diff --git a/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv b/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv
index b05dc06b7c..9113d79eb9 100644
--- a/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv
+++ b/apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv
@@ -43,20 +43,20 @@
 "42","beat-pd","BEAT-PD","Develop mobile sensors to remotely monitor Parkinson's disease for BEAT-PD","Recent advances in mobile health have demonstrated great potential to leverage sensor-based technologies for quantitative, remote monitoring of health and disease-particularly for diseases affecting motor function such as Parkinson's disease. Such approaches have been rolled out using research-grade wearable sensors and, increasingly, through the use of smartphones and consumer wearables, such as smart watches and fitness trackers. These devices not only provide the ability to measure much more detailed disease phenotypes but also provide the ability to follow patients longitudinally with much higher frequency than is possible through clinical exams. However, the conversion of sensor-based data streams into digital biomarkers is complex and no methodological standards have yet evolved to guide this process.  Parkinson's disease (PD) is a neurodegenerative disease that primarily affects the motor system but also exhibits other symptoms. Typical motor symptoms of the disease include...","","https://www.synapse.org/#!Synapse:syn20825169","completed","intermediate","1","","2020-01-13","2020-05-13","2023-06-23 00:00:00","2023-10-14 05:38:45"
 "43","ctd2-pancancer-chemosensitivity","CTD2 Pancancer Chemosensitivity","Predict drug sensitivity from cell line gene expression for CTD2 Pancancer C...","Over the last two years, the Columbia CTD2 Center developed PANACEA (Pancancer Analysis of Chemical Entity Activity), a comprehensive repertoire of dose response curves and molecular profiles representative of cellular responses to drug perturbations. PANACEA covers a broad spectrum of cellular contexts representative of poor outcome malignancies, including rare ones such as GIST sarcoma and gastroenteropancreatic neuroendocrine tumors (GEP-NETs). PANACEA is uniquely suited to support DREAM Challenges related to the elucidation of drug mechanism of action (MOA), drug sensitivity, and drug synergy.  The goal of this Challenge is to foster development and benchmarking of algorithms to predict the sensitivity, as measured by the area under the dose-response curve, of a cell line to a compound based on the baseline transcriptional profiles of the cell line. The drug perturbational RNAseq profiles of 11 cell lines for 30 chosen compounds will be provided to challenge participants, with...","","https://www.synapse.org/#!Synapse:syn21763589","completed","good_for_beginners","1","","2020-04-28","2020-07-27","2023-06-23 00:00:00","2023-10-14 05:38:45"
 "44","ehr-dream-challenge-covid-19","EHR DREAM Challenge: COVID-19","Develop tools to predict COVID-19 risk without sharing data for EHR DREAM Ch...","The rapid rise of COVID-19 has challenged healthcare globally. The underlying risks and outcomes of infection are still incompletely characterized even as the world surpasses 4 million infections. Due to the importance and emergent need for better understanding of the condition and the development of patient specific clinical risk scores and early warning tools, we have developed a platform to support testing analytic and machine learning hypotheses on clinical data without data sharing as a platform to rapidly discover and implement approaches for care. We have previously applied this approach in the successful EHR DREAM Challenge focusing on Patient Mortality Prediction with UW Medicine.    We have the goal of incorporating machine learning and predictive algorithms into clinical care and COVID-19 is an important and highly urgent challenge. In our first iteration, we will facilitate understanding risk factors that lead to a positive test utilizing electronic health recorded dat...","","https://www.synapse.org/#!Synapse:syn21849255","completed","intermediate","1","https://doi.org/10.1001/jamanetworkopen.2021.24946","2020-04-30","2021-07-01","2023-06-23 00:00:00","2023-11-01 14:57:29"
-"45","anti-pd1-response-prediction","Anti-PD1 Response Prediction","Anti-PD1 Response Prediction: 1. Predicting lung cancer response to immuno-o...","While durable responses and prolonged survival have been demonstrated in some lung cancer patients treated with immuno-oncology (I-O) anti-PD-1 therapy, there remains a need to improve the ability to predict which patients are more likely to receive benefit from treatment with I-O. The goal of this challenge is to leverage clinical and biomarker data to develop predictive models of response to I-O therapy in lung cancer and ultimately gain insights that may facilitate potential novel monotherapies or combinations with I-O.","","https://www.synapse.org/#!Synapse:syn18404605","completed","intermediate","1","","2020-11-17","2021-02-25","2023-06-23 00:00:00","2023-11-02 18:25:16"
