Precise Prognosis Research Articles

Predicting the risk of in-hospital Asian NSTEMI patients using stacked ensemble learning

Abstract Background NSTEMI affects 60–75% of patients with MI and has a variety of clinical manifestations and prognoses. Ensemble learning (EL) utilizes the machine learning (ML) models on a classification task to make more accurate predictions than any single ML model. It has also been demonstrated to be superior to conventional risk scores in predicting mortality risk. However, no studies on the application of EL to ASIAN NSTEMI patients have been published. Purpose To identify factors associated with Asian in-hospital NSTEMI patients using ML and to develop a stacking EL-based NSTEMI risk score that is specifically tailored to the Asian population. Methods From 2006 to 2016, 10464 data were collected from the Malaysian National Cardiovascular Disease Database (NCVD-ACS) registry on hospitalized multiethnic Asian patients admitted with NSTEMI. This study utilized 39 variables, including demographic, cardiovascular risk, medication, and clinical variables. For stacked EL model development, four algorithms, Support Vector Machine (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGB), and Naive Bayes (NB), are used as base learners, and Generalized Linear Model (GLM) is used as the meta learner. Significant variables were chosen and ranked using ML variable importance with backward elimination. As a predictive performance metric, the area under the curve (AUC) was used. Using a validation dataset, algorithms were validated against the TIMI for NSTEMI, and the net reclassification index (NRI) was calculated. Results The stacked EL models developed using SVM feature selection (13 predictors, AUC = 0.823, CI: 0.779 - 0.867) outperforms the conventional risk score, TIMI (AUC = 0.563, CI: 0.515 - 0.610). RF contributes the most to the EL model predictions, followed by NB, XGB and SVM (Figure 1). Common predictors between SVM feature selection and TIMI are; age, hypertension and ECG-type depression. SVM feature selection also identified gender, MI history, chronic lung disease, heart rate, systolic blood pressure, Killip class, fasting blood glucose, LMWH, beta-blockers and angiotensin II receptor blocker as predictors that improve mortality prediction in ASIAN NSTEMI patients. Our findings indicate that the TIMI score underestimates the mortality risk of NSTEMI patients. Stacked EL model using selected predictors classified 47.4% of nonsurvivors as high risk (risk probabilities > 50%), whereas TIMI (score > 5) classified only 3.3% of nonsurvivors as high risk (Figure 2). The NRI of NSTEMI patients using the EL model (SVM selected var) resulted in an NRI of 79.8% with a p-value < 0.00001 when compared to the TIMI for NSTEMI risk score. Conclusions The stacked EL algorithm classifies ASIAN NSTEMI patients more accurately than the single ML model and TIMI score. A precise prognosis is useful for selecting the appropriate level of care and pharmacological or invasive treatment for patients with Asian NSTEMI.Relative influence of each ML modelsStacked EL performance on validation set

MAPK signaling pathway-based glioma subtypes, machine-learning risk model, and key hub proteins identification

An early diagnosis and precise prognosis are critical for the treatment of glioma. The mitogen‑activated protein kinase (MAPK) signaling pathway potentially affects glioma, but the exploration of the clinical values of the pathway remains lacking. We accessed data from TCGA, GTEx, CGGA, etc. Up-regulated MAPK signaling pathway genes in glioma were identified and used to cluster the glioma subtypes using consensus clustering. The subtype differences in survival, cancer stemness, and the immune microenvironment were analyzed. A prognostic model was trained with the identified genes using the LASSO method and was validated with three external cohorts. The correlations between the risk model and cancer-associated signatures in cancer were analyzed. Key hub genes of the gene set were identified by hub gene analysis and survival analysis. 47% of the MAPK signaling pathway genes were overexpressed in glioma. Subtypes based on these genes were distinguished in survival, cancer stemness, and the immune microenvironment. A risk model was calculated with high confidence in the prediction of overall survival and was correlated with multiple cancer-associated signatures. 12 hub genes were identified and 8 of them were associated with survival. The MAPK signaling pathway was overexpressed in glioma with prognostic value.

