Autophagy is a complex cellular digestion process involving multiple regulators. Compared to post-translational autophagy regulators, limited information is now available about transcriptional and post-transcriptional regulators such as transcription factors (TFs) and non-coding RNAs (ncRNAs). In this study, we proposed a computational method to infer novel autophagy-associated TFs, micro RNAs (miRNAs) and long non-coding RNAs (lncRNAs) based on TFs and ncRNAs coordinated regulatory (TNCR) network. First, we constructed a comprehensive TNCR network, including 155 TFs, 681 miRNAs and 1332 lncRNAs. Next, we gathered the known autophagy-associated factors, including TFs, miRNAs and lncRNAs, from public data resources. Then, the random walk with restart (RWR) algorithm was conducted on the TNCR network by using the known autophagy-associated factors as seeds and novel autophagy regulators were finally prioritized. Leave-one-out cross-validation (LOOCV) produced an area under the curve (AUC) of 0.889. In addition, functional analysis of the top 100 ranked regulators, including 55 TFs, 26 miRNAs and 19 lncRNAs, demonstrated that these regulators were significantly enriched in cell death related functions and had significant semantic similarity with autophagy-related Gene Ontology (GO) terms. Finally, extensive literature surveys demonstrated the credibility of the predicted autophagy regulators. In total, we presented a computational method to infer credible autophagy regulators of transcriptional factors and non-coding RNAs, which would improve the understanding of processes of autophagy and cell death and provide potential pharmacological targets to autophagy-related diseases. ATG) genes, ATG proteins and non-coding RNAs (ncRNAs). For instance, the autophagy database archived a list of 582 experimentally demonstrated ATG proteins [Autophagy is a process of cytoplasmic degradation that is essential in homeostasis and stress-response, as well as in protein degradation and organelles turnover . The regproteins  and Wu eproteins . For exaproteins .Currently, post-translational autophagy regulators, such as ATG proteins, are well known while limited information is available about transcriptional and post-transcriptional regulators, such as transcription factors (TFs) and ncRNAs . InferriIn this study, we proposed a computational method to predict novel autophagy-associated TFs, miRNAs and lncRNAs based on the TNCR network. First, experimentally verified transcriptional and post-transcriptional regulatory relationships among TFs, miRNAs and lncRNAs were collected and a comprehensive regulatory network was constructed. Next, the known autophagy-associated TFs, miRNAs and lncRNAs were gathered from public data resources. The random walk with restart (RWR) algorithm was implemented on the regulatory network to prioritize autophagy regulators. Leave-one-out cross-validation (LOOCV) achieved an area under the curve (AUC) of 0.889. Functional enrichment analyses and extensive literature surveys demonstrated the credibility of predicted regulators. Altogether, we presented a computational method of inferring credible autophagy regulators and we believed that this would help improve the understanding of the autophagy regulation mechanisms.We integrated five types of experimentally verified transcriptional and post-transcriptional regulatory relationships among TFs, miRNAs and lncRNAs, including TF-miRNA, TF-lncRNA, miRNA-lncRNA, miRNA-TF, lncRNA-TF. The TFs regulations of miRNAs were downloaded from the database TransmiR, which manually surveyed literature and recorded experimentally supported TF-miRNA regulation . The TFsThe known autophagy-associated TFs, miRNAs and lncRNAs were collected from public data resources. We first obtained human genes in autophagy related Gene Ontology (GO) terms from the AmiGO-2 database. Next, we downloaded the human autophagy-associated genes from the autophagy database , a multiP0 as the initial probability vector and tP as a vector in which the i-th element held the probability of finding the random walker at node i in step t. Let α be the restart probability of the random walk in each step at the source nodes. W denotes the probability transition matrix and is derived from the adjacency matrix of the TNCR network. The formula is defined as:w  represents the element in the probability transition matrix, and A  represents the element in the adjacency matrix. The probability vector in step t + 1 can be described as follows:We performed the RWR method on the constructed TNCR network to prioritize novel autophagy regulators. The RWR method simulates a random walker that starts on given seed nodes and transits randomly from the current node to neighboring nodes in the network with the restart probability to teleport to the start nodes. Here, the known autophagy regulators were used as seed nodes. We denoted α) was set as 0.5, and the initial probability (P0) of each seed node was set as 1/n (where n is the number of seed autophagy regulators) while the initial probability of all non-seed nodes was set as zero [tP and t+1P was less than 10−10. The stable probability of i will be in closer proximity to the seed regulators in the regulatory network than nodej. As a result, all candidate nodes in the regulatory