, among them grade. As whole slide histopathology image (WSI) of tumor tissue contains a huge amount of hidden morphological features unexploited by pathologists, we investigate the potential of Artificial Intelligence (AI)-based analysis on WSI to predict prognosis in terms of 5-year overall survival in breast cancer patients. Methods: We used a novel deep neural network (DNN), DiaDeepBreastPRS, designed specifically for predicting a survival risk score in breast cancer patients based on H&E-stained whole slide images (WSI) of tumor tissues. The model incorporates two distinct viewpoints, one capturing cellular details and the other the tissue-level information. DiaDeepBreastPRS was trained and evaluated on a multi-centric discovery cohort of 1,027 patients (1,095 H&E WSI) from the TCGA-BRCA dataset using a cross-testing and cross-validation technique. It was evaluated on an external cohort, comprising 232 patients (247 H&E WSI). A statistical analysis with a multivariate Cox regression model was carried out on clinico-pathological data. AI scores and the concordance index (c-index) serves as the metric for assessing the performance. The predicted risk scores were used for effective risk stratification of breast carcinomas. Results: On the TCGA-BRCA dataset, the model achieved an average c-index of 67%, which reaches 78% by adding the pTNM stage and age at diagnosis. On the external dataset, the model achieved a c-index of 66% and 75% when including some histopathological prognosis factors (pTNM stage, age at diagnosis, HER2 and HR status and mitosis). The AI score was independently associated with the survival of breast cancer patients with the highest hazard ratio (HR 2.46, p<0.005). Furthermore, the model was able to significantly discriminate between the 2 groups of patients, with a good and a poor prognosis in terms of overall survival (p<0.005). Conclusions: In this study, we showcased that the algorithm was able to instantly extract prognostic morphological features from H&E whole slide images (WSI) and could be included in the pathology report. This could potentially enhance clinical decision-making, elevating the standard of care. Compared to commonly used molecular signatures, the AI algorithm enables a reduction in response time and cost savings. However, further investigations using additional independent cohorts are essential to consolidate the algorithm's performance and allow its generalizability, establish its superiority over existing prognostic markers, and provide insights into its interpretability.

Nursing Intervention to Prevent and Manage Delirium in Critically Ill Patients: A Scoping Review.

Delirium is an acute neuropsychiatric syndrome of multifactorial etiology with a high incidence in people admitted to intensive care units. In addition to reversible impairment of cognitive processes, it may be associated with changes in thinking and perception. If, in the past, it was considered an expected complication of severe disease, nowadays, delirium is associated with a poor short-term and long-term prognosis. Knowing that its prevention and early identification can reduce morbidity, mortality, and health costs, it is vital to investigate nursing interventions focused on delirium in critically ill patients. This study aimed to identify nursing interventions in the prevention and management of delirium in critically ill adults. The method used to answer the research question was a scoping review. The literature search was performed in the Medline (via PubMed), CINAHL (via EBSCOhost), Scopus, Web of Science, and JBI databases. The final sample included 15 articles. Several categories of non-pharmacological interventions were identified, addressing the modifiable risk factors that contribute to the development of delirium, and for which nurses have a privileged position in their minimization. No drug agent can, by itself, prevent or treat delirium. However, psychoactive drugs are justified to control hyperactive behaviors through cautious use. Early diagnosis, prevention, or treatment can reduce symptoms and improve the individual's quality of life. Therefore, nursing professionals must ensure harmonious coordination between non-pharmacological and pharmacological strategies.