An Activated Dendritic-Cell-Related Gene Signature Indicative of Disease Prognosis and Chemotherapy and Immunotherapy Response in Colon Cancer Patients

Accumulating evidence has underscored the prognostic value of tumor-infiltrating immune cells in the tumor microenvironment of colon cancer (CC). In this retrospective study, based on publicly available transcriptome profiles and clinical data from the Gene Expression Omnibus and The Cancer Genome Atlas databases, we derived and verified an activated dendritic cell (aDC)-related gene signature (aDCRS) for predicting the survival outcomes and chemotherapy and immunotherapy response of CC patients. We quantified the infiltration abundance of 22 immune cell subtypes via the “CIBERSORT” R script. Univariate Cox proportional hazards (PHs) regression was used to identify aDC as the most robust protective cell type for CC prognosis. After selecting differentially expressed genes (DEGs) significantly correlated with aDC infiltration, we performed univariate Cox-PH regression, LASSO regression, and stepwise multivariate Cox-PH regression successively to screen out prognosis-related genes from selected DEGs for constructing the aDCRS. Receiver operating characteristic (ROC) curves and Kaplan–Meier (KM) analysis were employed to assess the discriminatory ability and risk-stratification capacity. The “oncoPredict” package, Cancer Treatment Response gene signature DataBase, and Tumor Immune Dysfunction and Exclusion algorithm were utilized to estimate the practicability of the aDCRS in predicting response to chemotherapy and immune checkpoint blockade. Gene set enrichment analysis and single-cell RNA sequencing analysis were also implemented. Furthermore, an aDCRS-based nomogram was constructed and validated via ROC curves, calibration plots and decision curve analysis. In conclusion, aDCRS and an aDCRS-based nomogram will facilitate precise prognosis prediction and individualized therapeutic interventions, thus improving the survival outcomes of CC patients in the future.

Fusing WGCNA and Machine Learning for Immune-Related Gene Prognostic Index in Lung Adenocarcinoma: Precision Prognosis, Tumor Microenvironment Profiling, and Biomarker Discovery.

The objective is to create an IRGPI (Immune-related genes prognostic index), which could predict the survival and effectiveness of immune checkpoint inhibitor (ICI) treatment for lung adenocarcinoma (LUAD). By applying weighted gene co-expression network analysis (WGCNA), we ascertained 13 genes associated with immune functions. An IRGPI was constructed using four genes through multicox regression, and its validity was assessed in the GEO dataset. Next, we explored the immunological and molecular attributes and advantages of ICI treatment in subcategories delineated by IRGPI. The model genes were also validated by the random forest tree, and functional experiments were conducted to validate it. The IRGPI relied on the genes CD79A, IL11, CTLA-4, and CD27. Individuals categorized as low-risk exhibited significantly improved overall survival in comparison to those classified as high-risk. Extensive findings indicated that the low-risk category exhibited associations with immune pathways, significant infiltration of CD8 T cells, M1 macrophages, and CD4 T cells, a reduced rate of gene mutations, and improved sensitivity to ICI therapy. Conversely, the higher-risk group displayed metabolic signals, elevated frequencies of TP53, KRAS, and KEAP1 mutations, escalated levels of NK cells, M0, and M2 macrophage infiltration, and a diminished response to ICI therapy. Additionally, our study unveiled that the downregulation of IL11 effectively impedes the proliferation and migration of lung carcinoma cells, while also inducing cell cycle arrest. IRGPI is a biomarker with significant potential for predicting the effectiveness of ICI treatment in LUAD patients and is closely related to the microenvironment and clinicopathological characteristics.