network can be ranked according to Based upon the previous work, the restart probability , including TFs, miRNAs and lncRNAs, and performed separately the functional enrichment analyses. For the obtained TFs, we used DAVID to perform GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis . For theGOSemSim  and the In this study, we integrated five types of experimentally verified transcriptional and post-transcriptional regulatory relationships from public data resources and constructed a comprehensive TNCR network . The TNCR network comprised of 4529 edges, including 155 TFs, 681 miRNAs and 1332 lncRNAs . To get networks . Next, wBy integrating data from AmiGO-2, the autophagy database and the ncRDeathDB, we obtained 1222 known autophagy regulators in total . After mIn order to evaluate the performance of our method for inferring autophagy regulators, we performed LOOCV analysis. Each known autophagy regulator was left out in turn as the test case and the other known autophagy regulators were taken as seeds. All the other nodes in the TNCR network were regarded as candidate autophagy regulators. Sensitivity and specificity were calculated for each threshold. Finally, a receiver operating characteristic (ROC) curve was plotted by varying the threshold and then the value of the AUC was calculated. Our method, tested on already known autophagy regulators, achieved an AUC of 0.889 , exhibitThe prioritization of all candidate autophagy regulators is provided in p-value < 0.05) for top ranked TFs, miRNAs and lncRNAs were shown in The top 100 ranked candidate autophagy regulators were retrieved, including 55 TFs, 19 miRNAs and 26 lncRNAs , then thp-values were less than 2.2 × 10−16 . This meant that the top ranked regulators were significantly associated with autophagy. The functional characteristics of the top ranked regulators indicated that our method was capable of identifying novel autophagy regulators.To further evaluate the top ranked regulators associated with autophagy, we compared the GO terms enriched by the top 100 ranked regulators with those enriched by known autophagy-associated factors . As shown in Autophagy is an intracellular catabolic process for maintaining homeostasis and involved systematic regulation at post-translational, transcriptional, and post-transcriptional levels . Both itIn the past several years, the landscape of TNCR networks has been described elaborately ,13. Seve


Purpose: We aimed to evaluate and compare relative impacts of various second-line treatments on overall survival (OS) in metastatic urothelial carcinoma (mUC).Method: A literature search was conducted in PubMed, Embase, and the Cochrane Library for all articles published prior to December 2018 in accordance with the Preferred Reporting Items for Systematic Review and Meta-analysis guidelines. Seven randomized controlled trials with phase III design that met study eligibility criteria were selected for final analysis. A Bayesian framework network meta-analysis (NMA) was applied to indirectly compare the effect of each treatment on OS.Results: In NMA, atezolizumab  and pembrolizumab  showed no significant effect on OS improvement compared to vinflunine. Gemcitabine/paclitaxel combination  and lapatinib  was not significantly associated with OS improvement compared to atezolizumab and best supportive care, respectively. However, results of rankograms revealed that pembrolizumab and atezolizumab were the first and second rank therapeutic agents for OS improvement in post-platinum mUC.Conclusions: Our NMA results are inconclusive. The optimal second-line treatment for OS improvement could not be determined because there were no significant OS differences among evaluated therapeutic agents. However, the use of immunotherapeutic agents such as atezolizumab and pembolizumab may have priority for improving OS in second-line setting of mUC. Urothelial carcinoma (UC) originating from the bladder or upper urinary tracts  is the most common histologic type of cancer. It generally shows chemo-sensitive feature. On the basis of these chemo-sensitivity of UC, platinum (cisplatin or carboplatin) based combination chemotherapy has long been used as first-line standard treatment for metastatic UC (mUC) , 2. WithIn spite of these efficacies of first-line treatments for mUC patients, a considerable number of patients experience disease progression during or after fist-line treatments. Therefore, they will require second-line therapy. Although several chemotherapeutic agents have been investigated in the second-line setting of mUC, they have only presented marginal activity with ORR of <20% and median OS of <9 months with considerable toxicity profiles , 9. FurtIn the last decades, with an increasing understanding of molecular biology and cancer immunobiology, research on systemic therapy in the oncologic field has mainly focused on targeted and immunotherapeutic agents other than cytotoxic chemotherapy . LapatinThus, the objective of the present study was to assess and compare the efficacy of each second-line treatment on OS improvement for determining the optimal therapeutic agent in post-platinum mUC setting. To achieve this goal, we conducted a network meta-analysis (NMA) of available data by only including phase III, randomized clinical