Early Effective Treatment Research Articles

The microbiome and ovarian cancer: insights, implications, and therapeutic opportunities

Ovarian cancer is the leading cause of gynecologic cancer death in the United States. Most ovarian cancer patients are diagnosed with advanced-stage disease, which poses a challenge for early detection and effective treatment. At present, cytoreductive surgery and platinum-based chemotherapy are foundational for patients with newly diagnosed ovarian cancer, but unfortunately, most patients will recur and die of their disease. Therefore, there is a significant need to seek innovative, novel approaches for early detection and to overcome chemoresistance for ovarian cancer patients. The microbiome, comprising diverse microbial communities inhabiting various body sites, is vital in maintaining human health. Changes to the diversity and composition of the microbial communities impact the microbiota-host relationship and are linked to diseases, including cancer. The microbiome contributes to carcinogenesis through various mechanisms, including altered host immune response, modulation of DNA repair, upregulation of pro-inflammatory pathways, altered gene expression, and dysregulated estrogen metabolism. Translational and clinical studies have demonstrated that specific microbes contribute to ovarian cancer development and impact chemotherapy’s efficacy. The microbiome is malleable and can be altered through different approaches, including diet, exercise, medications, and fecal microbiota transplantation. This review provides an overview of the current literature regarding ovarian cancer and the microbiome of female reproductive and gastrointestinal tracts, focusing on mechanisms of carcinogenesis and options for modulating the microbiota for cancer prevention and treatment. Advancing our understanding of the complex relationship between the microbiome and ovarian cancer may provide a novel approach for prevention and therapeutic modulation in the future.

Equity in Maths: Mechanisms Underlying the Sharpening of the Gender Gap

Given that early maths skills are crucial predictors of later academic success, the results from the latest maths SIMCE (System for Measurement of Quality in Education) are cause for alarm. These results reveal a concerning setback in gender equality in mathematical learning, particularly affecting girls from low socioeconomic status (SES) backgrounds. Neuroscience indicates that both men and women share a similar biological basis for mathematical learning. Hence, the observed gender gap may be attributed to social beliefs associating mathematics more with masculinity than femininity. In Chile, studies have demonstrated that these beliefs are prevalent among teachers, families, and children as young as 5 years old, even more so than in other countries. It is also evident that these beliefs vary based on gender and SES. To design effective early interventions, it is crucial to understand the nature of these beliefs through genuine listening and active participation of children in research studies.

Visualizing Dipeptidyl Peptidase-IV with an Advanced Non-π-Conjugated Fluorescent Probe for Early Thyroid Disease Diagnosis.

Early detection and effective treatment of thyroid cancer are vital due to the aggressiveness and high mortality rate of the cancer. Nevertheless, the exploration of dipeptidyl peptidase-IV (DPP-IV) as a biomarker for thyroid diseases has not been widely conducted. In this study, we developed a novel non-π-conjugated near-infrared fluorescent probe, MB-DPP4, specifically designed to visualize and detect endogenous DPP-IV. Traditional DPP-IV-specific fluorescent probes rely primarily on the intramolecular charge transfer mechanism. For this reason, these probes are often hampered by high background levels that can inhibit their ability to achieve a fluorescence turn-on effect. MB-DPP4 successfully surmounts several drawbacks of traditional DPP-IV probes, boasting unique features such as exceptional selectivity, ultrahigh sensitivity (0.29 ng/mL), innovative structure, low background, and long-wavelength fluorescence. MB-DPP4 is an "off-on" chemosensor that exhibits strong fluorescence at 715 nm and releases a methylene blue (MB) fluorophore upon interacting with DPP-IV, resulting in a visible color change from colorless to blue. Given these remarkable attributes, MB-DPP4 shows great promise as a versatile tool for advancing research on biological processes and for evaluating the physiological roles of DPP-IV in living systems. Finally, we conducted a comprehensive investigation of DPP-IV expression in human serum, urine, thyroid cells, and mouse thyroid tumor models. Our findings could potentially establish a foundation for the early diagnosis and treatment of thyroid diseases.

