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Fazel S, Runeson B. Suicide. N. Engl J Med. 2020;382:26674.

Article PubMed PubMed Central Google Scholar

Bachmann S. Epidemiology of suicide and the psychiatric perspective. Int J Environ Res Public Health. 2018. https://doi.org/10.3390/IJERPH15071425.

Sanderson M, Bulloch AG, Wang JL, Williams KG, Williamson T, Patten SB. Predicting death by suicide following an emergency department visit for parasuicide with administrative health care system data and machine learning. EClinicalMedicine. 2020. https://doi.org/10.1016/j.eclinm.2020.100281.

Walsh CG, Ribeiro JD, Franklin JC. Predicting risk of suicide attempts over time through machine learning. Clin Psychol Sci. 2017;5:45769.

Article Google Scholar

Bauer BW, Law KC, Rogers ML, Capron DW, Bryan CJ. Editorial overview: analytic and methodological innovations for suicide-focused research. Suicide Life Threat Behav. 2021;51:57.

Article PubMed Google Scholar

Gradus JL, Rosellini AJ, Horvth-Puh E, Street AE, Galatzer-Levy I, Jiang T, et al. Prediction of sex-specific suicide risk using machine learning and single-Payer Health Care Registry Data from Denmark. JAMA Psychiatry. 2020;77:2534.

Article PubMed Google Scholar

Voros V, Tenyi T, Nagy A, Fekete S, Osvath P. Crisis concept re-loaded?-The recently described suicide-specific syndromes may help to better understand suicidal behavior and assess imminent suicide risk more effectively. Front Psychiatry. 2021. https://doi.org/10.3389/FPSYT.2021.598923.

Galynker I, Yaseen ZS, Cohen A, Benhamou O, Hawes M, Briggs J. Prediction of suicidal behavior in high risk psychiatric patients using an assessment of acute suicidal state: the suicide crisis inventory. Depress Anxiety. 2017;34:14758.

Article PubMed Google Scholar

Franklin JC, Ribeiro JD, Fox KR, Bentley KH, Kleiman EM, Huang X, et al. Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research. Psychol Bull. 2017;143:187232.

Article PubMed Google Scholar

Beck AT, Steer RA, Kovacs M, Garrison B. Hopelessness and eventual suicide: a 10-year prospective study of patients hospitalized with suicidal ideation. Am J Psychiatry. 1985;142:55963.

Article CAS PubMed Google Scholar

McHugh CM, Large MM. Can machine-learning methods really help predict suicide? Curr Opin Psychiatry. 2020;33:36974.

Article PubMed Google Scholar

Porcelli S, Marsano A, Caletti E, Sala M, Abbiati V, Bellani M, et al. Temperament and character inventory in bipolar disorder versus healthy controls and modulatory effects of 3 key functional gene variants. Neuropsychobiology. 2017;76:20921.

Article CAS PubMed Google Scholar

Grassi M, Perna G, Caldirola D, Schruers K, Duara R, Loewenstein DA. A clinically-translatable machine learning algorithm for the prediction of Alzheimers disease conversion in individuals with mild and premild cognitive impairment. J Alzheimers Dis. 2018;61:155573.

Article Google Scholar

Russak AJ, Chaudhry F, De Freitas JK, Baron G, Chaudhry FF, Bienstock S, et al. Machine learning in cardiology-ensuring clinical impact lives up to the hype. J Cardiovasc Pharm Ther. 2020;25:37990.

Article Google Scholar

Corke M, Mullin K, Angel-Scott H, Xia S, Large M. Meta-analysis of the strength of exploratory suicide prediction models; from clinicians to computers. BJPsych Open. 2021. https://doi.org/10.1192/BJO.2020.162.

Fazel S, OReilly L. Machine learning for suicide research-can it improve risk factor identification? JAMA Psychiatry. 2020;77:1314.

Article PubMed PubMed Central Google Scholar

Boudreaux ED, Rundensteiner E, Liu F, Wang B, Larkin C, Agu E, et al. Applying machine learning approaches to suicide prediction using healthcare data: overview and future directions. Front Psychiatry. 2021. https://doi.org/10.3389/FPSYT.2021.707916.

Jacobson NC, Yom-Tov E, Lekkas D, Heinz M, Liu L, Barr PJ. Impact of online mental health screening tools on help-seeking, care receipt, and suicidal ideation and suicidal intent: evidence from internet search behavior in a large U.S. cohort. J Psychiatr Res. 2022;145:27683.

Article PubMed Google Scholar

Holmstrand C, Bogren M, Mattisson C, Brdvik L. Long-term suicide risk in no, one or more mental disorders: the Lundby Study 19471997. Acta Psychiatr Scand. 2015;132:45969.