Outcomes among hospitalized patients with colorectal cancer and comorbid congestive heart failure: A United States population-based cohort study.

e24019 Background: Colorectal cancer is one of the leading causes of cancer related deaths in the United States. Advances in chemotherapeutics have improved survival in these patients, although with the potential for more adverse effects. Congestive heart failure continues to be an important comorbidity in patients with colorectal cancer. However, its impact on outcomes in this subgroup needs further exploration. Methods: We utilized the 2020 National Inpatient Sample (NIS) Database in conducting this retrospective cohort study. We identified patients with colorectal cancer and congestive heart failure using appropriate ICD-10 diagnostic codes. We stratified patients with colorectal cancer based on the presence or absence of congestive heart failure. A survey multivariable logistic and linear regression analysis was used to calculate adjusted odds ratios (ORs) for the primary and secondary outcomes. A p value of < 0.05 was considered statistically significant. The aim of this study was to investigate the impact of congestive heart failure on in-hospital mortality, hospital length of stay (LOS), and total hospitalization charge among hospitalized patients with colorectal cancer. Results: We identified a total of 77130 hospitalized patients with colorectal cancer, of which 10.08% (7755/77130) had comorbid congestive heart failure. The overall in-hospital mortality among patients with colorectal cancer was 2.74% (2115/77130). Among those with comorbid congestive heart failure, the mortality rate was significantly higher at 6.5% (504/7755, p < 0.001). Utilizing a stepwise survey multivariable logistic regression model that adjusted for patient and hospital level confounders, congestive heart failure was found to be an independent predictor of increased in-hospital mortality (adjusted OR 1.79; 95% (confidence interval [CI], 1.33-2.41; p < 0.001), longer LOS (coefficient 2.42; CI 1.91-2.92; p < 0.001), and higher total hospitalization charge (coefficient $22464; CI $15132- $29795; p < 0.001). Conclusions: Our analysis demonstrated that congestive heart failure is widely prevalent in patients with colorectal cancer, and significantly worsens outcomes including increased in-hospital mortality, LOS, and cost of healthcare utilization. Early diagnosis and management of heart failure using guideline directed medical therapy along with close follow up may improve outcomes. Further prospective multicentric studies are warranted to better describe these associations.

Prevention of Parkinson's Disease: From Risk Factors to Early Interventions.

Parkinson's disease (PD) is a debilitating neurological disorder characterized by progressively worsening motor dysfunction. Currently, available therapies merely alleviate symptoms, and there are no cures. Consequently, some researchers have now shifted their attention to identifying the modifiable risk factors of PD, with the intention of possibly implementing early interventions to prevent the development of PD. Four primary risk factors for PD are discussed including environmental factors (pesticides and heavy metals), lifestyle (physical activity and dietary intake), drug abuse, and individual comorbidities. Additionally, clinical biomarkers, neuroimaging, biochemical biomarkers, and genetic biomarkers could also help to detect prodromal PD. This review compiled available evidence that illustrates the relationship between modifiable risk factors, biomarkers, and PD. In summary, we raise the distinct possibility of preventing PD via early interventions of the modifiable risk factors and early diagnosis.

Homozygosity of a Founder Variant c.1508dupC in DOK7 Causes Congenital Myasthenia With Variable Severity.

Description of 15 patients with the same variant in DOK7 causing congenital myasthenic syndrome (CMS). Nine adult and 6 pediatric patients were studied with molecular genetic and clinical investigations. All patients were identified with the c.1508dupC variant in DOK7, of whom 13 were homozygous and 2 patients compound heterozygous. Only 2 patients had limb girdle phenotype, while all adult patients also had ptosis, ophthalmoplegia, facial weakness, as well as inspiratory stridor. Pediatric patients had severe respiratory insufficiency and feeding difficulties at birth. The disease severity in our patients varied extensively from ventilator or wheelchair dependence to mild facial weakness, ptosis, and ophthalmoparesis. Most of the patients had normal transmission in conventional 3 Hz stimulation electrophysiologic studies, making the diagnosis of CMS challenging. Our cohort of adult and pediatric patients expands the phenotype of DOK7 CMS and shows the importance of correct and early diagnosis.