Pharmacoencephalographic Assessment of Antiphyschotic Agents’ Effect Dose-Dependency in Rats

Pharmacoencephalography (pharmaco-EEG) is a prominent instrument for the pharmacological screening new psychoactive molecules. This experimental approach has not remained a vestige of neurobiological studies, and can be used successfully to complete today’s research objectives. The development and rise to universal use of machine learning techniques opens up novel prospects for the use of pharmaco-EEG data to solve the problems of classification and prognosis. We have previously shown that naïve Bayes classifier (NBC) combined with the principal component analysis (PCA) can be used to differentiate between antipsychotic and sedative drug effects as well as to distinguish among the antipsychotics’ effects. In the present study, we evaluated the possibility to employ this method to assess the dose-dependency of antipsychotic effects. The experiments were carried out in white outbred male rats with chronically implanted electrocorticographic electrodes. As the agents of interest, we chose two drugs with antipsychotic activity, chlorpromazine and promethazine, in three doses each (0.1, 1, 10 mg/kg and 0.5, 5 and 20 mg/kg, respectively). The training set, used as a reference to determine the pharmacological effects of the agents of interest, included the D2-dopamine receptor blocker haloperidol, M-cholinergic receptor blocker tropicamide, H1-histamine receptor blocker chloropyramine, the sedative dexmedetomidine, and the anxiolytic phenazepam. We have shown that the lowest chlorpromazine dose (0.1 mg/kg) can be characterized as antipsychotic with a marked histaminolytic effect, while the highest one (10 mg/kg) exhibits predominantly antipsychotic activity with a cataleptogenic effect. All the doses demonstrated anticholinergic activity, which increased with the dose. For promethazine, we observed a clear dose-dependent transition from antipsychotic action to cataleptogenic, alongside a notable antimuscarinic effect of all doses. None of promethazine doses showed any resemblance to chloropyramine, which probably indicates its anti-dopaminergic and antimuscarinic effects being able to mask its H1-antihistamine effect in the used dose range. In summary, our results demonstrate that NBC coupled with PCA can be used to determine the dose-dependency of antipsychotic agents’ effects based on their impact on electrocorticogram parameters. Further development of this method as well as expansion of psychotropic agent electropharmacogram library would allow for more precise prognosis of pharmacological activity of the agents of interest.

Ensemble Classification Model for Brain Tumor Detection

Abstract: Brain tumor analysis is essential in timely diagnosis and effective treatment to cure patients. The growth of abnormal cells in the brain is the cause of brain tumors, which can lead to cancer. Brain tumors occur due to uncontrolled and rapid cell growth. If not treated at an initial phase, it may lead to death. The variations in tumor location, shape, and size pose a major challenge for brain tumor detection. The aim of this survey is to provide a comprehensive literature on brain tumor detection through magnetic resonance imaging to help the researchers. In this framework based on deep learning, magnetic resonance images (MRIs) are used to identify and categorize brain tumors. In the first step, we propose a preprocessing approach to work only on a small portion of the image rather than the entire image. These little portions of the images provide a very quick and precise brain tumor prognosis, which aids in giving patients the right treatment. The radiologist can make speedy conclusions thanks to these predictions. In the proposed work, A self-defined artificial neural network (ANN) and a network of convolution neural networks (CNN) are used in the proposed work to detect the presence of brain tumors, and their performance is examined

Osteosarcoma Multi-Omics Landscape and Subtypes.

Osteosarcoma (OS) is the most common primary bone malignancy that exhibits remarkable histologic diversity and genetic heterogeneity. The complex nature of osteosarcoma has confounded precise molecular categorization, prognosis, and prediction for this disease. In this study, we performed a comprehensive multiplatform analysis on 86 osteosarcoma tumors, including somatic copy-number alteration, gene expression and methylation, and identified three molecularly distinct and clinically relevant subtypes of osteosarcoma. The subgrouping criteria was validated on another cohort of osteosarcoma tumors. Previously unappreciated osteosarcoma-type-specific changes in specific genes' copy number, expression and methylation were revealed based on the subgrouping. The subgrouping and novel gene signatures provide insights into refining osteosarcoma therapy and relationships to other types of cancer.