-"46","brats-2021-challenge","BraTS 2021 Challenge","BraTS 2021 Challenge: 1. Developing ML methods to analyze brain tumor MRI scans","Glioblastoma, and diffuse astrocytic glioma with molecular features of glioblastoma (WHO Grade 4 astrocytoma), are the most common and aggressive malignant primary tumor of the central nervous system in adults, with extreme intrinsic heterogeneity in appearance, shape, and histology. Glioblastoma patients have very poor prognosis, and the current standard of care treatment comprises surgery, followed by radiotherapy and chemotherapy. The International Brain Tumor Segmentation (BraTS) Challenges —which have been running since 2012— assess state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans.","","https://www.synapse.org/#!Synapse:syn25829067","completed","advanced","1","","2021-07-07","2021-10-15","2023-06-23 00:00:00","2023-10-14 05:38:48"
-"47","cancer-data-registry-nlp","Cancer Data Registry NLP","Cancer Data Registry NLP: 1. Unlocking Clinical Trial Data Hidden in Medical...","A critical bottleneck in translational and clinical research is access to large volumes of high-quality clinical data. While structured data exist in medical EHR systems, a large portion of patient information including patient status, treatments, and outcomes is contained in unstructured text fields. Research in Natural Language Processing (NLP) aims to unlock this hidden and often inaccessible information. However, numerous challenges exist in developing and evaluating NLP methods, much of it centered on having “gold-standard” metrics for evaluation, and access to data that may contain personal health information (PHI).    This DREAM Challenge will focus on the development and evaluation of of NLP algorithms that can improve clinical trial matching and recruitment.","","https://www.synapse.org/#!Synapse:syn18361217","upcoming","intermediate","1","","\N","\N","2023-06-23 00:00:00","2023-10-14 05:38:49"
-"48","barda-community-challenge-pediatric-covid-19-data-challenge","BARDA Community Challenge - Pediatric COVID-19 Data Challenge","BARDA Community Challenge - Pediatric COVID-19 Data Challenge: 1. Models to ...","While most children with COVID-19 are asymptomatic or have mild symptoms, healthcare providers have difficulty determining which among their pediatric patients will progress to moderate or severe COVID-19 early in the progression. Some of these patients develop multisystem inflammatory syndrome in children (MIS-C), a life-threatening inflammation of organs and tissues. Methods to distinguish children at risk for severe COVID-19 complications, including conditions such as MIS-C, are needed for earlier interventions to improve pediatric patient outcomes.  Multiple HHS divisions are coming together for a data challenge competition that will leverage de-identified electronic health record data to develop, train and validate computational models that can predict severe COVID-19 complications in children, equipping healthcare providers with the information and tools they need to identify pediatric patients at risk.","","https://www.synapse.org/#!Synapse:syn25875374/wiki/611225","completed","intermediate","1","","2021-08-19","2021-12-17","2023-06-23 00:00:00","2023-10-14 05:38:50"
-"49","brats-continuous-evaluation","BraTS Continuous Evaluation","BraTS Continuous Evaluation: 1. Seeking Innovations To Improve Brain Tumor D...","Brain tumors are among the deadliest types of cancer. Specifically, glioblastoma, and diffuse astrocytic glioma with molecular features of glioblastoma (WHO Grade 4 astrocytoma), are the most common and aggressive malignant primary tumor of the central nervous system in adults, with extreme intrinsic heterogeneity in appearance, shape, and histology, with a median survival of approximately 15 months. Brain tumors in general are challenging to diagnose, hard to treat and inherently resistant to conventional therapy because of the challenges in delivering drugs to the brain, as well as the inherent high heterogeneity of these tumors in their radiographic, morphologic, and molecular landscapes. Years of extensive research to improve diagnosis, characterization, and treatment have decreased mortality rates in the U.S by 7% over the past 30 years. Although modest, these research innovations have not translated to improvements in survival for adults and children in low-and middle-income...","","https://www.synapse.org/brats_ce","completed","advanced","1","","2022-01-01","\N","2023-06-23 00:00:00","2023-10-14 05:38:51"
+"45","anti-pd1-response-prediction","Anti-PD1 Response Prediction","Predicting lung cancer response to immuno-oncology therapy","While durable responses and prolonged survival have been demonstrated in some lung cancer patients treated with immuno-oncology (I-O) anti-PD-1 therapy, there remains a need to improve the ability to predict which patients are more likely to receive benefit from treatment with I-O. The goal of this challenge is to leverage clinical and biomarker data to develop predictive models of response to I-O therapy in lung cancer and ultimately gain insights that may facilitate potential novel monotherapies or combinations with I-O.","","https://www.synapse.org/#!Synapse:syn18404605","completed","intermediate","1","","2020-11-17","2021-02-25","2023-06-23 00:00:00","2023-11-02 18:25:16"