Article CAS PubMed PubMed Central Google Scholar

Modai I, Kuperman J, Goldberg I, Goldish M, Mendel S. Suicide risk factors and suicide vulnerability in various major psychiatric disorders. Med Inform Internet Med. 2009;29:6574.

Modai I, Kuperman J, Goldberg I, Goldish M, Mendel S. Fuzzy logic detection of medically serious suicide attempt records in major psychiatric disorders. J Nerv Ment Dis. 2004;192:70810.

Article PubMed Google Scholar

ORourke MC, Siddiqui W. Suicide screening and prevention. StatPearls. 2019. http://www.ncbi.nlm.nih.gov/pubmed/30285348.

McIntyre RS, Berk M, Brietzke E, Goldstein BI, Lpez-Jaramillo C, Kessing LV, et al. Bipolar disorders. Lancet. 2020;396:184156.

Article CAS PubMed Google Scholar

Wiebenga JXM, Dickhoff J, Mrelle SYM, Eikelenboom M, Heering HD, Gilissen R, et al. Prevalence, course, and determinants of suicide ideation and attempts in patients with a depressive and/or anxiety disorder: a review of NESDA findings. J Affect Disord. 2021;283:26777.

Article PubMed Google Scholar

Mitchell SM, Cero I, Littlefield AK, Brown SL. Using categorical data analyses in suicide research: considering clinical utility and practicality. Suicide Life Threat Behav. 2021;51:7687.

Article PubMed PubMed Central Google Scholar

Page MJ, Mckenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021. https://doi.org/10.1136/bmj.n71.

Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015. https://doi.org/10.1136/bmj.g7594.

Tiet QQ, Ilgen MA, Byrnes HF, Moos RH. Suicide attempts among substance use disorder patients: an initial step toward a decision tree for suicide management. Alcohol Clin Exp Res. 2006;30:9981005.

Article PubMed Google Scholar

Jiang T, Rosellini AJ, Horvth-Puh E, Shiner B, Street AE, Lash TL, et al. Using machine learning to predict suicide in the 30 days after discharge from psychiatric hospital in Denmark. Br J Psychiatry. 2021;219:4407.

Parghi N, Chennapragada L, Barzilay S, Newkirk S, Ahmedani B, Lok B, et al. Assessing the predictive ability of the Suicide Crisis Inventory for near-term suicidal behavior using machine learning approaches. Int J Methods Psychiatr Res. 2021. https://doi.org/10.1002/MPR.1863.

McMullen L, Parghi N, Rogers ML, Yao H, Bloch-Elkouby S, Galynker I. The role of suicide ideation in assessing near-term suicide risk: a machine learning approach. Psychiatry Res. 2021. https://doi.org/10.1016/J.PSYCHRES.2021.114118.

Zelkowitz RL, Jiang T, Horvth-Puh E, Street AE, Lash TL, Srensen HT, et al. Predictors of nonfatal suicide attempts within 30 days of discharge from psychiatric hospitalization: sex-specific models developed using population-based registries. J Affect Disord. 2022;306:2608.

Article PubMed PubMed Central Google Scholar

Chen Q, Zhang-James Y, Barnett EJ, Lichtenstein P, Jokinen J, DOnofrio BM, et al. Predicting suicide attempt or suicide death following a visit to psychiatric specialty care: a machine learning study using Swedish national registry data. PLoS Med. 2020. https://doi.org/10.1371/JOURNAL.PMED.1003416.

Tran T, Luo W, Phung D, Harvey R, Berk M, Kennedy RL, et al. Risk stratification using data from electronic medical records better predicts suicide risks than clinician assessments. BMC Psychiatry. 2014. https://doi.org/10.1186/1471-244X-14-76.

Coley RY, Walker RL, Cruz M, Simon GE, Shortreed SM. Clinical risk prediction models and informative cluster size: Assessing the performance of a suicide risk prediction algorithm. Biom J. 2021;63:137588.

Article MathSciNet PubMed PubMed Central Google Scholar

Miranda O, Fan P, Qi X, Yu Z, Ying J, Wang H, et al. DeepBiomarker: identifying important lab tests from electronic medical records for the prediction of suicide-related events among PTSD patients. J Pers Med. 2022;12:524.

Article PubMed PubMed Central Google Scholar

Nock MK, Millner AJ, Ross EL, Kennedy CJ, Al-Suwaidi M, Barak-Corren Y, et al. Prediction of suicide attempts using clinician assessment, patient self-report, and electronic health records. JAMA Netw Open. 2022. https://doi.org/10.1001/JAMANETWORKOPEN.2021.44373.