CircUSP10 promotes liver cancer progression by regulating miR-211-5p/TCF12/EMT signaling pathway

There is no precise diagnosis or prognosis for liver cancer (LC) using a single biomarker. Circular RNAs (circRNAs) contribute to the pathogenesis of different cancers, but their role in LC is not entirely understood. In this study, circUSP10, an aberrantly expressed circRNA in LC, was screened using the Gene Expression Omnibus database, and its tissue-specific expression was verified using qRT-PCR. In vitro, functional assays and nude mouse tumorigenesis models were used to investigate circUSP10 role in LC. RNA immunoprecipitation and dual-luciferase reporter assays were performed to study the mechanistic relationship between circUSP10, miR-211-5p, and transcription factor 12 (TCF12). We found that circUSP10 expression was upregulated in LC tissues and cells. CircUSP10 expression was linked to tumor size and tumor node metastasis stage and negatively correlated with LC prognosis. In vitro assays confirmed circUSP10-mediated proliferation, migration, and invasion of LC cells and their association with the epithelial-mesenchymal transition (EMT) pathway. Mechanistically, circUSP10 adsorbed miR-211-5p, which regulated TCF12 and promoted tumorigenesis via the EMT signaling pathway. Therefore, our results suggest that circUSP10 may promote LC progression by modulating the miR-211-5p/TCF12/EMT signaling cascade and may serve as a potential biomarker for LC diagnosis and prognosis.

Circulating Tumor Cells as a Promising Tool for Early Detection of Hepatocellular Carcinoma.

Liver cancer is a significant contributor to the cancer burden, and its incidence rates have recently increased in almost all countries. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and is the second leading cause of cancer-related deaths worldwide. Because of the late diagnosis and lack of efficient therapeutic modality for advanced stages of HCC, the death rate continues to increase by ~2-3% per year. Circulating tumor cells (CTCs) are promising tools for early diagnosis, precise prognosis, and follow-up of therapeutic responses. They can be considered to be an innovative biomarker for the early detection of tumors and targeted molecular therapy. In this review, we briefly discuss the novel materials and technologies applied for the practical isolation and detection of CTCs in HCC. Also, the clinical value of CTC detection in HCC is highlighted.

P11.70.A EQUITY IN GENOMIC TESTING: LESSONS LEARNT FOLLOWING THE ROLLOUT OF AN AMBITIOUS GENOMIC TESTING STRATEGY FOR NEUROLOGICAL TUMOUR PATIENTS IN THE UNITED KINGDOM

Abstract BACKGROUND The NHS England Genomic Test Directory now offers extensive genomic testing to brain tumour patients including whole genome sequencing (WGS), methylation arrays, gene panels, and other targeted mutation tests. Comprehensive genomic testing enables precise diagnosis and prognosis, tailoring of treatment, and research into new actionable targets. Despite the ambitious plans and potential for significant patient benefits, the Tessa Jowell Brain Cancer Mission (TJBCM) identified variation in deployment of these tests across Neuro-Oncology Centres in the UK. Based on these findings, we aimed to further understand the variation. We aimed to uncover drivers to delivering a successful genomic testing programme and identify local and national barriers to rolling out extensive genomic testing. MATERIAL AND METHODS As part of the TJBCM Centre of Excellence Programme, 90% of Neuro-Oncology Centres in the UK provided self-reported data on volumes of genomic testing for brain tumour patients. Subsequently, structured interviews were conducted with neuro-oncologists, neuropathologists, neurosurgeons, clinical scientists and other stakeholders from these Centres to further evaluate the state of genomic testing. RESULTS Accessibility to WGS varied from no access, to Centres where WGS was available in a limited capacity, to Centres where WGS was accessible but not scalable or timely. Bottlenecks were identified throughout the WGS pathway including inadequate tumour tissue, sample processing and transport delays, and insufficient clinical scientists to interpret results. As such, no UK Centre interviewed had sufficient capacity to offer WGS to all neurological tumour patients in a timely manner using NHS-accredited pathways. Interviews showed geographic variability in access to basic molecular tests such as methylation array and CDKN2A/B which are required for diagnostics and prognostics. There was also a strong reliance on a small number of laboratories to provide methylation array, resulting in backlogs. Common drivers to effective delivery of genomic testing included funded posts for research nurses, innovative systems for delivery of tumour samples from surgery to the laboratory, dedicated staff who advocated for genomic testing, and well-established Genomic Tumour Advisory Boards for test interpretation. CONCLUSION Impressive progress has been made in the last two years yet there remains a mismatch between what the National Genomic Test Directory dictates, and what can currently be delivered in a manageable timeframe. Inconsistencies in infrastructure, funding and training are preventing UK Centres from delivering the intended world-leading genomic testing. Barriers to address vary from simple changes at a local level, to national coordination and funding so that genomic testing will be equally accessible to all brain tumour patients in the UK.