+"46","brats-2021-challenge","BraTS 2021 Challenge","Developing ML methods to analyze brain tumor MRI scans","Glioblastoma, and diffuse astrocytic glioma with molecular features of glioblastoma (WHO Grade 4 astrocytoma), are the most common and aggressive malignant primary tumor of the central nervous system in adults, with extreme intrinsic heterogeneity in appearance, shape, and histology. Glioblastoma patients have very poor prognosis, and the current standard of care treatment comprises surgery, followed by radiotherapy and chemotherapy. The International Brain Tumor Segmentation (BraTS) Challenges —which have been running since 2012— assess state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans.","","https://www.synapse.org/#!Synapse:syn25829067","completed","advanced","1","","2021-07-07","2021-10-15","2023-06-23 00:00:00","2023-10-14 05:38:48"
+"47","cancer-data-registry-nlp","Cancer Data Registry NLP","Predicting lung cancer response to immuno-oncology therapy","A critical bottleneck in translational and clinical research is access to large volumes of high-quality clinical data. While structured data exist in medical EHR systems, a large portion of patient information including patient status, treatments, and outcomes is contained in unstructured text fields. Research in Natural Language Processing (NLP) aims to unlock this hidden and often inaccessible information. However, numerous challenges exist in developing and evaluating NLP methods, much of it centered on having “gold-standard” metrics for evaluation, and access to data that may contain personal health information (PHI).    This DREAM Challenge will focus on the development and evaluation of of NLP algorithms that can improve clinical trial matching and recruitment.","","https://www.synapse.org/#!Synapse:syn18361217","upcoming","intermediate","1","","\N","\N","2023-06-23 00:00:00","2023-10-14 05:38:49"
+"48","barda-community-challenge-pediatric-covid-19-data-challenge","BARDA Community Challenge - Pediatric COVID-19 Data Challenge","Models to predict severe COVID-19 in children sought","While most children with COVID-19 are asymptomatic or have mild symptoms, healthcare providers have difficulty determining which among their pediatric patients will progress to moderate or severe COVID-19 early in the progression. Some of these patients develop multisystem inflammatory syndrome in children (MIS-C), a life-threatening inflammation of organs and tissues. Methods to distinguish children at risk for severe COVID-19 complications, including conditions such as MIS-C, are needed for earlier interventions to improve pediatric patient outcomes.  Multiple HHS divisions are coming together for a data challenge competition that will leverage de-identified electronic health record data to develop, train and validate computational models that can predict severe COVID-19 complications in children, equipping healthcare providers with the information and tools they need to identify pediatric patients at risk.","","https://www.synapse.org/#!Synapse:syn25875374/wiki/611225","completed","intermediate","1","","2021-08-19","2021-12-17","2023-06-23 00:00:00","2023-10-14 05:38:50"
+"49","brats-continuous-evaluation","BraTS Continuous Evaluation","Seeking Innovations To Improve Brain Tumor Diagnosis And Treatment","Brain tumors are among the deadliest types of cancer. Specifically, glioblastoma, and diffuse astrocytic glioma with molecular features of glioblastoma (WHO Grade 4 astrocytoma), are the most common and aggressive malignant primary tumor of the central nervous system in adults, with extreme intrinsic heterogeneity in appearance, shape, and histology, with a median survival of approximately 15 months. Brain tumors in general are challenging to diagnose, hard to treat and inherently resistant to conventional therapy because of the challenges in delivering drugs to the brain, as well as the inherent high heterogeneity of these tumors in their radiographic, morphologic, and molecular landscapes. Years of extensive research to improve diagnosis, characterization, and treatment have decreased mortality rates in the U.S by 7% over the past 30 years. Although modest, these research innovations have not translated to improvements in survival for adults and children in low-and middle-income...","","https://www.synapse.org/brats_ce","completed","advanced","1","","2022-01-01","\N","2023-06-23 00:00:00","2023-10-14 05:38:51"
 "50","fets-2022","FeTS 2022","Federated Learning Challenge 2022: Advancing Brain Tumor Segmentation Algorithms","FeTS 2022 focuses on benchmarking methods for federated learning (FL), and particularly i) weight aggregation methods for federated training, and ii) algorithmic generalizability on out-of-sample data based on federated evaluation. In line with its last instance (FeTS 2021-the 1st FL challenge ever organized), FeTS 2022 targets the task of brain tumor segmentation and builds upon i) the centralized dataset of >8,000 clinically-acquired multi-institutional MRI scans (from the RSNA-ASNR-MICCAI BraTS 2021 challenge) with their real-world partitioning, and ii) the collaborative network of remote independent institutions included in a real-world federation. Participants are welcome to compete in either of the two challenge tasks- Task 1 (“Federated Training”) seeks effective weight aggregation methods for the creation of a consensus model given a pre-defined segmentation algorithm for training, while also (optionally) accounting for network outages.  Task 2 (“Federated Evaluation”) see...","","https://www.synapse.org/#!Synapse:syn28546456/wiki/617093","completed","advanced","1","","2022-04-08","2022-08-15","2023-06-23 00:00:00","2023-10-18 00:36:14"
