Can dietary flavonoids be useful in the personalized treatment of colorectal cancer?

Activating mutations in the oncogenes KRAS,BRAF and PI3K define molecular colorectal cancer(CRC)subtypes because they play key roles in promoting CRC development and in determining the efficacy of chemotherapeutic agents such as 5-fluorouracil and anti-epidermal growth factor receptor monoclonal antibodies.Survival of patients with cancers displaying these molecular profiles is low.Given the limited efficacy of therapeutic strategies for CRC presenting mutational activations in mitogen-activated protein kinase and/or PI3K pathways,developing combination therapies with natural flavonoids or other phytochemicals with demonstrated effects on these pathways(and little or no toxic effects)may constitute a valuable path forward.Much has been published on the anticancer effects of dietary phytochemicals.However,even an exhaustive characterization of potential beneficial effects produced by in vitro studies cannot be extrapolated to effects in humans.So far,the available data constitute a good starting point.Published results show quercetin and curcumin as possibly the best candidates to be further explored in the context of adjuvant CRC therapy either as part of dietary prescriptions or as purified compounds in combination regimens with the drugs currently used in CRC treatment.Clinical trial data is still largely missing and is urgently needed to verify relevant effects and for the development of more personalized treatment approaches.

Personalized treatment of well-differentiated gastric neuroendocrine tumors based on clinicopathological classification and grading:A multicenter retrospective study

Background:The incidence of well-differentiated gastric neuroendocrine tumors(G-NET)is increasing annually,and while they have a good prognosis and low mortality rate,their high recurrence rate makes treatment options controversial.This study aims to determine the relationship between individualized treatment plans and the recurrence of G-NET.Methods:We performed a multicenter,retrospective study of 94 patients with highly differentiated G-NET and treated at Peking Union Medical College Hospital,Yantai Yuhuangding Hospital,and Beijing Zhong-Neng-Jian Hospital from November 2015 to September 2023.Risk factors for recurrence of G-NETs were investigated using chi-squared test and multifactorial logistic regression analysis.Results:After a median follow-up of 49 months,the overall recurrence rate among the 94 G-NET patients was 14%(13/94).The recurrence rates of endoscopic mucosal resection(EMR),endoscopic submucosal dissection(ESD),somatostatin analog(SSA)therapy,and surgery were 43%(6/14),10%(5/49),5%(1/22),and 11%(1/9),respectively.Post-treatment recurrence rates were significantly different(P=0.014)among four treatments(EMR,ESD,SSA,and surgery),and further subgroup comparisons revealed lower recurrence rates in the ESD and SSA groups than in the EMR group.From the second month onward,SSA therapy considerably reduced the gastrin levels from 1081.0(571.5,2472.8)pg/mL to 461.5(255.3,795.0)pg/mL(Z=-3.521,P<0.001).Both chi-squared test and multifactorial logistic regression analysis suggested that among the clinicopathological parameters studied,only the pre-treatment gastrin level(P=0.018 and 0.005)and the type of treatment(P=0.014 and 0.017)were significantly associated with G-NET recurrence.Conclusions:Individualized treatment strategies may reduce the risk of relapse after G-NET treatment.Long-term SSA therapy may be a secure and efficacious treatment option for type 1 G-NET with more than six lesions,and it substantially decreases the incidence of post-treatment recurrence.

Revolutionizing Treatment:AI-Driven Noninvasive Approaches for ODD and ADHD

Oppositional Defiant Disorder(ODD)and Attention Deficit/Hyperactivity Disorder(ADHD)are mental health conditions that have traditionally been managed through behavioral therapies and medication.However,the integration of Artificial Intelligence(AI)has brought about a revolutionary shift in treatment approaches.This article explores the role of AI-driven noninvasive treatments for ODD and ADHD.AI offers personalized treatment plans,predictive analytics,virtual therapeutic platforms,and continuous monitoring,enhancing the effectiveness and accessibility of interventions.Ethical considerations and the need for a balanced approach are discussed.As technology evolves,collaborative efforts between mental health professionals and technologists will shape the future of mental health care for individuals with ODD and ADHD.