Swarm-intelligent elephant herding optimized support vector machine for predicting heart disease

Considering the significant worldwide mortality rate of cardiac disease, early and precise prognosis is essential to enhancing patient outcomes. The accuracy, effectiveness, and dependability of the heart disease prediction techniques currently in use, however, continue to encounter difficulties. Therefore, it is essential to create novel strategies that can get beyond these constraints and offer a better way to forecast cardiac disease. A novel advanced elephant herding optimised support vector machine (AEHO-SVM) technique utilized for the study in suggest method for predicting cardiovascular disease. A heart disease dataset is used to assess the efficacy of the suggested AEHO-SVM algorithm. To deal with missing values, normalise the data, and minimise dimensionality, the dataset is pre-processed. The AEHO-SVM approach aims to maximise classification accuracy while lowering the possibility of misdiagnosis by optimising the hyperplane separation and changing the support vectors iteratively. A number of metrics, like sensitivity, accuracy, f-measure, and specificity are used for assess model's performance. The experiment show that the AEHO-SVM technique works better than conventional methods and achieves good predictive accuracy for the diagnosis of heart disease. A suggested AEHO-SVM technique makes use of the collective thinking of elephant herding and its built-in optimisation skills to provide a more effective and trustworthy prediction method.

A seven-immune-genes risk model predicts the survival and suitable treatments for patients with skin cutaneous melanoma

BackgroundSkin cutaneous melanoma is characterized by high malignancy and prognostic heterogeneity. Immune cell networks are critical to the biological progression of melanoma through the tumor microenvironment. Thus, identifying effective biomarkers for skin cutaneous melanoma from the perspective of the tumor microenvironment may offer strategies for precise prognosis prediction and treatment selection. MethodsA total of 470 cases from The Cancer Genome Atlas and 214 from the Gene Expression Omnibus were systematically evaluated to construct an optimal independent immune cell risk model with predictive value using weighted gene co-expression network analysis, Cox regression, and least absolute shrinkage and selection operator assay. The predictive power of the developed model was estimated through receiver operating characteristic curves and Kaplan-Meier analysis. The association of the model with tumor microenvironment status, immune checkpoints, and mutation burden was assessed using multiple algorithms. Additionally, the sensitivity of immune and chemotherapeutics was evaluated using the ImmunophenScore and pRRophetic algorithm. Furthermore, the expression profiles of risk genes were validated using gene expression profiling interactive analysis and Human Protein Atlas resources. ResultsThe risk model integrated seven immune-related genes: ARNTL, N4BP2L1, PARP11, NUB1, GSDMD, HAPLN3, and IRX3. The model demonstrated considerable predictive ability and was positively associated with clinical and molecular characteristics. It can be utilized as a prognostic factor for skin cutaneous melanoma, where a high-risk score was linked to a poor prognosis and indicated an immunosuppressive microenvironment. Furthermore, the model revealed several potential target checkpoints and predicted the therapeutic benefits of multiple clinically used drugs. ConclusionOur findings provide a comprehensive landscape of the tumor immune microenvironment in skin cutaneous melanoma and identify prognostic markers that may serve as efficient clinical diagnosis and treatment selection tools.