 "51","random-promotor","Random Promotor","Deciphering Gene Regulation: Training Models to Predict Gene Expression Patterns","Decoding how gene expression is regulated is critical to understanding disease. Regulatory DNA is decoded by the cell in a process termed “cis-regulatory logic”, where proteins called Transcription Factors (TFs) bind to specific DNA sequences within the genome and work together to produce as output a level of gene expression for downstream adjacent genes. This process is exceedingly complex to model as a large number of parameters is needed to fully describe the process (see Rationale, de Boer et al. 2020; Zeitingler J. 2020).  Understanding the cis-regulatory logic of the human genome is an important goal and would provide insight into the origins of many diseases. However, learning models from human data is challenging due to limitations in the diversity of sequences present within the human genome (e.g. extensive repetitive DNA), the vast number of cell types that differ in how they interpret regulatory DNA, limited reporter assay data, and substantial technical biases present ...","","https://www.synapse.org/#!Synapse:syn28469146/wiki/617075","completed","intermediate","1","","2022-05-02","2022-08-07","2023-06-23 00:00:00","2023-10-14 05:38:53"
-"52","preterm-birth-prediction-microbiome","Preterm Birth Prediction - Microbiome","Preterm Birth Prediction - Microbiome: 1. Predict preterm births to reduce i...","Globally, about 11% of infants every year are born preterm, defined as birth prior to 37 weeks of gestation, totaling nearly 15 million births.(5) In addition to the emotional and financial toll on families, preterm births have higher rates of neonatal death, nearly 1 million deaths each year, and long-term health consequences for some children. Infants born preterm are at risk for a variety of adverse outcomes, such as respiratory illnesses, cerebral palsy, infections, and blindness, with infants born very preterm (i.e., before 32 weeks) at increased risk of these conditions.(6) The ability to accurately predict which women are at a higher risk for preterm birth would help healthcare providers to treat in a timely manner those at higher risk of delivering preterm. Currently available treatments for pregnant women at risk of preterm delivery include corticosteroids for fetal maturation and magnesium sulfate provided prior to 32 weeks to prevent cerebral palsy.(7) There are several...","","https://www.synapse.org/#!Synapse:syn26133770/wiki/612541","completed","advanced","1","","2022-07-19","2022-09-16","2023-06-23 00:00:00","2023-10-14 05:38:54"
+"52","preterm-birth-prediction-microbiome","Preterm Birth Prediction - Microbiome","Seeking Innovations To Improve Brain Tumor Diagnosis And Treatment","Globally, about 11% of infants every year are born preterm, defined as birth prior to 37 weeks of gestation, totaling nearly 15 million births.(5) In addition to the emotional and financial toll on families, preterm births have higher rates of neonatal death, nearly 1 million deaths each year, and long-term health consequences for some children. Infants born preterm are at risk for a variety of adverse outcomes, such as respiratory illnesses, cerebral palsy, infections, and blindness, with infants born very preterm (i.e., before 32 weeks) at increased risk of these conditions.(6) The ability to accurately predict which women are at a higher risk for preterm birth would help healthcare providers to treat in a timely manner those at higher risk of delivering preterm. Currently available treatments for pregnant women at risk of preterm delivery include corticosteroids for fetal maturation and magnesium sulfate provided prior to 32 weeks to prevent cerebral palsy.(7) There are several...","","https://www.synapse.org/#!Synapse:syn26133770/wiki/612541","completed","advanced","1","","2022-07-19","2022-09-16","2023-06-23 00:00:00","2023-10-14 05:38:54"
 "53","finrisk","FINRISK - Heart Failure and Microbiome","FINRISK - Heart Failure and Microbiome: (No headline provided)","Cardiovascular diseases are the leading cause of death both in men and women worldwide. Heart failure (HF) is the most common form of heart disease, characterised by the heart's inability to pump a sufficient supply of blood to meet the needs of the body. The lifetime risk of developing HF is roughly 20%, yet, it remains difficult to diagnose due to its and a lack of agreement of diagnostic criteria. As the diagnosis of HF is dependent on ascertainment of clinical histories and appropriate screening of symptomatic individuals, identifying those at risk of HF is essential. This DREAM challenge focuses on the prediction of HF using a combination of gut microbiome and clinical variables. This challenge is designed to predict incident risk for heart failure in a large human population study of Finnish adults, FINRISK 2002 (Borodulin et al., 2018). The FINRISK study has been conducted in Finland to investigate the risk factors for cardiovascular disease every 5 years since 1972. A rand...","","https://www.synapse.org/#!Synapse:syn27130803/wiki/616705","completed","advanced","1","","2022-09-20","2023-01-30","2023-06-23 00:00:00","2023-10-16 21:19:55"
