Analysis of the effects of early screening combined with blood lactate on the severity of patients with sepsis.

This work aimed to investigate the adoption value of blood lactic acid (BLA) combined with the National Early Warning Score (NEWS) in the early screening of sepsis patients and assessing their severity. The data and materials utilized in this work were obtained from the electronic medical record system of 537 anonymized sepsis patients who received emergency rescue in the emergency rescue area of Liuzhou People's Hospital, Guangxi, from July 1, 2020, to December 26, 2020. Based on the 28-day outcomes of sepsis patients, the medical records were rolled into Group S (407 survival cases) and Group D (130 dead cases). Basic information such as the mode of hospital admission, initial management, use of emergency ventilator within 24 h of admission, NEWS score, arterial oxygen pressure/alveolar oxygen pressure ratio (PaO2/PAO2), alveolar-arterial oxygen difference (A-aDO2), serum creatinine (SCr), blood urea nitrogen (BUN), oxygenation index (OI), Glasgow Coma Scale (GCS), D-dimer, use of vasoactive drugs within 24 h of admission, C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), N-terminal pro-B-type natriuretic peptide (NT-proBNP), quick Sequential Organ Failure Assessment (qSOFA) score, SOFA score, BLA level, NEWS with lactate (NEWS-L) score, SOFA score including lactate level (SOFA-L) score, Intensive Care Unit (ICU) length of stay, total hospital stay, ICU stay/total hospital stay, and septic shock condition were compared between groups. Logistic regression analysis was performed to assess the impact of various predictive factors on prognosis and to plot the receiver operating characteristic (ROC) curve. The results suggested marked differences between Group S and Group D in terms of mean age (t = -5.620; OR = -9.96, 95 % CI: -13.44∼-6.47; P < 0.001). Group S showed drastic differences in terms of mode of hospital admission (χ2 = 9.618, P < 0.01), method of initial management (χ2 = 51.766, P < 0.001), use of emergency ventilator within 24 h of admission (χ2 = 98.564, P < 0.001), incidence of septic shock (χ2 = 77.545, P < 0.001), use of vasoactive drugs within 24 h of admission (χ2 = 102.453, P < 0.001), heart rate (t = -4.063, P < 0.001), respiratory rate (t = -4.758, P < 0.001), oxygenation status (χ2 = 20.547, P < 0.001), NEWS score (t = -6.120, P < 0.001), PaO2/PAO2 ratio (t = 2.625, P < 0.01), A-aDO2 value (Z = -3.581, P < 0.001), OI value (Z = -3.106, P < 0.01), PLT value (Z = -2.305, P < 0.05), SCr value (Z = -3.510, P < 0.001), BUN value (Z = -3.170, P < 0.01), D-dimer (Z = -4.621, P < 0.001), CRP level (Z = -4.057, P < 0.001), PCT value (Z = -2.783, P < 0.01), IL-6 level (Z = -2.904, P < 0.001), length of hospital stay (Z = -4.138, P < 0.001), total hospital stay (Z = -8.488, P < 0.001), CCU/total hospital stay (Z = -9.118, P < 0.001), NEWS score (t = -6.120, P < 0.001), SOFA score (t = -6.961, P < 0.001), SOFA-L score (Z = -4.609, P < 0.001), NEWS-L score (Z = -5.845, P < 0.001), BLA level (Z = -6.557, P < 0.001), and GCS score (Z = 6.909, P < 0.001) when compared to Group D. The use of ventilators, septic shock, PCT, NEWS score, GCS score, SOFA score, SOFA-L score, NEWS-L score, and BLA level were identified as independent risk factors for predicting the prognosis of sepsis patients (P < 0.001). The areas under ROC curve (AUC) of blood lactic acid, PCT, NEWS, NEWS-L, GCS, SOFA, and SOFA-L were 0.695, 0.665, 0.692, 0.698, 0.477, 0.700, and 0.653, respectively. These findings indicate that the combination of BLA with NEWS (NEWS-L) score and SOFA score has certain advantages in assessing the prognosis of sepsis.