trials (RCTs).The present NMA was performed and reported in line with recommendations of the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement . AND (systemic chemotherapy OR systemic treatment OR immune checkpoint inhibitor). Citation lists of all searched articles were then used to confirm other possible relevant publications. Only studies with well-established study design with comparative arms and explicit description of patients' characteristics were finally selected. Two independent reviewers (HSK and CK) selected these articles. Any disagreements among reviewers were settled by consensus with a third reviewer (HHK).We conducted an electronic search for clinical trials on second-line systemic therapeutic agents in mUC prior to December 2018 using PubMed, Embase, and the Cochrane Library. The search was limited to English articles with full-text publications. Search terms were used separately or in combination as followings: Population, Patients with mUC; Interventions, Second-line treatment after first-line chemotherapy; Comparators, Another second-line treatment ; Outcome, OS; Study design, prospective RCTs with phase III design.Study eligibility was defined according to Population, Intervention, Comparator, Outcome, and Study design (PICOS) system : PopulatArticles were eligible if they met following inclusion criteria: (1) human research; (2) patients with mUC previously treated with the first-line systemic chemotherapy; (3) received second-line systemic treatment; (4) reported outcome value (OS); (5) available assessment for the association between second-line treatment and OS; (6) sufficient information provided to estimate hazard ratio (HR) and their 95% confidence interval (CI); and (7) RCTs. Exclusion criteria were as followings: review articles, letters, editorial comments, case reports, and articles that did not provide raw data.Three independent reviewers  extracted the required information from all eligible studies and then compared their results to confirm accuracy. Any disagreements for extracted data between two reviewers were settled by consensus. Extracted data were recorded in accordance with reporting recommendations for tumor marker prognostic studies (REMARK) guidelines  as follop-values for the Begg's and Egger's tests are <0.05  . Either re <0.05 , 26. There <0.05 . All p-vWe identified 232 articles after initial database searches. Among these, 80 duplicated publications were excluded. After reviewing titles and abstracts, 107 articles were also excluded. Thus, a total of 45 articles remained for full text review. According to inclusion criteria of our analysis, a total of 7 RCTs were finally selected for the current NMA , 18, 19.Characteristics of each included study are summarized in Details on treatment characteristics of these eligible 7 studies are shown in Networks for indirect comparisons among multiple second-line treatments in terms of OS are described in Results of NMA are depicted in p-values > 0.05; There was no strong evidence of publication bias in that the funnel plot showed a certain degree of symmetry in the NMA of OS . Also, tAlthough platinum (cisplatin or carboplatin)-based combination chemotherapy has been used as the primary treatment to improve survival outcomes in mUC patients, a substantial number of patients have experienced disease progression during or after the first-line platinum-based combination chemotherapy. Thus, secondary treatment is usually considered for such patients , 2. SeveSeveral therapeutic agents have been explored in phase III RCT design at second-line setting of mUC. Bellmunt et al. , 14 haveHowever, there is no consensus on which of these drugs is the optimal second-line treatment for mUC. In the current study, we sought to evaluate the optimal second-line therapeutic agent in terms of OS improvement by indirectly comparing agents mentioned above. To the best of our knowledge, this is the first report pooling multiple second-line treatment agents of mUC  assessed in previous phase III RCTs , 18, 19.The present study has some limitations. First, although our study only included previously reported phase III RCTs for NMA, the association between second-line treatments and OS could not be controlled through multivariate analysis with other variables such as treatment-induced toxicity and patient dropout which might have affected results of this study. Besides, there might be differences in patient background among anticancer agents. Although these differences could not be easily identified and adjusted, they might have effect on the results of the study if considered when agents are compared. Second, the primary end-point of this analysis only included OS improvement. Therefore, we did not assess the relationship between second-line therapeutic agents and other clinical outcomes, including treatment-related side effects and response rates. Even if the final goal of cancer treatment is to improve the survival of patients, adverse events, and response rates for treatment might have significant impact on the choice of treatment modality. If these factors were considered as other end-points in the analysis, it might be possible to provide a more crucial basis for finding the optimal second-line therapeutic agent in mUC. Lastly, the current study