A Novel Scoring System for Response of Preoperative Chemoradiotherapy in Locally Advanced Rectal Cancer Using Early-Treatment Blood Features Derived From Machine Learning

BackgroundPreoperative chemoradiotherapy (CRT) is a standard treatment for locally advanced rectal cancer (LARC). However, individual responses to preoperative CRT vary from patient to patient. The aim of this study is to develop a scoring system for the response of preoperative CRT in LARC using blood features derived from machine learning.MethodsPatients who underwent total mesorectal excision after preoperative CRT were included in this study. The performance of machine learning models using blood features before CRT (pre-CRT) and from 1 to 2 weeks after CRT (early-CRT) was evaluated. Based on the best model, important features were selected. The scoring system was developed from the selected model and features. The performance of the new scoring system was compared with those of systemic inflammatory indicators: neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, and the prognostic nutritional index.ResultsThe models using early-CRT blood features had better performances than those using pre-CRT blood features. Based on the ridge regression model, which showed the best performance among the machine learning models (AUROC 0.6322 and AUPRC 0.5965), a novel scoring system for the response of preoperative CRT, named Response Prediction Score (RPS), was developed. The RPS system showed higher predictive power (AUROC 0.6747) than single blood features and systemic inflammatory indicators and stratified the tumor regression grade and overall downstaging clearly.ConclusionWe discovered that we can more accurately predict CRT response by using early-treatment blood data. With larger data, we can develop a more accurate and reliable indicator that can be used in real daily practices. In the future, we urge the collection of early-treatment blood data and pre-treatment blood data.

Impact of the serum and blood biomarkers on the severity and survival of the COVID-19 infected patients with dementia

Aim Elderly population is categorized as a risk group for COVID-19 infection and dementia is the major cause of disability in elderly individuals and affects 70% of the elderly population. In this study, we evaluated blood and serum biomarkers of the patients with dementia infected by COVID-19 to evaluate possible indicators of the severity of COVID-19 infection. Methods Laboratory biomarkers of 11 dementia patients between the ages 85-96 infected by COVID-19 have been used for this study. Serum biochemistry and blood data of survived six patients were compared with the five patients who died because of COVID-19 to evaluate biomarkers correlated with COVID-19 severity and disease mortality. Results Fibrinogen, d-dimer, C-reactive protein (CRP), P, and Mg levels increased in the deceased dementia patients compared to the survived ones. Glucose, blood urea nitrogen (BUN), alanine transaminase (ALT), aspartate aminotransferase (AST), troponin, lactate, and procalcitonin levels significantly decreased in the deceased patients compared to the survived ones infected by COVID-19. %NEU, %LYM, MONO, %MONO, EOS, %EOS, %BASO, MPV, PT, INR, hematocrit (HCT), hemoglobin (Hb), total Hb, red blood cells (RBC), PDW, and ferritin levels decreased in the deceased patients compared to the healthy ones, where red cell distribution width (RDW), prothrombin time (PT), WBC and NEU levels significantly increased in the deceased patients infected by COVID-19. Conclusion Changes in the serum biochemistry and blood markers are correlated with COVID-19 infection severity and mortality that can be used to the prediction of disease progression in dementia patients.

Correlation between Individual Characteristics and Work Position with Work Fatigue on Workers

Introduction: Work fatigue is a condition of decreased efficiency and resilience of workers, which can interfere the companys’ productivity. Job fatigue can be caused by the performance, duration, and effort (work position) of the workers. Based on the observations, workers at the expedition sub units of PT X needed to lift 200 to 400kg heavy loads. The activity of lifting very heavy weights can cause fatigue. One form of physical fatigue indicators is an increase in lactic acid in the blood. The purpose of this study was to analyze the correlation between workers’ characteristics and work position with work fatigue among workers in the expedition sub units of PT X. Methods: This research was a quantitative study with a cross sectional design. The population of this study was 36 people. This research used total sampling. The independent variable were individual characteristics and work position as measured using REBA tools, and the dependent variable was work fatigue as measured using lactic acid levels in blood. Data was analyzed using statistical tools to obtain the value of correlation coefficient between variables. Result: The results showed that there was a weak positive correlation between workers’ characteristics and physical fatigue. The results showed that the coefficient value between work position and physical fatigue was 0.354. The coefficient figure indicated a weak positive correlation between work position and work fatigue. Conclusion: There was a correlation between work position and work fatigue in workers.Keywords: ergonomics, lactic acid, REBA, work fatigue, work position .