Utilizing machine learning for early screening of thyroid nodules: a dual-center cross-sectional study in China.

Background: Thyroid nodules, increasingly prevalent globally, pose a risk of malignant transformation. Early screening is crucial for management, yet current models focus mainly on ultrasound features. This study explores machine learning for screening using demographic and biochemical indicators. Methods: Analyzing data from 6,102 individuals and 61 variables, we identified 17 key variables to construct models using six machine learning classifiers: Logistic Regression, SVM, Multilayer Perceptron, Random Forest, XGBoost, and LightGBM. Performance was evaluated by accuracy, precision, recall, F1 score, specificity, kappa statistic, and AUC, with internal and external validations assessing generalizability. Shapley values determined feature importance, and Decision Curve Analysis evaluated clinical benefits. Results: Random Forest showed the highest internal validation accuracy (78.3%) and AUC (89.1%). LightGBM demonstrated robust external validation performance. Key factors included age, gender, and urinary iodine levels, with significant clinical benefits at various thresholds. Clinical benefits were observed across various risk thresholds, particularly in ensemble models. Conclusion: Machine learning, particularly ensemble methods, accurately predicts thyroid nodule presence using demographic and biochemical data. This cost-effective strategy offers valuable insights for thyroid health management, aiding in early detection and potentially improving clinical outcomes. These findings enhance our understanding of the key predictors of thyroid nodules and underscore the potential of machine learning in public health applications for early disease screening and prevention.

[Study on Circulating Tumor Cell Detection System Based on Microfluidic Chip].

Objective: To achieve high throughput and high detection rate of circulating tumor cells (CTCs) in human peripheral blood, and to provide efficient and accurate early screening for cancer patients. Methods: A microfluidic chip with the integration of sorting, enrichment and detection was designed, and CTCs at the single cell level were detected by fluorescence detection system to obtain the number of CTCs in samples. Results: The peripheral blood samples after lysed red blood cells were used for 6 experiments. When the injection rate reached 0.2 mL/h, CTCs could reach the best detection rate of 78.6%, and the correlation coefficient within the group was above 0.8. Conclusion: CTCs detection system can achieve high detection rate and has good reliability, which can provide a reliable reference for clinical research in related fields.

Multi-risk factors joint prediction model for risk prediction of retinopathy of prematurity.

Purpose: Retinopathy of prematurity (ROP) is a retinal vascular proliferative disease common in low birth weight and premature infants and is one of the main causes of blindness in children.In the context of predictive, preventive and personalized medicine (PPPM/3PM), early screening, identification and treatment of ROP will directly contribute to improve patients' long-term visual prognosis and reduce the risk of blindness. Thus, our objective is to establish an artificial intelligence (AI) algorithm combined with clinical demographics to create a risk model for ROP including treatment-requiring retinopathy of prematurity (TR-ROP) infants. Methods: A total of 22,569 infants who underwent routine ROP screening in Shenzhen Eye Hospital from March 2003 to September 2023 were collected, including 3335 infants with ROP and 1234 infants with TR-ROP among ROP infants. Two machine learning methods of logistic regression and decision tree and a deep learning method of multi-layer perceptron were trained by using the relevant combination of risk factors such as birth weight (BW), gestational age (GA), gender, whether multiple births (MB) and mode of delivery (MD) to achieve the risk prediction of ROP and TR-ROP. We used five evaluation metrics to evaluate the performance of the risk prediction model. The area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUCPR) were the main measurement metrics. Results: In the risk prediction for ROP, the BW + GA demonstrated the optimal performance (mean ± SD, AUCPR: 0.4849 ± 0.0175, AUC: 0.8124 ± 0.0033). In the risk prediction of TR-ROP, reasonable performance can be achieved by using GA + BW + Gender + MD + MB (AUCPR: 0.2713 ± 0.0214, AUC: 0.8328 ± 0.0088). Conclusions: Combining risk factors with AI in screening programs for ROP could achieve risk prediction of ROP and TR-ROP, detect TR-ROP earlier and reduce the number of ROP examinations and unnecessary physiological stress in low-risk infants. Therefore, combining ROP-related biometric information with AI is a cost-effective strategy for predictive diagnostic, targeted prevention, and personalization of medical services in early screening and treatment of ROP.