Unmasking Sturge–Weber syndrome in adulthood: a case with extrafacial port-wine stain and delayed neurological symptoms

Background: Sturge–Weber syndrome (SWS) is a rare neurocutaneous disorder characterized by a facial port-wine birthmark, leptomeningeal angiomatosis, and glaucoma. This case report highlights the challenges of diagnosing SWS when presenting with atypical features. Here, the authors present a 55-year-old man with an extrafacial port-wine stain and delayed-onset seizures, deviating from the classic triad. Case presentation: A 55-year-old man presented with a recent seizure and a characteristic port-wine birthmark extending beyond the typical facial region. Neurological examination revealed no weakness, speech difficulties, or coordination problems. Ophthalmological examination didn’t reveal glaucoma. Limited resources restricted access to advanced imaging like MRI scans. However, based on the constellation of clinical findings, including the facial birthmark with angiomatosis and the new-onset seizure, the patient received a diagnosis of SWS. Treatment with Levetiracetam was initiated to prevent future seizures, and patient education on managing diabetes and hypertension was provided. Clinical discussion: This case underscores the importance of considering SWS in diagnosing adult-onset seizures, especially with a characteristic facial birthmark. The delayed presentation and isolated seizure suggest potentially less severe brain involvement. Resource limitations necessitated a clinical diagnosis and treatment with readily available medications. Conclusion: This case highlights the challenges of diagnosing atypical SWS presentations. Early diagnosis is crucial for prompt management and improved patient outcomes. Future research should focus on developing robust diagnostic tools and exploring novel treatment options for atypical SWS presentations.

Myofibroblastic sarcoma: A study of demographic and socioeconomic factors.

e23545 Background: Myofibroblastic sarcoma is a type of non-rhabdomyosarcoma soft tissue sarcoma originating from spindle cells of mesenchymal origin with myofibroblastic differentiation. First described in 1998, there is little known about the prognostic factors of this rare and relatively new cancer. This typically non-metastasizing tumor is primarily found in the head, neck, or extremities. Treatment focuses on tumor excision, and the 5-year overall survival of low-grade myofibroblastic sarcoma cancer is 72%. This study analyzed data from the National Cancer Database (NCDB) to understand how demographic factors contribute to the treatment and prognosis of myofibroblastic sarcoma. Methods: A retrospective cohort analysis was conducted on demographic factors of patients with confirmed myofibroblastic sarcoma (ICD-8825-3) using the NCDB from 2004-2020. Regression analysis was used to study trends of patient age, sex, race, education, insurance status, distance from facility, and Charles/Deyo score within this population. Results: A total of 111 patients (N = 111) were identified in the database with a confirmed diagnosis of myofibroblastic sarcoma from 2004 to 2020 with an increasing frequency of diagnoses per year (R^2 = 0.6283). The average age of diagnosis was 47.3 (SD = 24.17, range = 0 - 90 years). Although Myofibroblastic sarcoma appeared throughout the body, the most prominent sites were the long bones of the lower limb and associative joints (6%) followed by the uterus (5%). The majority of patients were white (83%), non-Hispanic (86%), and female (63%). A higher percentage of patients lived in metropolitan areas with a population greater than 1 million (58%) compared to smaller metropolitan, urban, or rural counties (42%). Most of the patients were treated at an academic/research program (30%) followed by a comprehensive community cancer program (21%). More facility locations were based in the Pacific (15%) and the Middle Atlantic (11%) regions. The primary payer at diagnosis was private insurance/managed care (44%) followed by Medicare (32%) and Medicaid (19%). Few patients (5%) received palliative care. Conclusions: To the best of our knowledge, this is the first NCDB analysis on Myofibroblastic sarcoma. Based on our dataset, Myofibroblastic sarcoma tends to occur in the long bones of the lower limb and associative joints which differed slightly from what we found in previous reports, which described the head and neck as the two areas of highest prevalence. This is the first time the socioeconomic factors have been described in the literature: these patients are more likely to be non-Hispanic, female individuals, living in smaller towns. This study offers insight on how much further investigation is required to better understand the impact of demographic and socioeconomic factors on early diagnosis and treatments.

Cerebral Palsy: A Current Perspective.