OGC P59 Health Inequalities in oesophago-gastric (OG) cancer in a Single Tertiary Centre

Abstract Background This abstract presents the findings of a study investigating health inequality in oesophago-gastric (OG) cancer throughout Greater Manchester (GM). The study aimed to assess the impact of socio-economic deprivation on cancer incidence, clinical stage at diagnosis and treatment intent. Methods The study reviewed MDT minutes from 2022 for patients newly diagnosed with oesophageal or gastric cancer. Treatment intent at diagnosis, patient age, and deprivation scores based on national data were examined. Patients were categorised into quintile (Groups 1 to 5) based on their Index of Multiple Deprivation (IMD) score from their postcode. Results 674 patients were included from a population of 3.5 million. Stages 1-2 accounted for only 21% of cases and stage 4 accounted for 57% of cases. 31.6% of all cases occurred in the MD (p=.565). Among all cancers, 9% more patients who had palliative disease resided in the MD (12% vs 21%, p=.565). There were 7.2% more stage 4 cancer in the MD (10.2% vs 17.4%, p=.865). Men were proportionally more represented in stage 4 disease compared to stage 1 (65.5% vs. 72.7%, p=.155). Patients in the MD had earlier onset of disease (median 70 years vs. 75 years, p=.051). Conclusions This retrospective study highlights important trends that warrant further research. It identifies health inequalities in OG cancer, particularly in terms of age at diagnosis, incidence rates in deprived areas, and gender disparities. The study underscores the urgency for targeted interventions, public awareness campaigns, and resource allocation to address these disparities, alleviate the burden of advanced-stage OG cancer, and improve access to early diagnosis and effective treatment in an equitable manner. The findings contribute to the growing body of evidence on health inequality in cancer, however further research is required.

OGC P43 Is surgery (gastrectomy) alone more effective than surgery combined with adjuvant chemotherapy for the treatment of Stage I-III gastric adenocarcinoma?

Abstract Background Gastric adenocarcinoma, the malignant proliferation of glandular cells in stomach epithelium, is a type of gastric cancer with statistical disease-burden of the 5th most common global and 17th most common cancer in the UK. Gastric adenocarcinoma at different stages (Stage I-IV) present with different prognoses. Particularly promising subset is patients with Stage I-III gastric adenocarcinoma, presenting with 5-year survival rates up to 71.8%, warranting early diagnosis and effective treatment can be vital. There are several treatment modalities, currently surgery being gold-standard. However, understanding pathological nature of tumour development, combination treatments, such as adjuvant chemotherapy after surgery can maximise patient outcomes. Methods The study aimed to investigate patients with Stage I-III (non-advanced) gastric adenocarcinoma, in understanding whether adjuvant chemotherapy combined with surgery provides (1) greater disease-free/disease-specific/cause-specific survival, (2) overall survival, (3) decreased recurrence rates or improved recurrence-free survival, compared to surgery alone. 17 (3 randomised-control, 10 cohort, 4 case-control) English-language, full-text, Level I-III, peer-reviewed studies from MEDLINE and Embase databases, in past 10 years were reviewed. No age/sex/ethnicity/country restrictions applied and dimension of interest limited to Stage I-III gastric adenocarcinoma patients who had undergone tumour resection via surgery, and received chemotherapy as the only adjuvant therapy. Results 7/17 (41.2%) of studies have at least one statistical outcome measure (DFS/OS/RFS, measured by p-values and HR/OR) that supports, with statistical significance, that adjuvant chemotherapy combined with surgery has benefit over surgery alone for the treatment of Stage I-III gastric adenocarcinoma. Majority of studies, 10/17 (58.8%) do not show statistically significant benefit of adjuvant chemotherapy. Conclusions In conclusion, findings of this study are limited by sample size and bias present in individual studies reviewed. Thus, findings are different when compared to prior large-scale meta-analyses and ongoing research is needed in the field (with consideration to advances in interventions i.e. surgical technique and new chemotherapeutic combinations) to fully understand whether adjuvant chemotherapy is effective when combined with surgery for treatment of gastric adenocarcinoma.