Plasma metabolomics and lipidomics reveal potential novel biomarkers in early gastric cancer: An explorative study.

BACKGROUND: Early identification and therapy can significantly improve the outcome for gastric cancer. However, there is still no perfect biomarker available for the detection of early gastric cancer. This study aimed to investigate the alterations in the plasma metabolites of early gastric cancer using metabolomics and lipidomics based on high-performance liquid chromatography-mass spectrometry (HPLC-MS), which detected potential biomarkers that could be used for clinical diagnosis. METHODS: To investigate the changes in metabolomics and lipidomics, a total of 30 plasma samples were collected, consisting of 15 patients with early gastric cancer and 15 healthy controls. Extensive HPLC-MS-based untargeted metabolomic and lipidomic investigations were conducted. Differential metabolites and metabolic pathways were uncovered through the utilization of statistical analysis and bioinformatics analysis. Candidate biomarker screening was performed using support vector machine-based multivariate receiver operating characteristic analysis. RESULTS: A disturbance was observed in a combined total of 19 metabolites and 67 lipids of the early gastric cancer patients. The analysis of KEGG pathways showed that the early gastric cancer patients experienced disruptions in the arginine biosynthesis pathway, the pathway for alanine, aspartate, and glutamate metabolism, as well as the pathway for glyoxylate and dicarboxylate metabolism. Plasma metabolomics and lipidomics have identified multiple biomarker panels that can effectively differentiate early gastric cancer patients from healthy controls, exhibiting an area under the curve exceeding 0.9. CONCLUSION: These metabolites and lipids could potentially serve as biomarkers for the screening of early gastric cancer, thereby optimizing the strategy for the detection of early gastric cancer. The disrupted pathways implicated in early gastric cancer provide new clues for additional understanding of gastric cancer's pathogenesis. Nonetheless, large-scale clinical data are required to prove our findings.

Developing an optimal stratification model for colorectal cancer screening and reducing racial disparities in multi-center population-based studies.

BACKGROUND: Early detection of colorectal neoplasms can reduce the colorectal cancer (CRC) burden by timely intervention for high-risk individuals. However, effective risk prediction models are lacking for personalized CRC early screening in East Asian (EAS) population. We aimed to develop, validate, and optimize a comprehensive risk prediction model across all stages of the dynamic adenoma-carcinoma sequence in EAS population. METHODS: To develop precision risk-stratification and intervention strategies, we developed three trans-ancestry PRSs targeting colorectal neoplasms: (1) using 148 previously identified CRC risk loci (PRS148); (2) SNPs selection from large-scale meta-analysis data by clumping and thresholding (PRS183); (3) PRS-CSx, a Bayesian approach for genome-wide risk prediction (PRSGenomewide). Then, the performance of each PRS was assessed and validated in two independent cross-sectional screening sets, including 4600 patients with advanced colorectal neoplasm, 4495 patients with non-advanced adenoma, and 21,199 normal individuals from the ZJCRC (Zhejiang colorectal cancer set; EAS) and PLCO (the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial; European, EUR) studies. The optimal PRS was further incorporated with lifestyle factors to stratify individual risk and ultimately tested in the PLCO and UK Biobank prospective cohorts, totaling 350,013 participants. RESULTS: Three trans-ancestry PRSs achieved moderately improved predictive performance in EAS compared to EUR populations. Remarkably, the PRSs effectively facilitated a thorough risk assessment across all stages of the dynamic adenoma-carcinoma sequence. Among these models, PRS183 demonstrated the optimal discriminatory ability in both EAS and EUR validation datasets, particularly for individuals at risk of colorectal neoplasms. Using two large-scale and independent prospective cohorts, we further confirmed a significant dose-response effect of PRS183 on incident colorectal neoplasms. Incorporating PRS183 with lifestyle factors into a comprehensive strategy improves risk stratification and discriminatory accuracy compared to using PRS or lifestyle factors separately. This comprehensive risk-stratified model shows potential in addressing missed diagnoses in screening tests (best NPV = 0.93), while moderately reducing unnecessary screening (best PPV = 0.32). CONCLUSIONS: Our comprehensive risk-stratified model in population-based CRC screening trials represents a promising advancement in personalized risk assessment, facilitating tailored CRC screening in the EAS population. This approach enhances the transferability of PRSs across ancestries and thereby helps address health disparity.