-"54","scrna-seq-and-scatac-seq-data-analysis","scRNA-seq and scATAC-seq Data Analysis","scRNA-seq and scATAC-seq Data Analysis: 1. Assess computational methods for ...","Understanding transcriptional regulation at individual cell resolution is fundamental to understanding complex biological systems such as tissues and organs. Emerging high-throughput sequencing technologies now allow for transcript quantification and chromatin accessibility at the single cell level. These technologies present unique challenges due to inherent data sparsity. Proper signal correction is key to accurate gene expression quantification via scRNA-seq, which propagates into downstream analyses such as differential gene expression analysis and cell-type identification. In the even more sparse scATAC-seq data, the correct identification of informative features is key to assessing cell heterogeneity at the chromatin level. The aims of this challenge will be two-fold- 1) To evaluate computational methods for signal correction and peak identification in scRNA-seq and scATAC-seq, respectively;  2) To assess the impact of these methods on downstream analysis","","https://www.synapse.org/#!Synapse:syn26720920/wiki/615338","completed","advanced","1","","2022-11-29","2023-02-08","2023-06-23 00:00:00","2023-10-14 05:38:56"
-"55","cough-diagnostic-algorithm-for-tuberculosis","COugh Diagnostic Algorithm for Tuberculosis","COugh Diagnostic Algorithm for Tuberculosis: 1. Develop low-cost cough scree...","Tuberculosis (TB), a communicable disease caused by Mycobacterium tuberculosis, is a major cause of ill health and one of the leading causes of death worldwide. Until the COVID-19 pandemic, TB was the leading cause of death from a single infectious agent, ranking even above HIV/AIDS. In 2020, an estimated 9.9 million people fell ill with TB and 1.3 million died of TB worldwide. However, approximately 40% of people with TB were not diagnosed or reported to public health authorities because of challenges in accessing health facilities or failure to be tested or treated when they do. The development of low-cost, non-invasive digital screening tools may improve some of the gaps in diagnosis. As cough is a common symptom of TB, it has the potential to be used as a biomarker for diagnosis of disease. Several previous studies have demonstrated the potential for cough sounds to be used to screen for TB[1-3], though these were typically done in small samples or limited settings. Further de...","","https://www.synapse.org/#!Synapse:syn31472953/wiki/617828","completed","advanced","1","","2022-10-16","2023-02-13","2023-06-23 00:00:00","2023-10-14 05:38:57"
-"56","nih-long-covid-computational-challenge","NIH Long COVID Computational Challenge","NIH Long COVID Computational Challenge: 1. Understanding Prevalence and Outc...","The overall prevalence of post-acute sequelae of SARS-CoV-2 (PASC) is currently unknown, but there is growing evidence that more than half of COVID-19 survivors experience at least one symptom of PASC/Long COVID at six months after recovery of the acute illness. Reports also reflect an underlying heterogeneity of symptoms, multi-organ involvement, and persistence of PASC/Long COVID in some patients. Research is ongoing to understand prevalence, duration, and clinical outcomes of PASC/Long COVID. Symptoms of fatigue, cognitive impairment, shortness of breath, and cardiac damage, among others, have been observed in patients who had only mild initial disease. The breadth and complexity of data created in today's health care encounters require advanced analytics to extract meaning from longitudinal data on symptoms, laboratory results, images, functional tests, genomics, mobile health/wearable devices, written notes, electronic health records (EHR), and other relevant data types. Adva...","","https://www.synapse.org/#!Synapse:syn33576900/wiki/618451","completed","intermediate","1","","2022-08-25","2022-12-15","2023-06-23 00:00:00","2023-10-18 00:39:03"
-"57","bridge2ai","Bridge2AI","Bridge2AI: What makes a good color palette?","What makes a good color palette?","","","upcoming","good_for_beginners","1","","\N","\N","2023-06-23 00:00:00","2023-10-14 05:38:58"
-"58","rare-x-open-data-science","RARE-X Open Data Science","RARE-X Open Data Science: 1. Unlocking rare disease mysteries through open s...","The Xcelerate RARE-A Rare Disease Open Science Data Challenge is bringing together researchers and data scientists in a collaborative and competitive environment to make the best use of patient-provided data to solve big unknowns in healthcare. The Challenge will launch to researchers in late May 2023, focused on rare pediatric neurodevelopmental diseases.","","https://www.synapse.org/#!Synapse:syn51198355/wiki/621435","completed","intermediate","1","","2023-05-17","2023-08-16","2023-06-23 00:00:00","2023-10-14 05:38:59"