Edgcomb JB, Thiruvalluru R, Pathak J, Brooks JO. Machine learning to differentiate risk of suicide attempt and self-harm after general medical hospitalization of women with mental illness. Med Care. 2021;59:S58S64.

Article PubMed PubMed Central Google Scholar

Kessler RC, Warner CH, Ivany C, Petukhova MV, Rose S, Bromet EJ, et al. Predicting suicides after psychiatric hospitalization in US army soldiers: the Army study to assess risk and resilience in servicemembers (Army STARRS). JAMA Psychiatry. 2015;72:4957.

Article PubMed PubMed Central Google Scholar

Jordan JT, McNiel DE. Characteristics of a suicide attempt predict who makes another attempt after hospital discharge: a decision-tree investigation. Psychiatry Res. 2018;268:31722.

Article PubMed Google Scholar

Xu Z, Zhang Q, Yip PSF. Predicting post-discharge self-harm incidents using disease comorbidity networks: a retrospective machine learning study. J Affect Disord. 2020;277:4029.

Article PubMed Google Scholar

Niculescu AB, Levey DF, Phalen PL, Le-Niculescu H, Dainton HD, Jain N, et al. Understanding and predicting suicidality using a combined genomic and clinical risk assessment approach. Mol Psychiatry. 2015;20:126685.

Article CAS PubMed PubMed Central Google Scholar

Levey DF, Niculescu EM, Le-Niculescu H, Dainton HL, Phalen PL, Ladd TB, et al. Towards understanding and predicting suicidality in women: biomarkers and clinical risk assessment. Mol Psychiatry. 2016;21:76885.

Article CAS PubMed Google Scholar

Kessler RC, Stein MB, Petukhova MV, Bliese P, Bossarte RM, Bromet EJ, et al. Predicting suicides after outpatient mental health visits in the Army study to assess risk and resilience in servicemembers (Army STARRS). Mol Psychiatry. 2017;22:54451.

Article CAS PubMed Google Scholar

Cook BL, Progovac AM, Chen P, Mullin B, Hou S, Baca-Garcia E. Novel use of natural language processing (NLP) to predict suicidal ideation and psychiatric symptoms in a text-based mental health intervention in Madrid. Comput Math Methods Med. 2016. https://doi.org/10.1155/2016/8708434.

Setoyama D, Kato TA, Hashimoto R, Kunugi H, Hattori K, Hayakawa K, et al. Plasma metabolites predict severity of depression and suicidal ideation in psychiatric patients-a multicenter pilot analysis. PLoS ONE. 2016. https://doi.org/10.1371/journal.pone.0165267.

Chen J, Zhang X, Qu Y, Peng Y, Song Y, Zhuo C, et al. Exploring neurometabolic alterations in bipolar disorder with suicidal ideation based on proton magnetic resonance spectroscopy and machine learning technology. Front Neurosci. 2022. https://doi.org/10.3389/FNINS.2022.944585.

Peis I, Olmos PM, Vera-Varela C, Barrigon ML, Courtet P, Baca-Garcia E, et al. Deep sequential models for suicidal ideation from multiple source data. IEEE J Biomed Heal Inform. 2019;23:228693.

Article Google Scholar

Weng J-C, Lin T-Y, Tsai Y-H, Cheok MT, Chang Y-PE, Chen VC-H. An autoencoder and machine learning model to predict suicidal ideation with brain structural imaging. J Clin Med. 2020;9:658.

Article PubMed PubMed Central Google Scholar

Cusick M, Adekkanattu P, Campion TR, Sholle ET, Myers A, Banerjee S, et al. Using weak supervision and deep learning to classify clinical notes for identification of current suicidal ideation. J Psychiatr Res. 2021;136:95102.

Article PubMed PubMed Central Google Scholar

Ge F, Jiang J, Wang Y, Yuan C, Zhang W. Identifying suicidal ideation among chinese patients with major depressive disorder: evidence from a real-world hospital-based study in China. Neuropsychiatr Dis Treat. 2020;16:66572.

Article PubMed PubMed Central Google Scholar

Tubo-Fungueirio M, Cernadas E, Gonalves F, Segalas C, Bertoln S, Mar-Barrutia L, et al. Viability study of machine learning-based prediction of COVID-19 pandemic impact in obsessive-compulsive disorder patients. Front Neuroinform. 2022. https://doi.org/10.3389/FNINF.2022.807584.

Hong S, Liu YS, Cao B, Cao J, Ai M, Chen J, et al. Identification of suicidality in adolescent major depressive disorder patients using sMRI: a machine learning approach. J Affect Disord. 2021;280:7276.