Alzheimer's disease early screening and staged detection with plasma proteome using machine learning and convolutional neural network.

Alzheimer's disease (AD) stands as the prevalent progressive neurodegenerative disease, precipitating cognitive impairment and even memory loss. Amyloid biomarkers have been extensively used in the diagnosis of AD. However, amyloid proteins offer limited information about the disease process and accurate diagnosis depends on the presence of a substantial accumulation of amyloid deposition which significantly impedes the early screening of AD. In this study, we have combined plasma proteomics with an ensemble learning model (CatBoost) to develop a cost-effective and non-invasive diagnostic method for AD. A longitudinal panel has been identified that can serve as reliable biomarkers across the entire progression of AD. Simultaneously, we have developed a neural network algorithm that utilizes plasma proteins to detect stages of Alzheimer's disease. Based on the developed longitudinal panel, the CatBoost model achieved an area under the operating curve of at least 0.90 in distinguishing mild cognitive impairment from cognitively normal. The neural network model was utilized for the detection of three stages of AD, and the results demonstrated that the neural network model exhibited an accuracy as high as 0.83, surpassing that of the traditional machine learning model.

Global progress and future prospects of early gastric cancer screening.

Gastric cancer is a prevalent malignancy that poses a serious threat to global health. Despite advances in medical technologies, screening methods, and public awareness, gastric cancer remains a significant cause of morbidity and mortality worldwide. Early gastric cancer frequently does not present with characteristic symptoms, while advanced stage disease is characterized by a dismal prognosis. As such, early screening in gastric cancer is of great importance. In recent years, advances have been made globally in both clinical and basic research for the screening of early gastric cancer. The current predominant screening methods for early gastric cancer include imaging screening, endoscopic screening and serum biomarker screening. Imaging screening encompasses upper gastrointestinal barium meal, multidimensional spiral computed tomography (MDCT), Magnetic resonance imaging (MRI), and ultrasonography. Endoscopic screening methods include white light endoscopy, chromoendoscopy, computed virtual chromoendoscopy, and other endoscopic techniques like endocytoscopy, confocal laser endomicroscopy, optical coherence tomography and so on. Biomarkers screening involves the assessment of conventional biomarkers such as CEA, CA19-9 and CA72-4 as well as more emerging biomarkers such as peptides (PG, G-17, GCAA, TAAs and others), DNA (cfDNA, DNA methylation, MSI), noncoding RNA (miRNA, lncRNA, circRNA, and tsRNA) and others. Each screening method has its strengths and limitations. This article systematically summarizes worldwide progress and future development of early gastric cancer screening methods to provide new perspectives and approaches for early diagnostic and treatment advancements in gastric cancer worldwide.

Pancreatic cancer: from early detection to personalized treatment approaches.