Personalized Nutritional Therapy Based on Blood Data Analysis for Malaise Patients

As medical doctors, we routinely check patient blood chemistry and CBC data to diagnose disease. However, these data and methods of analysis are very rarely used to find pre-disease conditions or treat undiagnosed malaise. Masatoshi Kaneko Ph.D. found that many pre-disease conditions and types of malaise could be detected using his unique method of blood data analysis, and could also be treated using personalized nutritional therapy as an alternative to using drugs. The authors of this article introduce personalized nutritional therapy based on blood data analysis (Kaneko’s method), and present and discuss some clinical cases. In total, 253 pre-disease or undiagnosed patients were treated using this nutritional therapy approach, and most of them recovered from their chronic symptoms and pre-disease conditions. This novel nutritional therapy has the potential to help many presymptomatic and undiagnosed patients suffering from malaise.

Source-to-Target Automatic Rotating Estimation (STARE) — A publicly-available, blood-free quantification approach for PET tracers with irreversible kinetics: Theoretical framework and validation for [18F]FDG

Introduction: Full quantification of positron emission tomography (PET) data requires an input function. This generally means arterial blood sampling, which is invasive, labor-intensive and burdensome. There is no current, standardized method to fully quantify PET radiotracers with irreversible kinetics in the absence of blood data. Here, we present Source-to-Target Automatic Rotating Estimation (STARE), a novel, data-driven approach to quantify the net influx rate (Ki) of irreversible PET radiotracers, that requires only individual-level PET data and no blood data. We validate STARE with [18F]FDG PET and assess its performance using simulations. Methods: STARE builds upon a source-to-target tissue model, where the tracer time activity curves (TACs) in multiple "target" regions are expressed at once as a function of a "source" region, based on the two-tissue irreversible compartment model, and separates target region Ki from source Ki by fitting the source-to-target model across all target regions simultaneously. To ensure identifiability, data-driven, subject-specific anchoring is used in the STARE minimization, which takes advantage of the PET signal in a vasculature cluster in the FOV that is automatically extracted and partial volume-corrected. To avoid the need for any a priori determination of a single source region, each of the considered regions acts in turn as the source, and a final Ki is estimated in each region by averaging the estimates obtained in each source rotation. Results: In a large dataset of [18F]FDG human scans (N=69), STARE Ki estimates were in good agreement with corresponding arterial blood-based estimates (regression slope=0.88, r=0.80), and were precisely estimated, as assessed by comparing STARE Ki estimates across several runs of the algorithm (coefficient of variation across runs=6.74 ± 2.48%). In simulations, STARE Ki estimates were largely robust to factors that influence the individualized anchoring used within its algorithm. Conclusion: Through simulations and application to [18F]FDG PET data, feasibility is demonstrated for STARE blood-free, data-driven quantification of Ki. Future work will include applying STARE to PET data obtained with a portable PET camera and to other irreversible radiotracers.

Correlation of Hyperandrogenemia and Metabolic Syndrome in Thai Women With Polycystic Ovary Syndrome (PCOS).

Objective: To investigate the prevalence of metabolic syndrome compared between hyperandrogenemia and non-hyperandrogenemia in Thai women with PCOS, and to identify factors significantly associated with metabolic syndrome in this PCOS population.Methods: Thai PCOS women were conducted during 2010-2018. Patients were categorized into the non-hyperandrogenemia group or the hyperandrogenemia group defined by total testosterone >0.8 ng/mL or free testosterone >0.006 ng/mL or dehydroepiandrosterone sulfate (DHEA-S) >350 mcg/dL. Metabolic syndrome was diagnosed according to National Cholesterol Education Program-Adult Treatment Panel III criteria. Demographic, anthropometric, clinical, and biochemical blood data were collected and analyzed.Results: 520 PCOS women were included. 22.6% had metabolic syndrome and 75.0% had hyperandrogenemia. Free testosterone cut-off to define hyperandrogenemia for determining metabolic syndrome in PCOS yielded the highest sensitivity (88.9%) and the highest negative predictive value (90.8%). The prevalence of metabolic syndrome was 27.1% in hyperandrogenemia and 9.2% in non-hyperandrogenemia. Factors significantly associating with metabolic syndrome in Thai PCOS women were age, BMI, free testosterone, and DHEA-S.Conclusion: The prevalence of metabolic syndrome was to be significantly higher in hyperandrogenemia women than in their non-hyperandrogenemia counterparts. Older age, higher BMI, higher free testosterone, and lower DHEA-S were all identified as factors significantly associated with metabolic syndrome.