Article PubMed Google Scholar

Yang J, Palaniyappan L, Xi C, Cheng Y, Fan Z, Chen C, et al. Aberrant integrity of the cortico-limbic-striatal circuit in major depressive disorder with suicidal ideation. J Psychiatr Res. 2022;148:27785.

Article PubMed Google Scholar

Chen S, Zhang X, Lin S, Zhang Y, Xu Z, Li Y, et al. Suicide risk stratification among major depressed patients based on a machine learning approach and whole-brain functional connectivity. J Affect Disord. 2022;322:1739.

Article PubMed Google Scholar

Morales S, Barros J, Echvarri O, Garca F, Osses A, Moya C, et al. Acute mental discomfort associated with suicide behavior in a clinical sample of patients with affective disorders: ascertaining critical variables using artificial intelligence tools. Front Psychiatry. 2017. https://doi.org/10.3389/fpsyt.2017.00007.

Fan P, Guo X, Qi X, Matharu M, Patel R, Sakolsky D, et al. Prediction of suiciderelated events by analyzing electronic medical records from PTSD patients with bipolar disorder. Brain Sci. 2020;10:130.

Article Google Scholar

Shao R, Gao M, Lin C, Huang CM, Liu HL, Toh CH, et al. Multimodal neural evidence on the corticostriatal underpinning of suicidality in late-life depression. Biol Psychiatry Cogn Neurosci Neuroimaging. 2021. https://doi.org/10.1016/J.BPSC.2021.11.011.

Chen VC-H, Wong F-T, Tsai Y-H, Cheok MT, Chang Y-PE, McIntyre RS, et al. Convolutional neural network-based deep learning model for predicting differential suicidality in depressive patients using brain generalized q-sampling imaging. J Clin Psychiatry. 2021. https://doi.org/10.4088/JCP.19M13225.

Xu M, Zhang X, Li Y, Chen S, Zhang Y, Zhou Z, et al. Identification of suicidality in patients with major depressive disorder via dynamic functional network connectivity signatures and machine learning. Transl Psychiatry. 2022. https://doi.org/10.1038/S41398-022-02147-X.

Kumar P, Nestsiarovich A, Nelson SJ, Kerner B, Perkins DJ, Lambert CG. Imputation and characterization of uncoded self-harm in major mental illness using machine learning. J Am Med Inform Assoc. 2020;27:13646.

Article PubMed Google Scholar

Obeid JS, Dahne J, Christensen S, Howard S, Crawford T, Frey LJ, et al. Identifying and predicting intentional self-harm in electronic health record clinical notes: deep learning approach. JMIR Med Informatics. 2020. https://doi.org/10.2196/17784.

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Machine learning and the prediction of suicide in psychiatric populations: a systematic review | Translational Psychiatry - Nature.com

In this video, Glenn T. Werneburg, MD, PhD, discusses the abstracts "Machine learning algorithms demonstrate accurate prediction of objective and patient-reported response to botulinum toxin for overactive bladder and outperform expert humans in an external cohort and "Machine learning algorithms predict urine culture bacterial resistance to first line antibiotic therapy at the time of sample collection, which were presented at the Society of Urodynamics, Female Pelvic Medicine & Urogenital Reconstruction 2024 Winter Meeting in Fort Lauderdale, Florida. Werneburg is a urology resident at Glickman Urological & Kidney Institute at Cleveland Clinic, Cleveland, Ohio.

We'll start with our efforts regarding overactive bladder. Overactive bladder is a common and costly condition. It includes symptoms of urgency, frequency, and urinary incontinence. Patients who don't respond to behavioral therapies or medical management generally require a third-line therapy. That third-line therapy is usually either sacral neuromodulation or onabotulinumtoxinA injection into the bladder. For these patients, many of them would do well with either of these therapies, but a subset would not respond to them. Our goal was to identify which patients would respond and which patients would not respond to these different treatments for medically refractory overactive bladder. To do so, we developed machine learning algorithms. We assembled a team, of course with urologists, but also with quantitative mathematicians, who have lots of experience making predictions regarding stock market fluctuations and things like this. And we developed these algorithms to predict the responder vs the nonresponder status of our patients. We defined how a patient responded based on whether they had a reduction in urge incontinence episodes following the treatment, and whether they perceived an improvement in their symptoms following the therapy. What we found was that we were able to identify patients who responded vs didn't respond to the treatment with a high degree of accuracy. The accuracy surpassed even that of human experts and other standard algorithms. We found this really encouraging. The algorithms even held up in an external validation set, so when we trained the algorithms on 1 data set, and then validated them on a very different group of patients, they still were able to predict who would respond vs not respond to the treatment.