Pancreatic cancer is notorious for its persistently poor prognosis and health outcomes, so some of the questions that may be begged are "Why is it mostly diagnosed at end stage?", "What could we possibly do with the advancing technology in today's world to detect early pancreatic cancer and intervene?", and "Are there any implementation of the existing novel imaging technologies?". Well, to start with, this is in part because the majority of patients presented would already have reached a locally advanced or metastatic stage at the time of diagnosis due to its highly aggressive characteristics and lack of symptoms. Due to this striking disparity in survival, advancements in early detection and intervention are likely to significantly increase patients' survival. Presently, screening is frequently used in high-risk individuals in order to obtain an early pancreatic cancer diagnosis. Having a thorough understanding of the pathogenesis and risk factors of pancreatic cancer may enable us to identify individuals at high risk, diagnose the disease early, and begin treatment promptly. In this review, the authors outline the clinical hurdles to early pancreatic cancer detection, describe high-risk populations, and discuss current screening initiatives for high-risk individuals. The ultimate goal of this current review is to study the roles of both traditional and novel imaging modalities for early pancreatic cancer detection. A lot of the novel imaging techniques mentioned seem promising, but they need to be put to the test on a large scale and may need to be combined with other non-invasive biomarkers before they can be widely used.

The upregulation and transcriptional regulatory mechanisms of Extra spindle pole bodies like 1 in bladder cancer: An immunohistochemistry and high-throughput screening Evaluation.

Background: This study aimed to explore the expression level and transcriptional regulation mechanism of Extra Spindle Pole Bodies Like 1 (ESPL1) in bladder cancer (BC). Methods: A multicentre database of samples (n = 1391) was assayed for ESPL1 mRNA expression in BC and validated at the protein level by immunohistochemical (IHC) staining of in-house samples (n = 202). Single-cell sequencing (scRNA-seq) analysis and enrichment analysis explored ESPL1 distribution and their accompanying molecular mechanisms. ATAC-seq, ChIP-seq and Hi-C data from multiple platforms were used to investigate ESPL1 upstream transcription factors (TFs) and potential epigenetic regulatory mechanisms. Immune-related analysis, drug sensitivity and molecular docking of ESPL1 were also calculated. Furthermore, upstream microRNAs and the binding sites of ESPL1 were predicted. The expression level and early screening efficacy of miR-299-5p in blood (n = 6625) and tissues (n = 537) were examined. Results: ESPL1 was significantly overexpressed at the mRNA level (p < 0.05, SMD = 0.75; 95 % CI = 0.09, 1.40), and IHC staining of in-house samples verified this finding (p < 0.0001). ESPL1 was predominantly distributed in BC epithelial cells. Coexpressed genes of ESPL1 were enriched in cell cycle-related signalling pathways, and ESPL1 might be involved in the communication between epithelial and residual cells in the Hippo, ErbB, PI3K-Akt and Ras signalling pathways. Three TFs (H2AZ, IRF5 and HIF1A) were detected upstream of ESPL1 and presence of promoter-super enhancer and promoter-typical enhancer loops. ESPL1 expression was correlated with various immune cell infiltration levels. ESPL1 expression might promote BC growth and affect the sensitivity and therapeutic efficacy of paclitaxel and gemcitabine in BC patients. As an upstream regulator of ESPL1, miR-299-5p expression was downregulated in both the blood and tissues, possessing great potential for early screening. Conclusions: ESPL1 expression was upregulated in BC and was mainly distributed in epithelial cells. Elevated ESPL1 expression was associated with TFs at the upstream transcription start site (TSS) and distant chromatin loops of regulatory elements. ESPL1 might be an immune-related predictive and diagnostic marker for BC, and the overexpression of ESPL1 played a cancer-promoting role and affected BC patients' sensitivity to drug therapy. miR-299-5p was downregulated in BC blood and tissues and was also expected to be a novel marker for early screening.

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

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

A Decision-Making Algorithm for Remote Digital Assessments of Alzheimer's Disease.

INTRODUCTION: Remote digital assessments (RDAs) such as voice recording, video and motor sensors, olfactory, hearing, and vision screenings are now starting to be employed to complement classical biomarker and clinical evidence to identify patients in the early AD stages. Choosing which RDA can be proposed to individual patients is not trivial and often time-consuming. This position paper presents a decision-making algorithm for using RDA during teleconsultations in memory clinic settings. METHOD: The algorithm was developed by an expert panel following the Delphi methodology. RESULTS: The decision-making algorithm is structured as a series of yes-no questions. The resulting questionnaire is freely available online. DISCUSSION: We suggest that the use of screening questionnaires in the context of memory clinics may help accelerating the adoption of RDA in everyday clinical practice.