+"54","scrna-seq-and-scatac-seq-data-analysis","scRNA-seq and scATAC-seq Data Analysis","Assess computational methods for scRNA-seq and scATAC-seq analysis","Understanding transcriptional regulation at individual cell resolution is fundamental to understanding complex biological systems such as tissues and organs. Emerging high-throughput sequencing technologies now allow for transcript quantification and chromatin accessibility at the single cell level. These technologies present unique challenges due to inherent data sparsity. Proper signal correction is key to accurate gene expression quantification via scRNA-seq, which propagates into downstream analyses such as differential gene expression analysis and cell-type identification. In the even more sparse scATAC-seq data, the correct identification of informative features is key to assessing cell heterogeneity at the chromatin level. The aims of this challenge will be two-fold- 1) To evaluate computational methods for signal correction and peak identification in scRNA-seq and scATAC-seq, respectively;  2) To assess the impact of these methods on downstream analysis","","https://www.synapse.org/#!Synapse:syn26720920/wiki/615338","completed","advanced","1","","2022-11-29","2023-02-08","2023-06-23 00:00:00","2023-10-14 05:38:56"
+"55","cough-diagnostic-algorithm-for-tuberculosis","COugh Diagnostic Algorithm for Tuberculosis","Assess computational methods for scRNA-seq and scATAC-seq analysis","Tuberculosis (TB), a communicable disease caused by Mycobacterium tuberculosis, is a major cause of ill health and one of the leading causes of death worldwide. Until the COVID-19 pandemic, TB was the leading cause of death from a single infectious agent, ranking even above HIV/AIDS. In 2020, an estimated 9.9 million people fell ill with TB and 1.3 million died of TB worldwide. However, approximately 40% of people with TB were not diagnosed or reported to public health authorities because of challenges in accessing health facilities or failure to be tested or treated when they do. The development of low-cost, non-invasive digital screening tools may improve some of the gaps in diagnosis. As cough is a common symptom of TB, it has the potential to be used as a biomarker for diagnosis of disease. Several previous studies have demonstrated the potential for cough sounds to be used to screen for TB[1-3], though these were typically done in small samples or limited settings. Further de...","","https://www.synapse.org/#!Synapse:syn31472953/wiki/617828","completed","advanced","1","","2022-10-16","2023-02-13","2023-06-23 00:00:00","2023-10-14 05:38:57"
+"56","nih-long-covid-computational-challenge","NIH Long COVID Computational Challenge","Understanding Prevalence and Outcomes of Post-COVID Syndrome","The overall prevalence of post-acute sequelae of SARS-CoV-2 (PASC) is currently unknown, but there is growing evidence that more than half of COVID-19 survivors experience at least one symptom of PASC/Long COVID at six months after recovery of the acute illness. Reports also reflect an underlying heterogeneity of symptoms, multi-organ involvement, and persistence of PASC/Long COVID in some patients. Research is ongoing to understand prevalence, duration, and clinical outcomes of PASC/Long COVID. Symptoms of fatigue, cognitive impairment, shortness of breath, and cardiac damage, among others, have been observed in patients who had only mild initial disease. The breadth and complexity of data created in today's health care encounters require advanced analytics to extract meaning from longitudinal data on symptoms, laboratory results, images, functional tests, genomics, mobile health/wearable devices, written notes, electronic health records (EHR), and other relevant data types. Adva...","","https://www.synapse.org/#!Synapse:syn33576900/wiki/618451","completed","intermediate","1","","2022-08-25","2022-12-15","2023-06-23 00:00:00","2023-10-18 00:39:03"
+"57","bridge2ai","Bridge2AI","What makes a good color palette?","What makes a good color palette?","","","upcoming","good_for_beginners","1","","\N","\N","2023-06-23 00:00:00","2023-10-14 05:38:58"
+"58","rare-x-open-data-science","RARE-X Open Data Science","Unlocking rare disease mysteries through open science collaboration","The Xcelerate RARE-A Rare Disease Open Science Data Challenge is bringing together researchers and data scientists in a collaborative and competitive environment to make the best use of patient-provided data to solve big unknowns in healthcare. The Challenge will launch to researchers in late May 2023, focused on rare pediatric neurodevelopmental diseases.","","https://www.synapse.org/#!Synapse:syn51198355/wiki/621435","completed","intermediate","1","","2023-05-17","2023-08-16","2023-06-23 00:00:00","2023-10-14 05:38:59"