EEG-Based Effective Connectivity Analysis for Attention Deficit Hyperactivity Disorder Detection Using Color-Coded Granger-Causality Images and Custom Convolutional Neural Network

Background: Attention deficit hyperactivity disorder (ADHD) is prevalent worldwide, affecting approximately 8-12% of children. Early detection and effective treatment of ADHD are crucial for improving academic, social, and emotional outcomes. Despite numerous studies on ADHD detection, existing models still lack accuracy distinguishing between ADHD and healthy control (HC) children. Methods: This study introduces an innovative methodology that utilizes granger causality (GC), a well-established brain connectivity analysis technique, to reduce the required EEG electrodes. We computed GC indexes (GCI) for the entire brain and specific brain regions, known as regional GCI, across different frequency bands. Subsequently, these GCIs were transformed into color-coded images and fed into a custom-developed 11-layer convolutional neural network. Results: The proposed model is evaluated through a five-fold cross-validation, achieving the highest accuracy of 99.80% in the gamma frequency band for the entire brain and an accuracy of 98.50% in distinguishing the theta frequency band of the right hemisphere of ADHD and HC children by only using eight electrodes. Conclusion: The proposed framework provides a powerful automated tool for accurately classifying ADHD and HC children. The study’s outcome demonstrates that the innovative proposed methodology utilizing GCI and a custom-developed convolutional neural network can significantly improve ADHD detection accuracy, improving affected children’s overall quality of life.

Two Rare Mutations in Cases of Late-Onset cblC type

These cases suggest that even if combined methylmalonic aciduria with homocystinuria is a rare entity, its identification is essential so as to allow the initiation of an effective early treatment to avoid the pathophysiological progression and the serious clinical consequences of this disease, even in cases of late-onset cblC

ARID3A and ARID3B exert direct regulatory control over the long non-coding RNAs (lncRNAs) MALAT1 and NORAD within the context of non-small cell lung cancer (NSCLC)

Lung cancer, known for its high mortality rates and poor prognosis, remains one of the most prevalent cancer types. Early detection and effective treatment methods are crucial for improving survival rates. Non-small cell lung cancer (NSCLC) accounts for approximately 85 % of all lung cancer cases. Long non-coding RNAs (lncRNAs), which play vital roles in various biological processes, have been implicated in the development of cancer and can impact key therapeutic targets in different cancer types. In NSCLC, the dysregulation of specific lncRNAs, such as MALAT1 and NORAD, has been associated with neoplastic initiation, progression, metastasis, tumor angiogenesis, chemoresistance, and genomic instability. Both MALAT1 and NORAD directly regulate the expression of the transcription factor E2F1, thereby influencing cell cycle progression. Additionally, MALAT1 has been reported to affect the expression of p53 target genes, leading to cell cycle progression through the repression of p53 promoter activity. NORAD, on the other hand, is indirectly regulated by p53. The AT-rich interaction domain (ARID) family of DNA-binding proteins, particularly ARID3A and ARID3B, are involved in various biological processes such as cell proliferation, differentiation, and development. They also play significant roles in E2F-dependent transcription and are transcriptional targets of p53. The intricate balance between promoting cellular proliferation through the pRB-E2F pathway and inducing growth arrest through the p53 pathway underscores the crucial regulatory role of ARID3A, ARID3B, and their interaction with lncRNAs MALAT1 and NORAD. In this study, we aimed to investigate the potential interactive and functional connections among ARID3A, ARID3B, MALAT1, and NORAD in NSCLC, considering their involvement in the pRB-E2F and p53 pathways. Our findings strongly suggest that ARID3A and ARID3B play a regulatory role in controlling MALAT1 and NORAD in NSCLC. Specifically, our study demonstrates that the activities of MALAT1 and NORAD were markedly increased upon the overexpression of ARID3A and ARID3B. Therefore, we can conclude that ARID3A and ARID3B likely contribute significantly to the oncogenic functions of MALAT1 and NORAD in NSCLC. Consequently, targeting ARID3A and ARID3B could hold promise as a therapeutic approach in NSCLC, given their direct control over the expression of MALAT1 and NORAD.

Assessment of the impact of implementation research on the Visceral Leishmaniasis (VL) elimination efforts in Nepal.