Prevalence of Keratoconus in the Young Eastern Population of Saudi Arabia.

BACKGROUND: Keratoconus (KCN) is a progressive corneal ectasia that manifests at a young age and significantly impacts vision and quality of life. Early diagnosis allows for effective treatment with corneal collagen crosslinking, yet there is a lack of screening methods. This research aims to screen adolescents and young adults for this sight-threatening disease using quick corneal tomography mapping. METHODS: This prospective cross-sectional study is being conducted at Johns Hopkins Aramco Healthcare in Saudi Arabia, focusing on subjects aged 13-23. We are presenting the data from our study as internal pilot study data. Bilateral corneal imaging with Pentacam HR (Oculus, Wetzlar, Germany), utilizing Scheimpflug corneal tomography, was performed. Historical data on allergies, eye rubbing, KCN, family history, previous eye surgery, and contact lens use were collected. The Belin Ambrosio Enhanced Ectasia Display total D value served as an objective criterion for suspect KCN (SKCN) diagnosis. RESULTS: In this study with 110 participants, KCN was identified in 2.75% of participants and SKCN in 11.93%. Systemic allergies or eczema were reported by 2.80%, with no cases in the KCN or SKCN groups. Eye rubbing behavior was observed in 5.50%, with the highest prevalence (33.30%) in the KCN group. A family history of KCN was found in 21.10%, with SKCN having the highest prevalence (30.80%). CONCLUSION: This restricted population study reveals a significant KCN rate of 2.75%. The condition, easily detected and treatable with corneal collagen crosslinking, highlights the need for larger population studies to determine the disease's true prevalence. Efficient screening programs tailored to regional data are essential for early detection and intervention.

Screening preterm-born infants for autistic traits may help to identify social communication difficulties at five years of age.

AIM: This study compared neurodevelopmental screening questionnaires completed when preterm-born children reached 2 years of corrected age with social communication skills at 5.5 years of age. METHODS: Eligible subjects were born in 2011 at 24-34 weeks of gestation, participated in a French population-based epidemiological study and were free of motor and sensory impairment at 2 years of corrected age. The Ages and Stages Questionnaire (ASQ) and the Modified Checklist for Autism in Toddlers (M-CHAT) were used at 2 years and the Social Communication Questionnaire (SCQ) at 5.5 years of age. RESULTS: We focused on 2119 children. At 2 years of corrected age, the M-CHAT showed autistic traits in 20.7%, 18.5% and 18.2% of the children born at 24-26, 27-31 and 32-34 weeks of gestation, respectively (p = 0.7). At 5.5 years of age, 12.6%, 12.7% and 9.6% risked social communication difficulties, with an SCQ score ≥90th percentile (p = 0.2). A positive M-CHAT score at 2 years was associated with higher risks of social communication difficulties at 5.5 years of age (odds ratio 3.46, 95% confidence interval 2.04-5.86, p < 0.001). Stratifying ASQ scores produced similar results. CONCLUSION: Using parental neurodevelopmental screening questionnaires for preterm-born children helped to identify the risk of later social communication difficulties.

Incidence, Prevalence, Risk Factors, and Clinical Treatment for Children with Developmental Dysplasia of the Hip in Saudi Arabia. A Systematic Review.