Extensive prediction of drug response in mutation-subtype-specific LUAD with machine learning approach

Lung cancer is the most prevalent cancer diagnosis and the leading cause of cancer death worldwide.Therapeutic failure in lung cancer(LUAD)is heavily influenced by drug resistance.This challenge stems from the diverse cell populations within the tumor,each having unique genetic,epigenetic,and phenotypic profiles.Such variations lead to varied therapeutic responses,thereby contributing to tumor relapse and disease progression.Methods:The Genomics of Drug Sensitivity in Cancer(GDSC)database was used in this investigation to obtain the mRNA expression dataset,genomic mutation profile,and drug sensitivity information of NSCLS.Machine Learning(ML)methods,including Random Forest(RF),Artificial Neurol Network(ANN),and Support Vector Machine(SVM),were used to predict the response status of each compound based on the mRNA and mutation characteristics determined using statistical methods.The most suitable method for each drug was proposed by comparing the prediction accuracy of different ML methods,and the selected mRNA and mutation characteristics were identified as molecular features for the drug-responsive cancer subtype.Finally,the prognostic influence of molecular features on the mutational subtype of LUAD in publicly available datasets.Results:Our analyses yielded 1,564 gene features and 45 mutational features for 46 drugs.Applying the ML approach to predict the drug response for each medication revealed an upstanding performance for SVM in predicting Afuresertib drug response(area under the curve[AUC]0.875)using CIT,GAS2L3,STAG3L3,ATP2B4-mut,and IL15RA-mut as molecular features.Furthermore,the ANN algorithm using 9 mRNA characteristics demonstrated the highest prediction performance(AUC 0.780)in Gefitinib with CCL23-mut.Conclusion:This work extensively investigated the mRNA and mutation signatures associated with drug response in LUAD using a machine-learning approach and proposed a priority algorithm to predict drug response for different drugs.

Harnessing the Power of Artificial Intelligence in Neuromuscular Disease Rehabilitation: A Comprehensive Review and Algorithmic Approach

Neuromuscular diseases present profound challenges to individuals and healthcare systems worldwide, profoundly impacting motor functions. This research provides a comprehensive exploration of how artificial intelligence (AI) technology is revolutionizing rehabilitation for individuals with neuromuscular disorders. Through an extensive review, this paper elucidates a wide array of AI-driven interventions spanning robotic-assisted therapy, virtual reality rehabilitation, and intricately tailored machine learning algorithms. The aim is to delve into the nuanced applications of AI, unlocking its transformative potential in optimizing personalized treatment plans for those grappling with the complexities of neuromuscular diseases. By examining the multifaceted intersection of AI and rehabilitation, this paper not only contributes to our understanding of cutting-edge advancements but also envisions a future where technological innovations play a pivotal role in alleviating the challenges posed by neuromuscular diseases. From employing neural-fuzzy adaptive controllers for precise trajectory tracking amidst uncertainties to utilizing machine learning algorithms for recognizing patient motor intentions and adapting training accordingly, this research encompasses a holistic approach towards harnessing AI for enhanced rehabilitation outcomes. By embracing the synergy between AI and rehabilitation, we pave the way for a future where individuals with neuromuscular disorders can access tailored, effective, and technologically-driven interventions to improve their quality of life and functional independence.

Integrated analysis of gene expression profiles reveals prognostic biomarkers for immunotherapy in cancer