only included full-text articles published in English which might lead to language bias , althougResults of our study were inconclusive in view of the inability to determine the optimal second-line treatment for OS improvement because there were no significant OS differences among evaluated therapeutic agents based on results of NMA. However, the use of ICI drugs such as atezolizumab and pembolizumab may be given priority for improving OS in second-line setting of mUC.All datasets generated for this study are included in the manuscript and/or the HSK, JK, CK, and HHK: conception and design and supervision. HY, HSK, and JK: data acquisition, manuscript drafting, and statistical analysis. JK and HSK: data analysis and interpretation and critical revision of the manuscript for scientific and factual content.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In recent years, efforts to exploit sunlight, a free and abundant energy source, have sped up dramatically. Oxygenic photosynthetic organisms, such as higher plants, algae, and cyanobacteria, can convert solar energy into chemical energy very efficiently using water as an electron donor. By providing organic building blocks for life in this way, photosynthesis is undoubtedly one of the most important processes on Earth. The aim of light-driven catalysis is to harness solar energy, in the form of reducing power, to drive enzymatic reactions requiring electrons for their catalytic cycle. Light-driven enzymes have been shown to have a large number of biotechnological applications, ranging from the production of high-value secondary metabolites to the development of green chemistry processes. Here, we highlight recent key developments in the field of light-driven catalysis using biological components. We will also discuss strategies to design and optimize light-driven systems in order to develop the next generation of sustainable solutions in biotechnology. One possible route to achieve this is through the light reactions of photosynthesis, which facilitate the conversion of solar energy into chemical energy. The initial, light-dependent part of photosynthesis is a highly efficient process; however, the overall efficiency of photosynthesis is limited by the carbon-fixing enzyme ribulose-1,5-bisphosphate carboxylase/oxygenase. This results in a significant loss of energy as heat, fluorescence, and through several alternative electron sinks H. Electrons can be supplied to the target enzyme by (A) a photosynthetic electron transport chain (PETC) mediated by photosystem I (PSI) and ferredoxin, e.g. a cytochrome P450, (B) an internal cofactor that captures light, e.g. a fatty acid photodecarboxylase (FAP), (C) an excited photosensitizer, e.g. a lytic polysaccharide monooxygenase (LPMO), or (D) a PETC mediated by the plastoquinone (PQ) pool, e.g. a particulate methane monooxygenase (pMMO).Box 2.Heterologous cytochrome P450 pathways can be driven by light in cyanobacteriaSynechocystis sp. PCC 6803. The P450s successfully receive electrons from PSI and ferredoxin, thus enabling enzyme catalysis, both in vitro and in vivo, in a light-dependent manner.LPMOs driven by photoexcited pigments can significantly increase biomass conversionA novel FAP converts long-chain fatty acids to alka(e)nes in microalgaeChlorella variabilis and Chlamydomonas reinhardtii.2 to hydrocarbons in microbial photosynthetic hostsDemonstration of light-driven conversion of COSynechocystis sp. with the heterologous expression of the Pseudomonas fatty acid desaturase UndB and the C. variabilis FAP.In a proof-of-concept study, Light-driven hydroxylation of methane is established in methanotroph membranesMethylosinus trichosporium OB3b and used to drive the native membrane-bound pMMO.Higher plants produce a vast number of various specialized metabolites, such as terpenoids, cyanogenic glucosides, alkaloids, phenylpropanoids, and related phenolic compounds. The biosynthesis of these compounds requires highly specific and complex enzymatic reactions that often involve a group of monooxygenases named cytochrome P450s (P450s). P450s constitute one of the largest superfamilies of enzymes found in all biological kingdoms . P450s cThe heterologous expression of class II eukaryotic P450s is a complex engineering exercise that requires attention to detail such as successful membrane integration and insertion of the heme group. Additionally, these P450s need a dedicated cytochrome P450 oxidoreductase that transfers electrons, via a flavin mononucleotide domain, from NADPH. Electron supply is generally considered to be the major limiting factor in native P450 systems . In ordeNicotiana benthamiana. The P450s were successfully inserted into the photosynthetic membranes, and in vivo activity was NADPH independent and mediated by the small 10–12 kDa soluble photoreduced electron carrier protein ferredoxin (Fd), thereby avoiding the need for a dedicated reductase  and a soluble glycosyltransferase (UGT85B1) in eductase . This wain vivo  produce hydrocarbons in a light-dependent, but photosynthesis-independent, manner. A follow-up study identified the responsible enzyme, a fatty acid photodecarboxylase (FAP), which carries a flavin adenine dinucleotide as a light‐absorbing cofactor and converts fatty acids to hydrocarbons, with