BACKGROUND: Developmental dysplasia of the hip (DDH) leads to pain, joint instability, and early degenerative joint disease. Incidence, prevalence, and management strategies of DDH have been well-documented in several countries, but not in Saudi Arabia. OBJECTIVE: We synthesized the current evidence regarding incidence, prevalence, risk factors, and clinical treatment for children with DDH in Saudi Arabia. METHODS: We searched 3 databases to locate studies. Studies that included children with DDH in Saudi Arabia; reported either incidence rate, prevalence, risk factors, and/or clinical practice; and were available in English or Arabic were included. We excluded reviews, case studies, or animal studies. Two independent authors reviewed potential studies and assessed study's quality. RESULTS: Our search yielded 67 potential studies, of which 16 studies were included (total DDH sample = 3,127; age range = 2.5 to 86.4 months). Three studies reported incidence rates ranging from 3.1 to 4.9 per 1000 births, and 3 studies reported prevalence ranging from 6 to 78%. Nine studies reported that female sex, breech position, family history, and age less than 3 years were risk factors associated with DDH. Four studies reported that brace applications and closed reduction were conservative treatments, and 9 studies reported that open hip reduction, adductor tenotomy, and/or pelvic osteotomy were surgical approaches to treat DDH. CONCLUSIONS: In Saudi Arabia, the Incidence and prevalence rates of DDH are 3.1 to 4.9 per 1,000 births, and 6-78%, respectively (differ from what has been reported in other countries), but the risk factors of DDH in Saudi Arabia appear to be similar in comparison to other countries (female, breech presentation, family history of DDH).

Exploring the application and future outlook of Artificial intelligence in pancreatic cancer.

Pancreatic cancer, an exceptionally malignant tumor of the digestive system, presents a challenge due to its lack of typical early symptoms and highly invasive nature. The majority of pancreatic cancer patients are diagnosed when curative surgical resection is no longer possible, resulting in a poor overall prognosis. In recent years, the rapid progress of Artificial intelligence (AI) in the medical field has led to the extensive utilization of machine learning and deep learning as the prevailing approaches. Various models based on AI technology have been employed in the early screening, diagnosis, treatment, and prognostic prediction of pancreatic cancer patients. Furthermore, the development and application of three-dimensional visualization and augmented reality navigation techniques have also found their way into pancreatic cancer surgery. This article provides a concise summary of the current state of AI technology in pancreatic cancer and offers a promising outlook for its future applications.

Multiscale Structured Trimetal Oxide Heterojunctions for Urinary Metabolic Phenotype-Dependent Screening of Early and Small Hepatocellular Carcinoma.

Developing a standardized screening tool for the detection of early and small hepatocellular carcinoma (HCC) through urinary metabolic analysis poses a challenging yet intriguing research endeavor. In this study, a range of intricately interlaced 2D rough nanosheets featuring well-defined sharp edges is fabricated, with the aim of constructing diverse trimetal oxide heterojunctions exhibiting multiscale structures. By carefully engineering synergistic effects in composition and structure, including improved adsorption, diffusion, and other surface-driven processes, the optimized heterojunctions demonstrate a substantial enhancement in signal intensity compared to monometallic or bimetallic oxides, as well as fragmented trimetallic oxides. Additionally, optimal heterojunctions enable the extraction of high-quality urinary metabolic fingerprints using high-throughput mass spectrometry. Leveraging machine learning, discrimination of HCC patients from high-risk and healthy populations achieves impressive performance, with area under the curve values of 0.940 and 0.916 for receiver operating characteristic and precision-recall curves, respectively. Six crucial metabolites are identified, enabling accurate detection of early, small-tumor, alpha-fetoprotein-negative HCC (93.3%-97.3%). A comprehensive screening strategy tailored to clinical reality yields precision metrics (accuracy, precision, recall, and F1 score) exceeding 95.0%. This study advances the application of cutting-edge matrices-based metabolic phenotyping in practical clinical diagnostics.

Glucagon-like peptide-1 receptor agonists as a possible intervention to delay the onset of type 1 diabetes: A new horizon.