Tumor immunotherapy has emerged as a promising method in cancer treatment,but patient responses vary,necessitating personalized strategies and prognostic biomarkers.This study aimed to identify prognostic factors and construct a predictive model for patient survival outcomes and immunotherapy response.We curated six immunotherapy datasets representing diverse cancer types and treatment regimens.After data preprocessing,patients were stratified based on immunotherapy response.Differential gene expression analysis identified 22 genes consistently dysregulated across multiple datasets.Functional analysis provided critical insights,highlighting the enrichment of these dysregulated genes in immune response pathways and tumor microenvironment-related processes.To create a robust prognostic model,we meticulously employed a multistep approach.Initially,the identified 22 genes underwent rigorous univariate Cox regression analysis to evaluate their individual associations with patient survival outcomes.Genes showing statistical significance(p-values<0.05)at this stage advanced to the subsequent multivariate Cox regression analysis,which aimed to address potential confounding factors and collinearity among genes.From this analysis,we ultimately identified four key genes—ST6GALNAC2,SNORA65,MFAP2,and CDKN2B—that were significantly associated with patient survival outcomes.Incorporating these four key genes along with their corresponding coefficients,we constructed a predictive model.This model’s efficacy was validated through extensive Cox regression analyses,demonstrating its robustness in predicting patient survival outcomes.Furthermore,our model exhibited promising predictive capability for immunotherapy response,providing a potential tool for anticipating treatment efficacy.These findings provide insights into immunotherapy response mechanisms and suggest potential prognostic biomarkers for personalized treatment.Our study contributes to advancing cancer immunotherapy and personalized medicine.

Identifying Biomarkers for Diabetic Kidney Disease Using GraphSAGE Neural Network

Diabetic Kidney Disease (DKD) is a common chronic complication of diabetes. Despite advancements in accurately identifying biomarkers for detecting and diagnosing this harmful disease, there remains an urgent need for new biomarkers to enable early detection of DKD. In this study, we modeled publicly available transcriptome datasets as a graph problem and used GraphSAGE Neural Networks (GNNs) to identify potential biomarkers. The GraphSAGE model effectively learned representations that captured the intricate interactions, dependencies among genes, and disease-specific gene expression patterns necessary to classify samples as DKD and Control. We finally extracted the features of importance;the identified set of genes exhibited an impressive ability to distinguish between healthy and unhealthy samples, even though these genes differ from previous research findings. The unexpected biomarker variations in this study suggest more exploration and validation studies for discovering biomarkers in DKD. In conclusion, our study showcases the effectiveness of modeling transcriptome data as a graph problem, demonstrates the use of GraphSAGE models for biomarker discovery in DKD, and advocates for integrating advanced machine-learning techniques in DKD biomarker research, emphasizing the need for a holistic approach to unravel the intricacies of biological systems.

Identification of a seven-gene signature and establishment of a prognostic nomogram predicting overall survival of triple-negative breast

Background: Triple-negative breast cancer (TNBC) is a highly heterogeneous breast cancersubtype characterized by the absence of expression of estrogen receptor (ER), progesteronereceptor (PR), and human epidermal growth factor receptor 2 (HER2). TNBC exhibitsresistance to hormone and HER2-targeted therapy, along with a higher incidence ofrecurrence and poorer prognosis. Therefore, exploring the molecular features of TNBC andconstructing prognostic models are of significant importance for personalized treatmentstrategies. Methods: In this research, bioinformatics approaches were utilized to screendifferentially expressed genes in 405 TNBC cases and 128 normal tissue samples from 8 GEOdatasets. Key core genes and signaling pathways were further identified. Additionally, aprognostic model incorporating seven genes was established using clinical and pathologicalinformation from 169 TNBC cases in the TCGA dataset, and its predictive performance wasevaluated. Results: Functional analysis revealed dysregulated biological processes such asDNA replication, cell cycle, and mitotic chromosome separation in TNBC. Protein-proteininteraction network analysis identified ten core genes, including BUB1, BUB1B, CDK1,CDC20, CDCA8, CCNB1, CCNB2, KIF2C, NDC80, and CENPF. A prognostic model consistingof seven genes (EXO1, SHCBP1, ABRACL, DMD, THRB, DCDC2, and APOD) was establishedusing a step-wise Cox regression analysis. The model demonstrated good predictiveperformance in distinguishing patients' risk. Conclusion: This research provides importantinsights into the molecular characteristics of TNBC and establishes a reliable prognosticmodel for understanding its pathogenesis and predicting prognosis. These findingscontribute to the advancement of personalized treatment for TNBC.