Our other study was a very different application. We were looking at what antibiotics would be optimally suited for which particular patients. One of the major threats to humanity right now is antibiotic resistance. The World Health Organization tells us it's 1 of the top 10 greatest threats. One of the main drivers for antibiotic resistance is overuse and misuse of antibiotics. One of the issues in urology is that a urine culture, which is commonly used to diagnose urinary tract infection, takes 3 days to results. In that time period, the clinician provides the best treatment based on his or her clinical judgment. But we know from the literature that about 30% of the time, this isn't the optimal therapy, and this therapy needs to be changed once the final urine culture results come back. So we set out with a goal to optimally predict which antibiotic would be most suitable for which patient at the time the urine culture is ordered, so it equates to 3 days prior to the final results. The idea was that if we target the therapy very specifically, we can improve our antibiotic usage and improve the time to the resolution of symptoms for our patients. It's beneficial at the patient level and beneficial at the population level. We developed a series of algorithms. We focused on only the most clinically relevant antibiotics. We trained these algorithms on a large data set of our patients - about 6.6 million cases were used. And then we validated them on another set of patients and we tested how they would perform in terms of predicting which cultures would be sensitive and which cultures would be resistant to these different antibiotics. They held up very well. The algorithms' accuracy was really good, and it held up even in an external data set. We find this really encouraging. We have some more validation to do. We're really looking forward to being able to implement these algorithms clinically to improve patient care and also to improve our antibiotic stewardship and reduce our selection for antibiotic resistance at the population level.

This transcription was edited for clarity.

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Machine learning algorithms show applications in OAB, antibiotic resistance - Urology Times

Identification of TRGs and their prognostic value

From the data obtained from the single-cell RNA-seq analyses of OC tissue (GSE184880 dataset), we identified six major types of cells, including T/NK cells, myeloid cells, Epithelial cells, Fibroblasts, B cells and endothelial cells (Fig.2A). Figure2B showed the expression of cell markers. We then extracted T/NK cells for further analysis. As result, T/NK cells could be re-clustered into CD8+ cytotoxic T, CD8+ exhausted T, NK, CD4+ exhausted T and CD4+ nave T based on expression pattern of cell markers (Fig.2C,D). Development trajectory analyses of T/NK cells unveiled that CD4+ nave T, CD8+ cytotoxic T, and NK were enriched in initial differentiation phase while CD4+ exhausted T and CD8+ exhausted T were enriched in terminal differentiation phase (Fig.2E). Based on the FindAllMarkers function of the Seurat package, we identified 384 TRGs. Compared with normal tissues, we obtained 9638 DEGs in OC tissues (Fig.2F), including 248 TRGs (Fig.2G) in TCGA dataset. Among these differentially expressed TRGs, a total of 41 genes were significantly associated with the prognosis of OC patients in TCGA dataset (Fig.2H, P<0.05).

Identification of TRGs and their prognostic value. (A) t-SNE plot showing the identified cell types of from 7 ovarian cancer sample. (B) Dotplot showing average expression levels of cell marker. (C,D) SNE plot of sub-cell types of T cells and dotplot of expression pattern of cell markers. (E) Developmental trajectory of T cells inferred by monocle, colored by pseudotime and cell subtype. (F) Volcano plot showing DEGs in ovarian cancer. (G) Overlap between DEGs and TRGs. (H) Potential biomarkers identified by univariate cox analysis.

These 41 potential prognostic biomarkers were submitted to an integrative machine learning procedure including 10 methods, with which we developed a stable TRPS. As a result, we obtained a total of 101 kinds of prognostic models and their C-index in training and testing cohorts were shown in Fig.3A. The data suggested that the prognostic signature constructed by Enet (alpha=0.3) method was considered as the optimal TRPS with a highest average C-index of 0.58 (Fig.3A). The optimal TRPS was developed by 18 TRGs. The formula of the risk score was shown in Supplementary methods and results. Using the best cut-off value, we then divided into ovarian cancer cases into high and low TRPS score. As expected, OC patients with high risk score had a poor OS rate in TCGA cohort (P<0.001), GSE14764 cohort (P=0.0146), GSE26193 cohort (P=0.0039), GSE26712 cohort (P=0.0013), GSE63885 cohort (P<0.001) and GSE140082 (P=0.0032) cohort (Fig.3BG), with the AUCs of 2-, 3-, and 4-year being 0.728, 0.783, and 0.773 in TCGA cohort; 0.629, 0.642, and 0.739 in GSE14764 cohort; 0.617, 0.644, and 0.616 in GSE26193 cohort; 0.607, 0.587, and 0.591 in GSE26712 cohort, 0.672, 0.646 and 0.721 in GSE63885 cohort, 0.608 and 0.617 in GSE140082 cohort, respectively (Fig.3BG).