Nepal, Bangladesh, and India signed a Memorandum of Understanding (MoU) in 2005 to eliminate visceral leishmaniasis (VL) as a public health problem from the Indian subcontinent by 2015. By 2021, the number of reported VL cases in these countries had declined by over 95% compared to 2007. This dramatic success was achieved through an elimination programme that implemented early case detection and effective treatment, vector control, disease surveillance, community participation, and operational research that underpinned these strategies. The experience offered an opportunity to assess the contribution of implementation research (IR) to VL elimination in Nepal. Desk review and a stakeholder workshop was conducted to analyse the relationship between key research outputs, major strategic decisions in the national VL elimination programme, and annual number of reported new cases over time between 2005 and 2023. The results indicated that the key decisions across the strategic elements, throughout the course of the elimination programme (such as on the most appropriate tools for diganostics and treatment, and on best strategies for case finding and vector management), were IR informed. IR itself responded dynamically to changes that resulted from interventions, addressing new questions that emerged from the field. Close collaboration between researchers, programme managers, and implementers in priority setting, design, conduct, and review of studies facilitated uptake of evidence into policy and programmatic activities. VL case numbers in Nepal are now reduced by 90% compared to 2005. Although direct attribution of disease decline to research outputs is difficult to establish, the Nepal experience demonstrates that IR can be a critical enabler for disease elimination. The lessons can potentially inform IR strategies in other countries with diseases targeted for elimination.

Single-session intervention on growth mindset on negative emotions for university student mental health (U-SIGMA): a protocol of two-armed randomized controlled trial

BackgroundThe university years are a developmentally crucial phase and a peak period for the onset of mental disorders. The beliefs about the changeability of negative emotion may play an important role in help-seeking. The brief digital growth mindset intervention is potentially scalable and acceptable to enhance adaptive coping and help-seeking for mental health needs in university students. We adapted the Single-session Intervention on Growth Mindset for adolescents (SIGMA) to be applied in university students (U-SIGMA). This protocol introduces a two-armed waitlist randomized controlled trial study to examine the effectiveness and acceptability of U-SIGMA in promoting help-seeking among university students in the Greater Bay Area.MethodsUniversity students (N = 250, ages 18–25) from universities in the Greater Bay Area will be randomized to either the brief digital growth mindset intervention group or the waitlist control group. Participants will report on the mindsets of negative emotions, perceived control over anxiety, attitude toward help-seeking, physical activity, hopelessness, psychological well-being, depression, anxiety, and perceived stress at baseline and the 2-week and 8-week follow-ups through web-based surveys. A 30-min digital intervention will be implemented in the intervention group, with a pre- and post-intervention survey collecting intervention feedback, while the control group will receive the link for intervention after 8 weeks.DiscussionThis protocol introduces the implementation plan of U-SIMGA in multi-cities of the Greater Bay Area. The findings are expected to help provide pioneer evidence for the effectiveness and acceptability of the brief digital intervention for university students in the Chinese context and beyond and contribute to the development of accessible and effective prevention and early intervention for university students’ mental health.Trial registrationHKU Clinical Trials Registry: HKUCTR-3012; Registered 14 April 2023. http://www.hkuctr.com/Study/Show/7a3ffbc0e03f4d1eac0525450fc5187e.

A prediction model for acute respiratory distress syndrome in immunocompetent adults with adenovirus-associated Pneumonia: a multicenter retrospective analysis

BackgroundIn recent years, the number of human adenovirus (HAdV)-related pneumonia cases has increased in immunocompetent adults. Acute respiratory distress syndrome (ARDS) in these patients is the predominant cause of HADV-associated fatality rates. This study aimed to identify early risk factors to predict early HAdV-related ARDS.MethodsData from immunocompetent adults with HAdV pneumonia between June 2018 and May 2022 in ten tertiary general hospitals in central China was analyzed retrospectively. Patients were categorized into the ARDS group based on the Berlin definition. The prediction model of HAdV-related ARDS was developed using multivariate stepwise logistic regression and visualized using a nomogram.ResultsOf 102 patients with adenovirus pneumonia, 41 (40.2%) developed ARDS. Overall, most patients were male (94.1%), the median age was 38.0 years. Multivariate logistic regression showed that dyspnea, SOFA (Sequential Organ Failure Assessment) score, lactate dehydrogenase (LDH) and mechanical ventilation status were independent risk factors for this development, which has a high mortality rate (41.5%). Incorporating these factors, we established a nomogram with good concordance statistics of 0.904 (95% CI 0.844–0.963) which may help to predict early HAdV-related ARDS.ConclusionA nomogram with good accuracy in the early prediction of ARDS in patients with HAdV-associated pneumonia may could contribute to the early management and effective treatment of severe HAdV infection.