Neuroimaging based biotypes for precision diagnosis and prognosis in cancer-related cognitive impairment.

Cancer related cognitive impairment (CRCI) is commonly associated with cancer and its treatments, yet the present binary diagnostic approach fails to capture the full spectrum of this syndrome. Cognitive function is highly complex and exists on a continuum that is poorly characterized by dichotomous categories. Advanced statistical methodologies applied to symptom assessments have demonstrated that there are multiple subclasses of CRCI. However, studies suggest that relying on symptom assessments alone may fail to account for significant differences in the neural mechanisms that underlie a specific cognitive phenotype. Treatment plans that address the specific physiologic mechanisms involved in an individual patient's condition is the heart of precision medicine. In this narrative review, we discuss how biotyping, a precision medicine framework being utilized in other mental disorders, could be applied to CRCI. Specifically, we discuss how neuroimaging can be used to determine biotypes of CRCI, which allow for increased precision in prediction and diagnosis of CRCI via biologic mechanistic data. Biotypes may also provide more precise clinical endpoints for intervention trials. Biotyping could be made more feasible with proxy imaging technologies or liquid biomarkers. Large cross-sectional phenotyping studies are needed in addition to evaluation of longitudinal trajectories, and data sharing/pooling is highly feasible with currently available digital infrastructures.

Insulinoma Associated with MEN1 Syndrome: A Case of Persistent Hypoglycemia in School-aged Child.

Insulinoma is a rare cause of non-ketotic hypoglycemia both in adults and in children. Pediatric patients account for approximately 5% of all cases, mostly due to isolated benign lesions, but it can also be part of a multiple endocrine neoplasia type 1 syndrome (MEN1). We report the case of a patient with multiple hospitalizations related to hypoglycemia and neuroglycopenia symptoms, with multiple studies demonstrating the presence of an insulinoma as part of the spectrum of MEN1 syndrome. The primary significance of our report is to underscore that insulinoma can present as the initial manifestation of MEN1 syndrome in 10% of pediatric patients. Furthermore, we describe a likely pathogenic variant in the MEN1 gene not previously reported in the literature. Our report highlights the importance of the convergence of clinical, biochemical and molecular investigations in establishing a precise diagnosis, prognosis, and appropriate follow-up for pediatric patients with hypoglycemia.

Spatiotemporal Theranostic Nanoprobes Dynamically Monitor Targeted Tumor Therapy

AbstractCurrently, spatiotemporal theranostic nanoprobes are in great demand, owing to their enhanced target therapy and precise dynamic tracing of in vivo drug fate. Herein, this study highlights the successful development of dynamic theranostic nanoprobes, which are facilely established via self‐assembly between glutathione (GSH)‐responsive dasatinib (DAS) dimers and indocyanine green (ICG). The DAS dimers endow the nanoprobes with aggregation‐induced emission (AIE) characteristic, whose emission wavelength successfully redshifts from 420 to 810 nm compared to DAS‐based nanoprobes, the same as that of ICG, thus improving the total fluorescence intensity. Moreover, the nanoprobes exhibit a dynamic fluorescence intensity conversion that first decreases and then increases at the tumor site via intracellular GSH‐triggered AIE quenching and fluorescence re‐enhancement of ICG, therefore achieving precise tumor diagnosis, prognosis evaluation, and spatiotemporal tracing of drug fate compared to other imaging strategies. Furthermore, the nanoprobes show long‐term circulation stability via suitable particle sizes and zeta potentials, improved tumor accumulation via extracellular protonation and active cellular uptake, efficient drug release via response to the intracellular milieu, and enhanced apoptosis via targeting to intracellular kinase, therefore achieving the significant tumor inhibition. Thus, the spatiotemporal theranostic nanoprobes can dynamically monitor the targeted tumor therapy, greatly advancing their application in clinics.