 "59","cagi5-regulation-saturation","CAGI5: Regulation saturation","Predicting effects of variants in disease-linked enhancers and promoters","17,500 single nucleotide variants (SNVs) in 5 human disease associated enhancers (including IRF4, IRF6, MYC, SORT1) and 9 promoters (including TERT, LDLR, F9, HBG1) were assessed in a saturation mutagenesis massively parallel reporter assay. Promoters were cloned into a plasmid upstream of a tagged reporter construct, and reporter expression was measured relative to the plasmid DNA to determine the impact of promoter variants. Enhancers were placed upstream of a minimal promoter and assayed similarly. The challenge is to predict the functional effects of these variants in the regulatory regions as measured from the reporter expression.","","https://genomeinterpretation.org/cagi5-regulation-saturation.html","completed","intermediate","14","","2018-01-04","2018-05-03","2023-06-23 00:00:00","2023-11-01 23:42:37"
 "60","cagi5-calm1","CAGI5: CALM1","Predicting effects of calmodulin variants on yeast growth","Calmodulin is a calcium-sensing protein that modulates the activity of a large number of proteins in the cell. It is involved in many cellular processes, and is especially important for neuron and muscle cell function. Variants that affect calmodulin function have been found to be causally associated with cardiac arrhythmias. A large library of calmodulin missense variants was assessed with respect to their effects on protein function using a high-throughput yeast complementation assay. The challenge is to predict the functional effects of these calmodulin variants on competitive growth in a high-throughput yeast complementation assay.","","https://genomeinterpretation.org/cagi5-calm1.html","completed","intermediate","14","","2017-10-21","2017-12-20","2023-06-23 00:00:00","2023-10-18 15:35:49"
 "61","cagi5-pcm1","CAGI5: PCM1","Assessing PCM1 variants' impact on zebrafish ventricle","The PCM1 (Pericentriolar Material 1) gene is a component of centriolar satellites occurring around centrosomes in vertebrate cells. Several studies have implicated PCM1 variants as a risk factor for schizophrenia. Ventricular enlargement is one of the most consistent abnormal structural brain findings in schizophrenia Therefore 38 transgenic human PCM1 missense mutations implicated in schizophrenia were assayed in a zebrafish model to determine their impact on the posterior ventricle area. The challenge is to predict whether variants implicated in schizophrenia impact zebrafish ventricular area.","","https://genomeinterpretation.org/cagi5-pcm1.html","completed","intermediate","14","","2017-11-09","2018-04-19","2023-06-23 00:00:00","2023-10-18 15:35:49"
@@ -154,7 +154,7 @@
 "153","tumor-mutational-burden-tmb-challenge-phase-2","Tumor Mutational Burden (TMB) Challenge Phase 2","The goal of the Friends of Cancer Research and precisionFDA Tumor Mutational...","Tumor mutational burden (TMB) is generally defined as the number of mutations detected in a patient's tumor sample per megabase of DNA sequenced. However different algorithms use different methods for calculating TMB. Mutations in genes in tumor cells may lead to the creation of neoantigens, which have the potential to activate an immune system response against the tumor, and the likelihood of an immune system response may increase with the number of mutations. Thus, TMB is a biomarker for some immunotherapy drugs, called immune checkpoint inhibitors, such as those that target the PD-1 and PD-L1 pathways (Chan et al., 2019). An outstanding problem is the lack of standardization for TMB calculation and reporting between different assays. To address this problem, the Friends of Cancer Research convened a working group of industry and regulatory stakeholders to develop guidance and tools for TMB harmonization. Results from the first phase of this effort were presented at AACR 2020 (s...","","https://precision.fda.gov/challenges/18","completed","intermediate","6","","2021-07-19","2021-09-12","2023-06-23 00:00:00","2023-10-14 05:40:20"
 "154","predicting-gene-expression-using-millions-of-random-promoter-sequences","Predicting Gene Expression Using Millions of Random Promoter Sequences","Decoding gene expression regulation to understand disease.","Decoding how gene expression is regulated is critical to understanding disease. Regulatory DNA is decoded by the cell in a process termed “cis-regulatory logic”, where proteins called Transcription Factors (TFs) bind to specific DNA sequences within the genome and work together to produce as output a level of gene expression for downstream adjacent genes. This process is exceedingly complex to model as a large number of parameters is needed to fully describe the process (see Rationale, de Boer et al. 2020; Zeitingler J. 2020). Understanding the cis-regulatory logic of the human genome is an important goal and would provide insight into the origins of many diseases. However, learning models from human data is challenging due to limitations in the diversity of sequences present within the human genome (e.g. extensive repetitive DNA), the vast number of cell types that differ in how they interpret regulatory DNA, limited reporter assay data, and substantial technical biases present i...","","https://www.synapse.org/#!Synapse:syn28469146/wiki/617075","completed","intermediate","1","","2022-06-15","2022-08-07","2023-06-23 00:00:00","2023-10-14 05:40:21"