An ultrasensitive photoelectrochemical immunosensor for carcinoembryonic antigen detection based on cobalt borate nanosheet array and Mn2O3@Au nanocube

Highly sensitive and selective method of determination for carcinoembryonic antigen (CEA) is of important significance for the effective early diagnosis and treatment of cancer. In this work, we report an ultrasensitive sandwich photoelectrochemical (PEC) immunosensor for CEA detection. To be precise, cobalt-borate nanosheet arrays (Co-Bi NSs) with the larger specific surface area are prepared by electrodeposition and used as the basal material to capture the primary antibody (Ab1) for the first time. And the other photoactive material Mn2O3@Au nanocube is exploited for labeling the secondary antibody (Ab2). The coulomb force of Mn2O3@Au nanocube and Co-Bi NSs photoelectrodes as the driving force effectively improves the photoelectric response. Under the light irradiation, charge separation occurs at the same time between AuNPs and Mn2O3, and the energy levels of Mn2O3@Au and Co-Bi NSs are effectively matched, achieving the high PEC performance of the biosensor. The synthesized nanomaterials are physically characterized by a series of characterization techniques. Besides, ascorbic acid (AA) is employed as an excellent electron donor, which can inhibit the recombination process of photogenerated electrons and holes, then obtaining enhanced and stable photocurrent signals. Under the optimal experimental conditions, the immunosensor shows a lower detection limit of 5.0 pg mL−1 (S/N = 3), a wider linear range of 0.1 ng mL−1 ∼ 1000 ng mL−1, as well as excellent specificity, stability and reproducibility. Additionally, as-constructed PEC immunosensor can be applied to detect CEA in human serum, this proposed strategy may afford a promising approach for the biomarker sensing and clinical applications.

Nanomedicines for the management of diabetic nephropathy: present progress and prospects.

Diabetic nephropathy (DN) is a serious microvascular consequence of diabetes mellitus (DM), posing an encumbrance to public health worldwide. Control over the onset and progress of DN depend heavily on early detection and effective treatment. DN is a major contributor to end-stage renal disease, and a complete cure is yet to be achieved with currently available options. Though some therapeutic molecules have exhibited promise in treating DN complications, their poor solubility profile, low bioavailability, poor permeation, high therapeutic dose and associated toxicity, and low patient compliance apprehend their clinical usefulness. Recent research has indicated nano-systems as potential theranostic platforms displaying futuristic promise in the diagnosis and treatment of DN. Early and accurate diagnosis, site-specific delivery and retention by virtue of ligand conjugation, and improved pharmacokinetic profile are amongst the major advantages of nano-platforms, defining their superiority. Thus, the emergence of nanoparticles has offered fresh approaches to the possible diagnostic and therapeutic strategies regarding DN. The present review corroborates an updated overview of different types of nanocarriers regarding potential approaches for the diagnosis and therapy of DN.

Application and development of nanomaterials in the diagnosis and treatment of esophageal cancer.

Esophageal cancer is a malignant tumor with a high incidence worldwide. Currently, there are a lack of effective early diagnosis and treatment methods for esophageal cancer. However, delivery systems based on nanoparticles (NPs) have shown ideal efficacy in real-time imaging and chemotherapy, radiotherapy, gene therapy, and phototherapy for tumors, which has led to their recent widespread design as novel treatment strategies. Compared to traditional drugs, nanomedicine has unique advantages, including strong targeting ability, high bioavailability, and minimal side effects. This article provides an overview of the application of NPs in the diagnosis and treatment of esophageal cancer and provides a reference for future research.

Generic hybrid model for breast cancer mammography image classification using EfficientNetB2