Status of precision medicine approaches to traumatic brain injury

Traumatic brain injury(TBI)is a serious condition in which trauma to the head causes damage to the brain,leading to a disruption in brain function.This is a significant health issue worldwide,with around 69 million people suffering from TBI each year.Immediately following the trauma,damage occurs in the acute phase of injury that leads to the primary outcomes of the TBI.In the hours-to-days that follow,secondary damage can also occur,leading to chronic outcomes.TBIs can range in severity from mild to severe,and can be complicated by the fact that some individuals sustain multiple TBIs,a risk factor for worse long-term outcomes.Although our knowledge about the pathophysiology of TBI has increased in recent years,unfortunately this has not been translated into effective clinical therapies.The U.S.Food and Drug Administration has yet to approve any drugs for the treatment of TBI;current clinical treatment guidelines merely offer supportive care.Outcomes between individuals greatly vary,which makes the treatment for TBI so challenging.A blow of similar force can have only mild,primary outcomes in one individual and yet cause severe,chronic outcomes in another.One of the reasons that have been proposed for this differential response to TBI is the underlying genetic differences across the population.Due to this,many researchers have begun to investigate the possibility of using precision medicine techniques to address TBI treatment.In this review,we will discuss the research detailing the identification of genetic risk factors for worse outcomes after TBI,and the work investigating personalized treatments for these higher-risk individuals.We highlight the need for further research into the identification of higher-risk individuals and the development of personalized therapies for TBI.

Mathematically modelling and controlling prostate cancer under intermittent hormone therapy

In this review, we summarize our recently developed mathematical models that predict the effects of intermittent androgen suppression therapy on prostate cancer （PCa）. Although hormone therapy for PCa shows remarkable results at the beginning of treatment, cancer cells frequently acquire the ability to grow without androgens during long-term therapy, resulting in an eventual relapse. To circumvent hormone resistance, intermittent androgen suppression was investigated as an alternative treatment option. However, at the present time, it is not possible to select an optimal schedule of on- and off-treatment cycles for any given patient. In addition, clinical trials have revealed that intermittent androgen suppression is effective for some patients but not for others. To resolve these two problems, we have developed mathematical models for PCa under intermittent androgen suppression. The mathematical models not only explain the mechanisms of intermittent androgen suppression but also provide an optimal treatment schedule for the on- and off-treatment periods.

Patient-centered developments in colon-and rectal cancer with a multidisciplinary international team:From translational research to national guidelines

Rarely,scientific developments centered around the patient as a whole arepublished.Our multidisciplinary group,headed by gastrointestinal surgeons,applied this research philosophy considering the most important aspects of thediseases“colon-and rectal cancer”in the long-term developments.Good expertcooperation/knowledge at the Comprehensive Cancer Center Ulm(CCCU)wereapplied in several phase III trials for multimodal treatments of primary tumors(MMT)and metastatic diseases(involving nearly 2000 patients and 64 centers),fortreatment individualization of MMT and of metastatic disease,for psychooncology/quality of life involving the patients’wishes,and for disease prevention.Most of the targets initially were heavily rejected/discussed in thescientific communities,but now have become standards in treatments andnational guidelines or are topics in modern translational research protocolsinvolving molecular biology for e.g.,“patient centered individualized treatment”.In this context we also describe the paths we had to tread in order to realize ournew goals,which at the end were highly beneficial for the patients from manypoints of view.This description is also important for students and youngresearchers who,with an actual view on our recent developments,might want toknow how medical progress was achieved.

Ageing Society and Assistive Products

North East Asian countries are facing to rapid increase in aged population ratio.The most recent values of aged population ratios are 19.5%,8.7%,and 6.9%,for Japan,Korea and China,respectively.One of the welfare services in the aged society is provision of assistive products.Electronic control systems are commonly adopted in modern assistive products and sensors are indispensable for control units.Alarm systems,such as fire alarm,smoke detectors,and gas leak detectors,have been regarded as indispensable to safety of elderly persons and persons with disability.Main application of chemical sensors in home care of elderly persons is in the field of personal care and personal medical treatment.Products for personal medical treatment include that for medical treatment in home care and that to keep elderly persons healthy.Large market is expected in the latter one.