Cerebral palsy (CP) is the most common cause of motor disability in children. Insults to the brain at different times lead to diverse injuries. As a result, CP is an extremely heterogeneous clinical diagnosis, presenting differently in each individual and at various ages. With improving survival rates of preterm newborns, increasing active resuscitation of extremely preterm newborns, and widespread availability of extensive genetic testing soon after birth, it is imperative to focus on earlier diagnosis and long-term outcomes of CP. CP is primarily classified into 4 categories based on type of motor impairment, functional ability, distribution, and etiology. As the understanding of CP has evolved significantly in the last 2 decades, the methods of early detection of CP have consequently advanced. Appropriate diagnosis is essential for proper education and counseling of affected families, and introduction of therapeutic interventions as early as possible. In this review, we focus on early brain development and provide an overview of the etiology, classification, diagnosis, early therapeutic options, and prognosis of CP.

Handheld echocardiography for the screening and diagnosis of rheumatic heart disease: a systematic review to inform WHO guidelines

Early detection and diagnosis of acute rheumatic fever and rheumatic heart disease are key to preventing progression, and echocardiography has an important diagnostic role. Standard echocardiography might not be feasible in high-prevalence regions due to its high cost, complexity, and time requirement. Handheld echocardiography might be an easy-to-use, low-cost alternative, but its performance in screening for and diagnosing acute rheumatic fever and rheumatic heart disease needs further investigation. In this systematic review and meta-analysis, we searched Embase, MEDLINE, LILACS, and Conference Proceedings Citation Index-Science up to Feb 9, 2024, for studies on the screening and diagnosis of acute rheumatic fever and rheumatic heart disease using handheld echocardiography (index test) or standard echocardiography or auscultation (reference tests) in high-prevalence areas. We included all studies with useable data in which the diagnostic performance of the index test was assessed against a reference test. Data on test accuracy in diagnosing rheumatic heart disease, acute rheumatic fever, or carditis with acute rheumatic fever (primary outcomes) were extracted from published articles or calculated, with authors contacted as necessary. Quality of evidence was appraised using GRADE and QUADAS-2 criteria. We summarised diagnostic accuracy statistics (including sensitivity and specificity) and estimated 95% CIs using a bivariate random-effects model (or univariate random-effects models for analyses including three or fewer studies). Area under the curve (AUC) was calculated from summary receiver operating characteristic curves. Heterogeneity was assessed by visual inspection of plots. This study was registered with PROSPERO (CRD42022344081). Out of 4868 records we identified 11 studies, and two additional reports, comprising 15 578 unique participants. Pooled data showed that handheld echocardiography had high sensitivity (0·87 [95% CI 0·76-0·93]), specificity (0·98 [0·71-1·00]), and overall high accuracy (AUC 0·94 [0·84-1·00]) for diagnosing rheumatic heart disease when compared with standard echocardiography (two studies; moderate certainty of evidence), with better performance for diagnosing definite compared with borderline rheumatic heart disease. High sensitivity (0·79 [0·73-0·84]), specificity (0·85 [0·80-0·89]), and overall accuracy (AUC 0·90 [0·85-0·94]) for screening rheumatic heart disease was observed when pooling data of handheld echocardiography versus standard echocardiography (seven studies; high certainty of evidence). Most studies had a low risk of bias overall. Some heterogeneity was observed for sensitivity and specificity across studies, possibly driven by differences in the prevalence and severity of rheumatic heart disease, and level of training or expertise of non-expert operators. Handheld echocardiography has a high accuracy and diagnostic performance when compared with standard echocardiography for diagnosing and screening of rheumatic heart disease in high-prevalence areas. World Health Organization. For the Chinese, French, Italian, Persian, Portuguese, Spanish and Urdu translations of the abstract see Supplementary Materials section.