Application value of serum protein indicators in constructing the early prediction model for the prognosis of patients with pulmonary tuberculosis

Objective: To analyze the clinical significance of laboratory examination indicators as the key prognostic factors and to construct an early prediction model for prognosis assessment of pulmonary tuberculosis patients. Methods: The basic information, biochemical indexes and blood routine items of 163 tuberculosis patients (144 males and 19 females, aged 41-70 years, with an average age of 56 years) and 118 healthy persons who underwent physical examination (101 males and 17 females, aged 46-64 years, with an average age of 54 years) in Suzhou Fifth People's Hospital from January 2012 to December 2020 were retrospectively collected. According to the presence of Mycobacterium tuberculosis after six months of treatment, the enrolled patients were divided into a cured group (96 cases) and a treatment failure group (67 cases). To analyze the baseline levels of laboratory examination indicators between these two groups, we screened the key predictors and the binary logistic regression method in SPSS statistics software was used to construct the prediction model. Results: The baseline levels of total protein, albumin, prealbumin, glutamic-pyruvic transaminase, erythrocyte, hemoglobin and lymphocyte were significantly higher in the cured group than in the treatment failure group. After 6 months of treatment, the indexes of total protein, albumin and prealbumin increased significantly in the cured group, but remained at the low levels in the treatment failure group. Receiver operating characteristic (ROC) curve analysis showed that total protein, albumin and prealbumin as independent predictors for forecasting the prognosis of pulmonary tuberculosis patients had the highest prediction accuracy. Logistic regression analysis showed that the combination of these three key predictors could construct the best early prediction model for assessing the prognosis of pulmonary tuberculosis patients, with a prediction accuracy of 0.924 (0.886-0.961), sensitivity of 75.0%, specificity of 94%, showing an ideal prediction accuracy. Conclusions: The routine test indexes of total protein, albumin and prealbumin show good application value in the construction of early prediction model for prognosis evaluation of pulmonary tuberculosis treatment. The combined prediction model consisting of total protein, albumin and prealbumin is expected to provide a theoretical basis and reference model for precision treatment and prognosis assessment of tuberculosis patients.

Pan-cancer evaluation of clinical value of mitotic network activity index (MNAI) and its predictive value for immunotherapy.

Increased mitotic activity is associated with the genesis and aggressiveness of many cancers. To assess the clinical value of mitotic activity as prognostic biomarker, we performed a pan-cancer study on the mitotic network activity index (MNAI) constructed based on 54-gene mitotic apparatus network. Our pan-cancer assessment on TCGA (33 tumor types, 10,061 patients) and validation on other publicly available cohorts (23 tumor types, 9,209 patients) confirmed the significant association of MNAI with overall survival, progression-free survival, and other prognostic endpoints in multiple cancer types, including lower-grade gliomas (LGG), breast invasive carcinoma (BRCA), as well as many others. We also showed significant association between MNAI and genetic instability, which provides a biological explanation of its prognostic impact at pan-cancer landscape. Our association analysis revealed that patients with high MNAI benefitted more from anti-PD-1 and Anti-CTLA-4 treatment. In addition, we demonstrated that multimodal integration of MNAI and the AI-empowered Cellular Morphometric Subtypes (CMS) significantly improved the predictive power of prognosis compared to using MNAI and CMS alone. Our results suggest that MNAI can be used as a potential prognostic biomarker for different tumor types toward different clinical endpoints, and multimodal integration of MNAI and CMS exceeds individual biomarker for precision prognosis.