 "155","brats-2023","BraTS 2023","Benchmarking brain tumor segmentation with expanded dataset.","The International Brain Tumor Segmentation (BraTS) challenge. BraTS, since 2012, has focused on the generation of a benchmarking environment and dataset for the delineation of adult brain gliomas. The focus of this year’s challenge remains the generation of a common benchmarking environment, but its dataset is substantially expanded to ~4,500 cases towards addressing additional i) populations (e.g., sub-Saharan Africa patients), ii) tumors (e.g., meningioma), iii) clinical concerns (e.g., missing data), and iv) technical considerations (e.g., augmentations). Specifically, the focus of BraTS 2023 is to identify the current state-of-the-art algorithms for addressing (Task 1) the same adult glioma population as in the RSNA-ANSR-MICCAI BraTS challenge, as well as (Task 2) the underserved sub-Saharan African brain glioma patient population, (Task 3) intracranial meningioma, (Task 4) brain metastasis, (Task 5) pediatric brain tumor patients, (Task 6) global & local missing data, (Task 7...","","https://www.synapse.org/brats","completed","advanced","1","","2023-06-01","2023-08-25","2023-06-23 00:00:00","2023-10-26 23:20:21"
-"156","challenges","CAGI7","The seventh round of CAGI.","There have been six editions of CAGI experiments, held between 2010 and 2022. The seventh round of CAGI is planned to take place over the Summer of 2024.","","https://genomeinterpretation.org/challenges.html","upcoming","intermediate","1","","\N","\N","2023-08-04 21:47:38","2023-10-14 05:40:32"
+"156","cagi7","CAGI7","The seventh round of CAGI.","There have been six editions of CAGI experiments, held between 2010 and 2022. The seventh round of CAGI is planned to take place over the Summer of 2024.","","https://genomeinterpretation.org/challenges.html","upcoming","intermediate","1","","\N","\N","2023-08-04 21:47:38","2023-10-14 05:40:32"
 "157","casp15","CASP15","Establish the state-of-art in modeling proteins and protein complexes.","CASP14 (2020) saw an enormous jump in the accuracy of single protein and domain models such that many are competitive with experiment. That advance is largely the result of the successful application of deep learning methods, particularly by the AlphaFold and, since that CASP, RosettaFold. As a consequence, computed protein structures are becoming much more widely used in a broadening range of applications. CASP has responded to this new landscape with a revised set of modeling categories. Some old categories have been dropped (refinement, contact prediction, and aspects of model accuracy estimation) and new ones have been added (RNA structures, protein ligand complexes, protein ensembles, and accuracy estimation for protein complexes). We are also strengthening our interactions with our partners CAPRI and CAMEO. We hope that these changes will maximize the insight that CASP15 provides, particularly in new applications of deep learning.","","https://predictioncenter.org/casp15/index.cgi","completed","intermediate","14","","2022-04-18","\N","2023-08-04 21:52:12","2023-09-28 23:09:59"
 "158","synthrad2023","SynthRAD2023","Synthesizing computed tomography for radiotherapy.","This challenge aims to provide the first platform offering public data evaluation metrics to compare the latest developments in sCT generation methods. The accepted challenge design approved by MICCAI can be found at https://doi.org/10.5281/zenodo.7746019. A type 2 challenge will be run, where the participant needs to submit their algorithm packaged in a docker both for validation and test.","","https://synthrad2023.grand-challenge.org/","completed","intermediate","5","","2023-04-01","2023-08-22","2023-08-04 21:54:31","2023-10-26 23:20:24"
 "159","synthetic-data-for-instrument-segmentation-in-surgery-syn-iss","Synthetic Data for Instrument Segmentation in Surgery (Syn-ISS)","Challenging Machine Learning in Surgical Instrument Segmentation with Synthe...","A common limitation noted by the surgical data science community is the size of datasets and the resources needed to generate training data at scale for building reliable and high-performing machine learning models. Beyond unsupervised and self-supervised approaches another solution within the broader machine learning community has been a growing volume of literature in the use of synthetic data (simulation) for training algorithms than can be applied to real world data. Synthetic data has multiple benefits like free groundtruth at large scale, possibility to collect larger sample of rare events, include anatomical variations, etc. A first step towards proving the validity of using synthetic data for real world applications is to demonstrate the feasibility within the simulation world itself. Our proposed challenge is to train machine learning methods for instrument segmentation using synthetic datasets and test their performance on synthetic datasets. That is, the challenge parti...","","https://www.synapse.org/#!Synapse:syn50908388/wiki/620516","completed","intermediate","1","","2023-07-19","2023-09-07","2023-08-04 23:49:44","2023-10-26 23:20:28"