Ketosis-prone Diabetes Presenting with Acute Esophageal Necrosis or “Black Esophagus”: An Intriguing New Clinical Association

Background:: Ketosis-prone diabetes (KPD) is an intermediate subtype of diabetes mellitus, usually affecting Afro-American adults, presenting with diabetic ketoacidosis (DKA), without the classic phenotype of autoimmune type 1 diabetes. Patients require insulin therapy at onset for the acute decompensation, then usually remain insulin-free for prolonged periods with diet alone or with other antidiabetic drugs. DKA can be rarely complicated by upper gastrointestinal bleeding and mucosal necrosis, a severe complication named acute esophageal necrosis (AEN) burdened by high mortality. The association of KPD presenting with DKA complicated by AEN is here reported for the first time, to the knowledge of the authors, in the medical literature. Case Presentation:: Here we report an interesting case of middle-aged African woman, newly diagnosed with KPD, presenting with DKA hematemesis. The patient was first treated at Intensive Care Unit for the ketoacidosis with intravenous fluids combined with continuous insulin infusion, and then switched to subcutaneous regimen. At the same time, esophagogastroduodenoscopy (EGD) was performed to diagnose acute esophageal necrosis, which was promptly managed with proton pump inhibitors infusion, fasting, and parenteral nutrition. After the correct clinical evaluation, the patient was switched to oral antidiabetic and basal insulin at discharge and an EGD follow-up was scheduled. Conclusions:: KPD remains an under-recognized and under-diagnosed type of diabetes which can present as DKA. Since DKA could be a possible trigger of AEN, a rare but potentially lifethreatening condition, that clinicians should be aware of, in patients presenting with upper gastrointestinal bleeding and ketoacidosis. The prompt management and classification of DKA, combined with the EGD execution for early AEN diagnosis and follow-up, is essential.

Multiplexed electrochemical nucleic acid sensor based on visible light-mediated metal-free thiol-yne click reaction for simultaneous detection of different nucleic acid targets

Simultaneous detection of multiple tumor biomarkers with a simple and low-cost assay is crucial for early cancer detection and diagnosis. Herein, we presented a low-cost and simple assay for multiplexed detection of tumor biomarkers using a spatially separated electrodes strategy. The sensor is fabricated based on a metal-free thiol-yne click reaction, which is mediated by visible light, on commercially available indium tin oxide (ITO) electrodes. Four biomarkers, including p53 DNA, Brca2 DNA, K-ras DNA, and MicroRNA-204 RNA, were used as model analytes, and the corresponding oligonucleotide probes were modified on the desired electrode units sequentially with 530 nm irradiation light in the presence of photosensitizer Eosin Y. By this visible light-mediated coupling reaction, oligonucleotide probe densities of up to 9.2 ± 0.7 × 1010 molecules/cm2 were readily obtained on the ITO electrode surface. The proposed multiplexed E-NA sensor could detect four different nucleic acid targets concurrently without crosstalk among adjacent electrodes and was also successfully applied for detecting targets in a 20% fetal calf serum sample. The detection limits for p53 DNA, Brca2 DNA, K-ras DNA, and MicroRNA-204 RNA were 0.72 nM, 0.97 nM, 2.15 nM, and 1.73 nM, respectively. The developed approach not only has a great potential for developing cost-effective biosensors on affordable substrates for nucleic acid target detection, but also be easily extended to detect other targets by modifying the specific oligonucleotide probes anchored on the electrode.

Brain magnetic resonance imaging image classification for Alzheimer's disease and its hardware acceleration

<span lang="EN-US">Alzheimer's is a progressive neurodegenerative disorder and is considered the sixth leading cause of death after cancer and heart attack. Early detection and diagnosis provide individuals to go through a wider variety of clinical trials and get multiple medical benefits. Research on the application of deep learning and machine learning to the early detection of Alzheimer's disease has recently gained considerable attention. In this paper, we propose a deep learning classification framework to classify the individual with different progression stages of Alzheimer's disease such as mild cognitive impairment (MCI) and cognitive normal (CN). The dataset from Alzheimer’s disease neuroimaging initiative (ADNI) is considered in this paper which is a multisite having collection of Neuroimaging data for researchers. Structural magnetic resonance imaging (MRI) images are considered from the ADNI data set and feature extraction is done using a 2D discrete wavelet transform. 97% of data reduction is achieved during data pre-processing. The algorithm is trained and validated. The algorithm is accelerated in Nvidia Tx2 graphics processing unit (GPU) to get the better throughput. The result shows our algorithm outperforms the other deep learning algorithms with 91.56% accuracy. </span>