Breast cancer is a global health issue that necessitates precise classification for early detection and effective treatment. In recent years, pre-trained models have shown great potential in the field of medical image classification, including breast cancer classification. These models have been trained on extensive datasets, and they possess the ability to capture intricate features and patterns within medical images, facilitating accurate classification. However, some of the models are non-generic. They can be sensitive to dataset biases, leading to over fitting on specific patterns present in the training data, and they equally struggle to handle data from different distributions. In this work, we proposed a generic hybrid model for image classification. The features were extracted from two datasets: the mammographic image analysis society (MIAS) and the INbreast dataset, respectively, through the pre trained EfficientNetB2 architecture. However, three classifiers were used in the image classification of the extracted features: MGSVM, CUBIC SVM, and XGBOOST. Eight evaluation metrics were selected to assess the performance of the proposed models. These metrics include accuracy, precision, F1-score, AUC, sensitivity, false negative rate (FNR), Kappa score, and time complexity. Experimental results show that the hybrid of EfficientNetB2 and the MGSVM classifier is more generic and efficient for breast cancer diagnosis and classification. It exhibits a strong performance when classifying mammography breast images from both datasets, achieving impressive metrics such as an overall accuracy of 99.47%, a sensitivity rate of 99.31%, precision of 99.44%, F1-score of 99.44%, AUC of 99.44%, a low FNR (False Negative Rate) of 0.007, a kappa score of 0.98, and a manageable time complexity of 231.44 seconds on the MIAS Dataset.

Untargeted and spatial-resolved metabolomics characterize serum and tissue-specific metabolic reprogramming in acute kidney injury

BackgroundAcute kidney injury (AKI) is one of the most common clinical emergencies characterized by rapid progression, difficulty in early diagnosis, and high mortality. Currently, there are no effective AKI early diagnostic methods and treatments. Therefore, identifying new mechanisms of AKI have become urgent for development new targets for early diagnosis and treatment of AKI in the current clinical setting. MethodsIn this study, systematic analysis and comparison of serum metabolic profiles of clinical AKI patients, chronic kidney disease (CKD) patients, and healthy subjects were performed using untargeted metabolomics. Moreover, the first spatial metabolomic analysis of kidney tissues in an AKI mouse model using MALDI-TOF MS technology was conducted. Differentially expressed metabolites were identified using a comprehensive, publicly available database. The metabolic data obtained were evaluated using principal component analysis, (orthogonal) partial least squares discriminant analysis, and metabolic pathway analysis to explore the unique serum metabolic profile of the patients, as well as to characterize the spatial distribution of differential metabolites in the kidneys of AKI mice. ResultsSignificant changes in the metabolite levels of amino acids, carnitine, and lipids were observed in the AKI and CKD groups versus the healthy population, suggesting that kidney injury may lead to abnormalities in various metabolic pathways, such as amino acids, fatty acids, and lipids. The significant difference between the AKI and CKD groups were found for the first time in these indexes including amino acid, carnitine, fatty acid, and lipid levels. Additionally, spatial metabolomics results revealed that amino acid, carnitine, organic acid, and fatty acid metabolites were more likely significantly altered in the renal cortex, while lipid metabolites were both differentially distributed in the cortex and medulla of the AKI group. ConclusionAbnormalities in the serum metabolism of amino acids, carnitine, and lipids in patients with kidney diseases, such as AKI and CKD, are closely associated with the physiological dysfunction of kidney injury. Metabolic differences between patients with AKI and CKD were compared for the first time, showing that fatty acid oxidative inhibition was more severe in patients with AKI. Furthermore, spatial metabolomics has revealed metabolic reprogramming with tissue heterogeneity in AKI mice model. Our study provides valuable information in the molecular pathological features of AKI in the kidney tissues.

Kawasaki Disease Associated Acute Abdomen: Most Require No Surgery.

To summarize the clinical features and treatment experiences of patients with Kawasaki disease (KD)-associated acute abdomen (KD-AA). We conducted a retrospective case-control study of patients with KD-AA treated at our hospital between January 2006 and November 2022. Of the 917 children with KD, 43 (4.7%) presented with AA. Of these, 33 with complete information were included in the KD-AA group. Patients with KD-AA were significantly older, with higher neutrophil rate, C-reactive protein, procalcitonin, and alanine transaminase levels and lower hemoglobin, albumin, and serum sodium levels. Additionally, more patients with KD-AA presented with aseptic meningitis and KD shock syndrome than those with KD alone (all p<0.05). The two groups did not differ in the incidence of intravenous immunoglobulin (IVIG) resistance, incomplete KD, or coronary artery abnormalities. All patients received aspirin and IVIG therapy, with nine receiving a second dose of IVIG and 11 receiving corticosteroids. Only two patients with KD-AA underwent surgery, and the prognosis of all patients with KD-AA was good. KD-AA should be suspected in febrile children with abdominal symptoms. Prompt diagnosis of KD-AA is important for early effective treatment to avoid unnecessary surgical harm. KD, complicated by acute abdomen, has a good prognosis.