Myocardial Infarction Detection using Morphological Features of ECG Signal

Cardiovascular Diseases (CVD’s) forms the vital basis of death in the world population. The human mortality rate due to CVD’s can be reduced with early detection and prevention of cardiac threat. Electrocardiogram (ECG) indicates the state of the cardiac heart and thus, acts as a standard to the cardiac health of an individual. ECG is a non-invasive, clinical therapeutic agent, which can be used to detect CVD in early stage and prognosis of threaten cardiac diseases. Since, the ECG is an abruptly changing, non-stationary waveform; the anomalies are non-periodic and may appear at varied fluctuations and intervals on random scale. Manual observation of ECG can be hectic in time and may be misleading to medical experts. So, it is necessary to endorse a computer assist framework for rapid and precise diagnosis of CVD’s. This paper deals with the automated characterization and detection of myocardial infarction (MI), dilated cardiomyopathy (DCM) diseases using morphological features extracted from ECG signal and machine learning approach. Our proposed approach attained maximum classification accuracy 87.5% for MIT-BIH Arrhythmia dataset and 99.33% accuracy for BIDMC Congestive Heart Failure (CHF) dataset with K-nearest neighbor (kNN) clustering and decision tree classifier with entropy criterion. The clinical experts can use our methodology to make precise prognosis of mentioned CVDs. Hence, survival prospect of a patient can be notably enlarged by prompt detection and clinical therapy of CVDs.

A novel AD theranostic platform with NIR-II laser controlled drug release and real-time monitoring of therapeutic outcomes

There is a long-standing lack of monitoring of Alzheimer's disease (AD) treatment. Imaging-guided dynamic monitoring of neurological disease treatment can provide a basis for sustained treatment, reflecting the outcomes of treatment and guiding the adjustment of therapy strategies. Here, by ingeniously using a second near-infrared (NIR-II) laser twice in tandem, we designed an AD theranostic nanoplatform with real-time monitoring of AD therapeutic outcomes. Upon first being irradiated with a 1064 nm laser, it realizes the light-controlled delivery of nanoparticles for valid antioxidant therapy. Notably, with secondary irradiation, the presence of AβO and Ca2+ triggered the DNA nanomachine, causing the fluorescent signal to recover. Using a two-step series switch based on thermodynamics, the platform can not only differentiate AD mice from wild-type mice but also provide guidance for the treatment of AD via monitoring the degree of intracellular calcium overload in the brain. Results suggest that the degree of calcium overload is negatively correlated with treatment outcome and can be used as a prognostic indicator for precision medicine. This method provides an image-based non-invasive strategy for early prognosis prediction of AD treatment, which is helpful for future precision medicine treatment planning and prognosis stratification.

Genomic Characterization of Emerging SARS-CoV-2: A Systematic Review

Introduction:: Severe Acute Respiratory Syndrome Coronavirus – 2, SARS-CoV-2, is a wellknown virus for its fatal infectivity and widespread impact on the health of the worldwide population. Genome sequencing is critical in understanding the virus’s behavior, origin, and genetic variants. This article conducts an extensive literature review on the SARS-CoV-2 genome, including its Genome Structure, Genome Analysis, Evolution, Mutation, and, Genome Computation. It highlights the summary of clinical and evolutionary research along with the applicability of computational methods in the areas. It lucidly presents the structural detail and mutation analysis of SARS-CoV-2 without overwhelming the readers with difficult terms. In the pandemic, machine learning and deep learning emerged as a paradigm change, that when combined with genome analysis, enabled more precise identification and prognosis of the virus's impact. Molecular detailing is crucial in extracting features from the SARS-CoV-2 genome before computation models are applied. Methods: Further, in this systematic study we investigate the usage of Machine Learning and Deep Learning models mapped to SARS-CoV-2 genome samples to see their applicability in virus detection and disease severity prediction. We searched research articles from various reputed journals explaining the structure, evolution, mutations, and computational methods published until June 2022. Results: The paper summarizes significant trends in the research of SARS-COV-2 genomes. Furthermore, this research also identifies the limitations and research gaps that yet have to be explored more and indicates future directions. Impact Statement:: There are few review articles on the SARS-CoV-2 genome; these reviews target various aspects of the SARS-COV2 genome individually. This article considers all the aspects simultaneously and provides in-depth knowledge about the SARS-CoV-2 genome. Conclusion:: This article provides a detailed description about the type of samples, volumes of selection, processes, and tools used by various researchers in their studies. Further, the computational techniques applied to the SARS-COV2 genome are also discussed and analysed thoroughly.