Identification of TRPS by machine learning. (A) The C-index of 101 kinds prognostic models constructed by 10 machine learning algorithms in training and testing cohort. (BG) The survival curve of ovarian cancer patients with different TRPS score and their corresponding ROC curve in TCGA, GSE14764, GSE26193, GSE26172, GSE63885 and GSE140082 cohort.

To compare the performance of TRPS with other prognostic signatures in predicting the OS rate of OC cases, we randomly collected 45 OC-related prognostic signatures (Supplementary Table 1) and calculated their C-index. As a result, the C-index of TRPS was higher than most of these prognostic signatures in TCGA dataset (Fig.4A). Moreover, the C-index of TRPS was higher than that of tumor grade and clinical stage in training and testing cohorts (Fig.4BF). These evidences suggested that the predictive value of TRPS in predicting the clinical outcome of OC patients was higher than most of signatures and clinical characters. However, we could not evaluate the predictive value of TRPS in predicting the OS rate of OC patients in GSE26712 cohort due to the missing data of tumor grade and clinical stage. Based on the result of univariate and multivariate cox regression analysis, TRPS served as an independent risk factor for the clinical outcome of OC patients in TCGA, GSE14764, GSE26193, GSE63885 and GSE140082 cohort (Fig.4G,H, all P<0.05). To predict the 1-year, 3-year and 5-year OS rate of OC patients, we then constructed a nomogram based on TRPS, clinical stage and tumor grade using TCGA dataset (Fig.4I). The comparison between the predicted curve and the ideal curve showed a high coincidence in TCGA dataset (Fig.4J). Compared with TPRS, clinical stage and tumor grade, the AUC of nomogram were higher in TCGA dataset (Fig.4K).

Evaluation the performance of TRPS in predicting prognosis of OC patients. (A) C-index of TRPS and other 45 established signatures in predicting the prognosis of OC patients. (BF) The C-index of TRPS, tumor grade and clinical stage in predicting prognosis of OC patients in TCGA, GSE14764, GSE26193, GSE63885 and GSE140082 cohort. (G,H) Univariate and multivariate cox regression analysis considering grade, stage and TRPS in training and testing cohort. (I,J) Predictive nomogram and calibration evaluating the 1-y, 3-y and 5-y overall survival rate of OC patients. (K) ROC curve evaluated the performance of nomogram in predicting prognosis of OC patients.

As shown in Fig.5A, TRPS showed significant correlation with the abundance of immune cells in TCGA dataset (all P<0.05). More specifically, TRPS showed a negative correlation with immuno-activated cell infiltration, such as CD8+ T cells, plasma cells, macrophage M1 and NK cells in TCGA dataset (Fig.5BE, all P<0.05). Interestingly, higher risk score indicated a higher level of cancer-related fibroblasts in TCGA dataset (Fig.5F). Similar results were obtained in ssGSEA analysis, suggesting a higher abundance of immuno-activated cells in low risk score group, including aDCs, B cells, CD8+ T cells, Neutrophils, NK cells, Tfh and TIL in TCGA dataset (Fig.5G, all P<0.05). Previous studies showed that macrophage M2/M1 polarization played a vital role in the progression of cancer9,10. Our study showed that OC patients with high risk score had a higher macrophage M2/M1 polarization in TCGA, GSE26712, and GSE140082 cohort (Fig.5H, all P<0.05). Further analysis suggested a higher stromal score, immune score and ESTIMAE score in low risk score group in TCGA dataset (Fig.5I, all P<0.001). Moreover, higher risk score indicated a higher APC co-stimulation score, CCR score, cytolytic activity score, para-inflammation promoting score, parainflammation and T cell co-stimulation score in TCGA dataset (Fig.5J).

Correlation between immune microenvironment and TRPS in OC. (A) Seven state-of-the-art algorithms evaluating the correlation between TRPS and immune cell infiltration in OC. (BF) The correlation between TRPS and the abundance of CD8+ T cells, plasma cells, macrophage M1 and CAFs. (G) The level of immune cells in different TRPS score group based on ssGSEA analysis. (H) The macrophage M2/M1 ratio in different TRPS score group in TCGA, GSE26712 and GSE140082 dataset. (I,J) The stromal score, immune score, ESTIMAE score and immune-related functions score in different TRPS score group. *P<0.05, **P<0.01, ***P<0.001.