Sepsis heterogeneity

Background Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection,with extremely high mortality.Notably,sepsis is a heterogeneous syndrome characterized by a vast,multidimensional array of clinical and biologic features,which has hindered advances in the therapeutic field beyond the current standards.Data sources We used PubMed to search the subject-related medical literature by searching for the following single and/or combination keywords:sepsis,heterogeneity,personalized treatment,host response,infection,epidemiology,mortality,incidence,age,children,sex,comorbidities,gene susceptibility,infection sites,bacteria,fungi,virus,host response,organ dysfunction and management.Results We found that host factors(age,biological sex,comorbidities,and genetics),infection etiology,host response dysregulation and multiple organ dysfunctions can all result in different disease manifestations,progression,and response to treatment,which make it difficult to effectively treat and manage sepsis patients.Conclusions Herein,we have summarized contributing factors to sepsis heterogeneity,including host factors,infection etiology,host response dysregulation,and multiple organ dysfunctions,from the key elements of pathogenesis of sepsis.An in-depth understanding of the factors that contribute to the heterogeneity of sepsis will help clinicians understand the complexity of sepsis and enable researchers to conduct more personalized clinical studies for homogenous patients.

Multistage analysis method for detection of effective herb prescription from clinical data

Determining effective traditional Chinese medicine （TCM） treatments for specific disease conditions or particular patient groups is a difficult issue that necessitates investigation because of the complicated personalized manifestations in real-world patients and the individualized combination therapies prescribed in clinical settings. In this study, a multistage analysis method that integrates propensity case matching, complex network analysis, and herb set enrichment analysis was proposed to identify effective herb prescriptions for particular diseases （e.g., insomnia）. First, propensity case matching was applied to match clinical cases. Then, core network extraction and herb set enrichment were combined to detect core effective herb prescriptions. Effectiveness-based mutual information was used to detect strong herb symptom relationships. This method was applied on a TCM clinical data set with 955 patients collected from well-designed observational studies. Results revealed that groups of herb prescriptions with higher effectiveness rates （76.9% vs. 42.8% for matched samples; 94.2% vs. 84.9% for all samples） compared with the original prescriptions were found. Particular patient groups with symptom manifestations were also identified to help investigate the indications of the effective herb prescriptions.

Skin Organoid Research Progress and Potential Applications

Skin diseases were characterized by various types and high incidence,which seriously affect people’s health.At present,skin pathogenesis research and the therapeutic drug development for skin diseases are limited by the lack of reasonable research models that recapitulate the development of skin diseases.Organoids are three-dimensionally cultured cell populations derived from skin stem cells,which exhibits the ability of multicell assembly and the similar histological characteristics with the living tissues and organs.This article reviews the establishment of normal skin organoids and skin tumor organoids,and summarizes the application of skin organoids in the evaluation of drug sensitivity,pathological mechanism research,and individualized treatment.In addition,the advantages and limitations of organoids in skin disease research are also discussed,which provides a basis for revealing the pathogenesis of skin diseases and developing preventive and therapeutic drugs for skin diseases.

Potential diagnostic biomarkers for schizophrenia

Schizophrenia(SCH)is a complex and severe mental disorder with high prevalence,disability,mortality and carries a heavy disease burden,the lifetime prevalence of SCH is around 0.7%–1.0%,which has a profound impact on the individual and society.In the clinical practice of SCH,key problems such as subjective diagnosis,experiential treatment,and poor overall prognosis are still challenging.In recent years,some exciting discoveries have been made in the research on objective biomarkers of SCH,mainly focusing on genetic susceptibility genes,metabolic indicators,immune indices,brain imaging,electrophysiological characteristics.This review aims to summarize the biomarkers that may be used for the prediction and diagnosis of SCH.