Transcranial eddy current damping sensors for detection and imaging of hemorrhagic stroke: feasibility in benchtop experimentation

OBJECTIVE Spontaneous intracerebral hemorrhage occurs in an estimated 10% of stroke patients, with high rates of associated mortality. Portable diagnostic technologies that can quickly and noninvasively detect hemorrhagic stroke may prevent unnecessary delay in patient care and help rapidly triage patients with ischemic versus hemorrhagic stroke. As such, the authors aimed to develop a rapid and portable eddy current damping (ECD) hemorrhagic stroke sensor for proposed in-field diagnosis of hemorrhagic stroke. METHODS A tricoil ECD sensor with microtesla-level magnetic field strengths was constructed. Sixteen gelatin brain models with identical electrical properties to live brain tissue were developed and placed within phantom skull replicas, and saline was diluted to the conductivity of blood and placed within the brain to simulate a hemorrhage. The ECD sensor was used to detect modeled hemorrhages on benchtop models. Data were saved and plotted as a filtered heatmap to represent the lesion location. The individuals performing the scanning were blinded to the bleed location, and sensors were tangentially rotated around the skull models to localize blood. Data were also used to create heatmap images using MATLAB software. RESULTS The sensor was portable (11.4-cm maximum diameter), compact, and cost roughly $100 to manufacture. Scanning time was 2.43 minutes, and heatmap images of the lesion were produced in near real time. The ECD sensor accurately predicted the location of a modeled hemorrhage in all (n = 16) benchtop experiments with excellent spatial resolution. CONCLUSIONS Benchtop experiments demonstrated the proof of concept of the ECD sensor for rapid transcranial hemorrhagic stroke diagnosis. Future studies with live human participants are warranted to fully establish the feasibility findings derived from this study.

A personalized network framework reveals predictive axis of anti-TNF response across diseases

Personalized treatment of complex diseases is an unmet medical need pushing towards drug biomarker identification of one drug-disease combination at a time. Here, we used a novel computational approach for modeling cell-centered individual-level network dynamics from high-dimensional blood data to predict infliximab response and uncover individual variation of non-response. We identified and validated that the RAC1-PAK1 axis is predictive of infliximab response in inflammatory bowel disease. Intermediate monocytes, which closely correlated with inflammation state, play a key role in the RAC1-PAK1 responses, supporting their modulation as a therapeutic target. This axis also predicts response in Rheumatoid arthritis, validated in three public cohorts. Our findings support pan-disease drug response diagnostics from blood, implicating common mechanisms of drug response or failure across diseases.

Next Generation Sequencing for Detecting Somatic FAS Mutations in Patients With Autoimmune Lymphoproliferative Syndrome

Autoimmune lymphoproliferative syndrome (ALPS) is a primary immune regulatory disorder clinically defined by chronic and benign lymphoproliferation, autoimmunity and an increased risk of lymphoma due to a genetic defect in the FAS-FASL apoptotic pathway. Genetic defects associated with ALPS are germinal and somatic mutations in FAS gene, in addition to germinal mutations in FASLG, FADD, CASP8 and CASP10 genes. The accumulation of CD3+TCRαβ+CD4-CD8- double negative T-cells (DNT) is a hallmark of the disease and 20-25% of ALPS patients show heterozygous somatic mutations restricted to DNT in the FAS gene (ALPS-sFAS patients). Nowadays, somatic mutations in the FAS gene are detected through Sanger sequencing in isolated DNT. In this study, we report an ALPS-sFAS patient fulfilling clinical and laboratory ALPS criteria, who was diagnosed through NGS with a targeted gene panel using DNA from whole blood. Data analysis was carried out with Torrent Suite Software and variant detection was performed by both germinal and somatic variant caller plugin. The somatic variant caller correctly detected other six ALPS-sFAS patients previously diagnosed in the authors’ laboratories. In summary, this approach allows the detection of both germline and somatic mutations related to ALPS by NGS, avoiding the isolation of DNT as the first step. The reads of the somatic variants could be detected even in patients with DNT in the cut off limit. Thus, custom-designed NGS panel testing may be a faster and more reliable method for the diagnosis of new ALPS patients, including those with somatic FAS mutations (ALPS-sFAS).

The analysis of epigenomic evolution

While the investigation of the epigenome becomes increasingly important, still little is known about the long-term evolution of epigenetic marks and systematic investigation strategies are still withstanding. Here, we systematically demonstrate the transfer of classic phylogenetic methods such as maximum likelihood based on substitution models, parsimony, and distance-based to interval-scaled epigenetic data (available at Github). Using a great apes blood data set, we demonstrate that DNA methylation is evolutionarily conserved at the level of individual CpGs in promotors, enhancers and genic regions. Our analysis also reveals that this epigenomic conservation is significantly correlated with its transcription factor binding density. Binding sites for transcription factors involved in neuron differentiation and components of AP-1 evolve at a significantly higher rate at methylation than at nucleotide level. Moreover, our models suggest an accelerated epigenomic evolution at binding sites of BRCA1, CBX2, and factors of the polycomb repressor 2 complex in humans. For most genomic regions, the methylation-based reconstruction of phylogenetic trees is at par with sequence-based reconstruction. Most strikingly, phylogenetic reconstruction using methylation rates in enhancer regions was ineffective independently of the chosen model. We identify a set of phylogenetically uninformative CpG sites enriching in enhancers controlling immune-related genes.