High HLA-related gene expression indicated wider range of antigen presentation, increasing the likelihood of presenting more immunogenic antigens, and the likelihood of benefiting from immunotherapy11. We found that OC patients with low risk score had a higher HLA-related genes in TCGA dataset (Fig.6A, all P<0.05). Immune checkpoints played a vital role in immune escape of cancer. Based on our results, the expression of most of immune checkpoints was higher in high risk score groups in OC in TCGA dataset (Fig.6B, all P<0.05). Previous study showed that high TMB score was correlated with a better response to immunotherapy12. IPS was a superior predictor of response to anti-CTLA-4 and anti-PD-1 antibody and high IPS indicated a better response to immunotherapy13. High TIDE score indicated a greater likelihood of immune escape and less effectiveness of ICI treatment14. As showed in Fig.6CF, OC patients with low risk score had a higher TMB score, higher PD1 immunophenoscore, CTLA4 immunophenoscore, and PD1&CTLA4 immunophenoscore, lower immune escape score, lower TIDE score, lower T cell exclusion and dysfunction score in TCGA dataset. Thus, OC patients with low risk score may have a better immunotherapy benefit. To further verify the predictive value of TRPS in immunotherapy benefits, we then applied two immunotherapy cohorts to further verify our results. As shown in Fig.6G, the risk score in non-responders was significantly higher than that in responders in IMvigor210 cohort (P<0.01). Moreover, high risk score indicated a poor clinical outcome and lower response rate in IMvigor210 cohort (Fig.6G). Similar results were obtained in GSE91061 cohort (Fig.6H). As the vital role of chemotherapy, targeted therapy and endocrinotherapy for the treatment of OC, we also detected the IC50 value of common drugs in OC patients. We found that the IC50 value of 5-Fluorouracil, Camptothecin, Cisplatin, Gemcitabine, Foretunib, KRAS inhibitor, Erlotinib, and Tamoxifen were higher in in OC patients with high risk score in TCGA dataset (Fig.7A, all P<0.05). Moreover, positive correlation was obtained between risk score and these drugs in TCGA dataset (Fig.7B). Thus, OC patients with low risk score may be better sensitivity to chemotherapy and targeted therapy.

TRPS as an indicator for immunotherapy response in OC. (A,B) The level of HLA-related genes and immune checkpoints in different TRPS score group. (BF) The TMB score, immunophenoscore, immune escape score and TIDE, T cell dysfunction and exclusion score in different TRPS score group. (G,H) The overall rate and immunotherapy response rate in patients with high and low risk score in GSE91061 and IMvigor210 cohort. *P<0.05, **P<0.01, ***P<0.001.

The IC50 value of common drugs in different TRPS score group. (A) Low risk score indicated a lower IC50 value of common drugs. (B) The correlation between IC50 value of common drugs and TRPS score.

We finally performed gene set enrichment analysis to explore the potential mechanism mediating the difference of OC patients in clinical outcome, immune infiltration, and therapy response. High risk score indicated a higher sore of angiogenesis, DNA repair, EMT, G2M checkpoint, glycolysis, hypoxia, IL2-STAT5 signaling, IL6-JAK-STAT3 signaling, MTORC1 signaling, NOTCH signaling, P53 pathway, and P13K-AKT-mTOR signaling in OC in TCGA dataset (Fig.8AL, all P<0.05).

Gene set enrichment analysis in different TRPS score group. High risk score indicated a higher score of angiogenesis (A), DNA repair (B), EMT (C), G2M checkpoint (D), glycolysis (E), hypoxia (F), IL2-STAT5 signaling (G), IL6-JAK-STAT3 signaling (H), MTORC1 signaling (I), NOTCH signaling (J), P53 pathway (K), and P13K-AKT-mTOR signaling (L).

To further verify the performance of TRPS, we selected ARL6IP5 that contributed the most to the TRPS for further analysis. We first examined the expression of ARL6IP5 in OC cell lines, which showed that the expression of ARL6IP5 was lower in OC cell lines (Fig.9A). Typical immunohistochemical of ARL6IP5 in OC and normal tissues were showed in Fig.9B. In the follow-up study, the results of the CCK-8 assay proved that overexpression of ARL6IP5 obviously inhibited the proliferation of SKOV3 and TOV21G (Fig.9C,D).

Validation of the potential function of ARL6IP5 in OC by in vitro assays. (A) Comparison of ARL6IP5 expressions in normal and OC cell lines. (B) Typical immunohistochemical of ARL6IP5 in OC and normal tissues. (C,D) CCK-8 assay showed that overexpression of ARL6IP5 obviously inhibited the proliferation of SKOV3 and TOV21G cells. *P<0.05, **P<0.01.