Fast Parabolic Fitting: An R-Peak Detection Algorithm for Wearable ECG Devices.

Heart diseases rank among the most fatal health concerns globally, with the majority being preventable through early diagnosis and effective treatment. Electrocardiogram (ECG) analysis is critical in detecting heart diseases, as it captures the heart's electrical activities. For continuous monitoring, wearable electrocardiographic devices must ensure user comfort over extended periods, typically 24 to 48 h. These devices demand specialized algorithms with low computational complexity to accommodate memory and power consumption constraints. One of the most crucial aspects of ECG signals is accurately detecting heartbeat intervals, specifically the R peaks. In this study, we introduce a novel algorithm designed for wearable devices, offering two primary attributes: robustness against noise and low computational complexity. Our algorithm entails fitting a least-squares parabola to the ECG signal and adaptively shaping it as it sweeps through the signal. Notably, our proposed algorithm eliminates the need for band-pass filters, which can inadvertently smooth the R peaks, making them more challenging to identify. We compared the algorithm's performance using two extensive databases: the meta-database QT database and the BIH-MIT database. Importantly, our method does not necessitate the precise localization of the ECG signal's isoelectric line, contributing to its low computational complexity. In the analysis of the QT database, our algorithm demonstrated a substantial advantage over the classical Pan-Tompkins algorithm and maintained competitiveness with state-of-the-art approaches. In the case of the BIH-MIT database, the performance results were more conservative; they continued to underscore the real-world utility of our algorithm in clinical contexts.

Novel K-Nearest Neighbor With Convolutional Neural Networks (KNN-CNN) For Accurate Brain Tumor Detection In Image Mining

Brain tumor classification plays a crucial role in early diagnosis and effective treatment planning. In this paper, we propose a novel approach, K-Nearest Neighbor with Convolutional Neural Networks (KNN-CNN), for accurate brain tumor classification. The proposed method combines the strengths of K-Nearest Neighbor (KNN) and Convolutional Neural Networks (CNNs) to leverage both traditional feature-based classification and deep learning-based feature extraction. We use CNNs to learn high-level features from brain tumor images, and KNN is employed to classify tumors based on the extracted features. The experimental results on a brain tumor dataset demonstrate the effectiveness and efficiency of the KNN-CNN approach, achieving high classification accuracy and outperforming traditional methods.

Clinical value of neutrophil-to-lymphocyte ratio and prognostic nutritional index on prediction of occurrence and development of diabetic foot-induced sepsis.

Diabetic foot-induced sepsis is a serious complication associated with increased disability and mortality in hospitalized patients. Early prediction of admission and detection effectively improve treatment options and prevent further deterioration. This study aims to evaluate the clinical value of the neutrophil-to-lymphocyte ratio (NLR) and prognostic nutritional index (PNI) to predict the risk of sepsis in patients with diabetic foot ulcers (DFU). Retrospective analysis was performed on 216 patients who were admitted to the Fujian Medical University Union Hospital between January 2015 and December 2022. Patients with DFU were divided into the non-sepsis (n = 166) and the DFU-induced sepsis (n = 50) groups. The independent factors of DFU-induced sepsis were determined by univariate and multivariate logistic regression analyses. A receiver operating characteristic (ROC) curve was performed to compare the area under the curves (AUC) of PNI and NLR. Multivariate logistic regression analysis revealed that the PNI, NLR, international normalized ratio (INR), thrombin time (PT), and C-reactive protein (CRP) were independent prognostic factors for DFU-induced sepsis. After adjusting for potential confounders, the adjusted odds ratios of NLR for DFU-induced sepsis were 1.121 (1.072-1.172), 1.132 (1.077-1.189), and 1.080 (1.022-1.142), while those of PNI were 0.912 (0.873-0.953), 0.902 (0.856-0.950), and 1.004 (1.001-1.006). Moreover, the AUC of NLR was significantly greater than that of CRP (0.790, 95% CI: 0.689-0.891, p < 0.001 vs. 0.780, 95% CI: 0.686-0.873, p < 0.001). NLR and PNI have been regarded as readily and independently predictive markers in patients with DFU-induced sepsis. NLR is critical for the early detection and effective treatment of DFU-induced sepsis and is superior to CRP.