Type 1 diabetes (T1D) is a chronic autoimmune condition that destroys insulin-producing beta cells in the pancreas, leading to insulin deficiency and hyper-glycemia. The management of T1D primarily focuses on exogenous insulin replacement to control blood glucose levels. However, this approach does not address the underlying autoimmune process or prevent the progressive loss of beta cells. Recent research has explored the potential of glucagon-like peptide-1 receptor agonists (GLP-1RAs) as a novel intervention to modify the disease course and delay the onset of T1D. GLP-1RAs are medications initially developed for treating type 2 diabetes. They exert their effects by enhancing glucose-dependent insulin secretion, suppressing glucagon secretion, and slowing gastric emptying. Emerging evidence suggests that GLP-1RAs may also benefit the treatment of newly diagnosed patients with T1D. This article aims to highlight the potential of GLP-1RAs as an intervention to delay the onset of T1D, possibly through their potential immunomodulatory and anti-inflammatory effects and preservation of beta-cells. This article aims to explore the potential of shifting the paradigm of T1D management from reactive insulin replacement to proactive disease modification, which should open new avenues for preventing and treating T1D, improving the quality of life and long-term outcomes for individuals at risk of T1D.

Global frailty screening tools: Review and application of frailty screening tools from 2001 to 2023.

As the aging population increases globally, health-related issues caused by frailty are gradually coming to light and have become a global health priority. Frailty leads to a significantly increased risk of falls, incapacitation, and death. Early screening leads to better prevention and management of frailty, increasing the possibility of reversing it. Developing assessment tools by incorporating disease states of older adults using effective interventions has become the most effective approach for preventing and controlling frailty. The most direct and effective tool for evaluating debilitating conditions is a frailty screening tool, but because there is no globally recognized gold standard, every country has its own scale for national use. The diversity and usefulness of the frailty screening tool has become a hot topic worldwide. In this article, we reviewed the frailty screening tool published worldwide from January 2001 to June 2023. We focused on several commonly used frailty screening tools. A systematic search was conducted using PubMed database, and the commonly used frailty screening tools were found to be translated and validated in many countries. Disease-specific scales were also selected to fit the disease. Each of the current frailty screening tools are used in different clinical situations, and therefore, the clinical practice applications of these frailty screening tools are summarized graphically to provide the most intuitive screening and reference for clinical practitioners. The frailty screening tools were categorized as (ⅰ) Global Frailty Screening Tools in Common; (ⅱ) Frailty Screening Tools in various countries; (ⅲ) Frailty Screening Tools for various diseases. As science and technology continue to advance, electronic frailty assessment tools have been developed and utilized. In the context of Coronavirus disease 2019 (COVID-19), electronic frailty assessment tools played an important role. This review compares the currently used frailty screenings tools, with a view to enable quick selection of the appropriate scale. However, further improvement and justification of each tool is needed to guide clinical practitioners to make better decisions.

Handwriting Markers for the Onset of Alzheimer's Disease.

INTRODUCTION: Alzheimer's disease has an impact on handwriting (AD). Numerous researchers reported that fact. Therefore, examining handwriting characteristics could be a useful way to screen for AD. The aim of the article is to present the reliability and effectiveness of the AD-HS tool. METHOD: Most of the existing studies examine either linguistic manifestations of writing or certain motor functions. However, handwriting is a complex of cognitive and motor activities. Since the influence of AD on handwriting is individual, it is important to analyze the complete set of handwriting features. The AD-HS instrument is based on this principle. Validation of the AD-HS instrument for revealing cognitive impairment in AD-diagnosed persons in comparison to the control group. The study is based on the evaluation of free handwritten texts. AD-HS includes 40 handwriting and 2 linguistic features of handwritten texts. It is based on the standard protocol for handwriting analysis. The cumulative evaluation of all features builds a quantitative AD-Indicator (ADI) as a marker of possible AD conditions. The analyzed experiment includes 53 AD-diagnosed persons and a control group of 192 handwriting specimens from the existing database. RESULT: AD-HS shows a distinct difference in evaluated ADI for the participants (the mean value equals 0.49) and the control group (the mean value equals 0.28). CONCLUSION: The handwriting marker of AD could be an effective supplement instrument for earlier screening. It is also useful when traditional biomarkers and neurological tests could not be applied. AD-HS can accompany therapy as an indication of its effect on a person.