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Machine learning developed a CD8+ exhausted T cells signature for predicting prognosis, immune infiltration and drug ... - Nature.com

Best-fit transit models for all 8 of the visible transits within Kepler of KOI 1271.01. Overlaid with the models is a scatter plot of the normalized flux values for the 4-day window with the associated error bars given by Kepler. The models were found using EXOPLANET, and the only parameter that was fit for was the time of center transit. The title of each subplot is the epoch number along with the best-fit time of the transit center. We fit the models over a 4-day range around the predicted time of transit using the ephemeris of KOI 1271.01. Therefore, the location of the transit within the window gives a hint of the order of magnitude of the epochs TTV. astro-ph.EP

Exoplanet discovery at long orbital periods requires reliably detecting individual transits without additional information about the system. Techniques like phase-folding of light curves and periodogram analysis of radial velocity data are more sensitive to planets with shorter orbital periods, leaving a dearth of planet discoveries at long periods.

We present a novel technique using an ensemble of Convolutional Neural Networks incorporating the onboard spacecraft diagnostics of Kepler to classify transits within a light curve. We create a pipeline to recover the location of individual transits, and the period of the orbiting planet, which maintains >80% transit recovery sensitivity out to an 800-day orbital period.

Our neural network pipeline has the potential to discover additional planets in the Kepler dataset, and crucially, within the -Earth regime. We report our first candidate from this pipeline, KOI 1271.02. KOI 1271.01 is known to exhibit strong Transit Timing Variations (TTVs), and so we jointly model the TTVs and transits of both transiting planets to constrain the orbital configuration and planetary parameters and conclude with a series of potential parameters for KOI 1271.02, as there is not enough data currently to uniquely constrain the system.

We conclude that KOI 1271.02 has a radius of 5.32 0.20 R and a mass of 28.940.230.47 M. Future constraints on the nature of KOI 1271.02 require measuring additional TTVs of KOI 1271.01 or observing a second transit of KOI 1271.02.

Matthew T. Hansen, Jason A. Dittmann

Comments: 23 pages, 23 figures, submitted to AJ Subjects: Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG) Cite as: arXiv:2403.03427 [astro-ph.EP] (or arXiv:2403.03427v1 [astro-ph.EP] for this version) Submission history From: Matthew Hansen [v1] Wed, 6 Mar 2024 03:16:47 UTC (1,474 KB) https://arxiv.org/abs/2403.03427 Astrobiology,

Explorers Club Fellow, ex-NASA Space Station Payload manager/space biologist, Away Teams, Journalist, Lapsed climber, Synaesthete, NaVi-Jedi-Freman-Buddhist-mix, ASL, Devon Island and Everest Base Camp veteran, (he/him)

Originally posted here:
Single Transit Detection In Kepler With Machine Learning And Onboard Spacecraft Diagnostics - Astrobiology - Astrobiology News

Acknowledgments: Many thanks to the NIA AI Working Group members for their contributions to this blog post.

Artificial intelligence (AI) the science of computer systems that can mimic human-like thinking and decision-making processes has continued to evolve since our 2022 blog on this topic. With that growth comes added fascination for AIs possibilities and caution about its potential pitfalls.

Beyond the headlines, the aging science community is most excited about how AI and its related field of machine learning (ML) can turbocharge tools and models to accelerate research in Alzheimers disease and related dementias as well as other complex health challenges.

As NIA continues to expand its portfolio of AI/ML initiatives, be sure to check out our latest funding opportunity on multi-scale computational models in aging and Alzheimers (RFA-AG-25-016) with an application deadline of June 13, 2024. This RFA encompasses a variety of computational approaches such as mathematical and computational modeling, image analysis, AI, and ML to better understand aging processes and Alzheimers and related dementias across molecules, cells, and cellular networks, and how they affect cognition and behavior.

If youre interested in learning more, the NIH Center for Alzheimers and Related Dementias (CARD) has numerous training opportunities, open-access resources, and tools to help investigators take advantage of AI and ML capabilities. For example, GenoML, an open-source project created by CARD staff and collaborators, offers a streamlined approach to machine learning in genomics and has been downloaded more than 15,000 times since its launch.

NIA also participates in broad efforts to advance cutting-edge AI research in partnership with other federal and international funders through programs such as:

NIA recognizes the transformative potential of AI in analyzing complex datasets, accelerating the understanding of Alzheimers pathology, and identifying novel treatment avenues. Together, we hope these advanced tools and methods will help us better understand the aging process and find a cure for dementia and other age-related diseases.

To be a part of the next chapter, apply for the latest multi-scale computational models in aging and Alzheimers funding opportunity by June 13. To learn more, visit theNIA AI page. As always, we invite comments below!

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Putting the AI in NIA: New opportunities in artificial intelligence - National Institute on Aging