a quantum yield exceeding 0.8  to alkanes  pool, thus hindering further attempts to increase hydrocarbon production. On the other hand, in Synechocystis, FFAs were efficiently liberated and two distinct light-driven pathways were introduced. First, the Pseudomonas aldehyde decarboxylase UndB was expressed and converted 55% of liberated FFAs into their respective alkenes. In its native environment, UndB is an integral membrane protein and requires electrons for its catalytic cycle ; therefore, their large-scale application in an industrial setting is challenging. This issue was addressed in a recent study seeking to improve the application of particulate methane monooxygenases (pMMOs) (of water . However2 or H2O2 to disrupt polysaccharide chains (−2 s−1), increased the catalytic activity of the LPMO 10- to 100-fold. While the mechanistic details of this light-driven system are still under intense debate    the choice of an appropriate host chassis and (ii) the exploitation of synthetic biology to rationally design and organize future light-driven systems.A consistent supply of electrons is required for an efficient light-driven system. Consequently, any organism capable of performing photosynthesis will be a superior choice of host chassis compared with traditional heterotrophic microbial systems. Additionally, there are several general aspects that are desirable in any production host: fast growth, efficient transformation systems, and the availability of tools to design and regulate light-driven catalysis.N. benthamiana , which might require testing different photosynthetic chassis to find the most suited one. In this context, it is encouraging that an array of standardized DNA assembly systems is becoming available for photosynthetic hosts. One example is the Golden Gate-based methods developed for both plants and nhardtii .The road to the industrial use of light-driven systems contains numerous engineering and optimization challenges. We believe that key strategies to overcome these challenges can be implemented with synthetic biology .In a light-driven system, photosynthetic reducing power is abundant; however, competition from native electron transfer pathways and the inherent regulation of light-harvesting and photosynthetic electron transport may constitute obstacles. Therefore, to take full advantage of the electron flux, it is advisable to design a system where the electron donor and target enzyme present a suitable redox coupling while also considering potential off-target redox partners . Going fN. tabacum with membrane anchors from components of the twin-arginine protein translocation system, which natively co-localize and assemble in the thylakoid membrane. This strategy increased the final dhurrin yield 5-fold (Another strategy to achieve more efficient and controlled interactions within a light-driven system is to spatially organize individual components . SynthetOverall, combining the key strategies outlined above should provide new avenues to design and fine-tune the next generation of light-driven enzymes and biosynthetic pathways. Together with a more sophisticated understanding of metabolic fluxes and trade-offs between growth and production , light-d


F = 62.597, p < 0.001)]. Voxel-level GMV analysis revealed strong relationships between CSIT score and volumes of the left precentral gyrus and left inferior frontal gyrus (L-IFG). In addition, ROI-level GMV analysis revealed associations between CSIT score and left amygdala volumes. Our results suggest the following: (1) OI, as measured by the CSIT, is impaired in AD and MCI patients compared with healthy controls in the Chinese population; (2) the severity of OI dysfunction can distinguish patients with cognitive impairment from controls and AD from MCI patients; and (3) the left-precentral cortex and L-IFG may be involved in the processing of olfactory cues.Alzheimer’s disease (AD) is a common memory-impairment disorder frequently accompanied by olfactory identification (OI) impairments. In fact, OI is a valuable marker for distinguishing AD from normal age-related cognitive impairment and may predict the risk of mild cognitive impairment (MCI)-to-AD transition. However, current olfactory tests were developed based on Western social and cultural conditions, and are not very suitable for Chinese patients. Moreover, the neural substrate of OI in AD is still unknown. The present study investigated the utility of a newly developed Chinese smell identification test (CSIT) for OI assessment in Chinese AD and MCI patients. We then performed a correlation analysis of gray matter volume (GMV) at the voxel and region-of-interest (ROI) levels to reveal the neural substrates of OI in AD. Thirty-seven AD, 27 MCI, and 30 normal controls (NCs) completed the CSIT and MRI scans. Patients (combined AD plus MCI) scored significantly lower on the CSIT compared to NCs [ Alzheimer’s disease (AD) is the most common age-related neurodegenerative disorder and is characterized by progressive impairment of cognitive function, ultimately leading to incapacitation and death . Thus, APrevious studies have indicated that olfactory perception is frequently disrupted in the early stages of AD , especiaThere are many OI tests, such as the University of Pennsylvania Smell Identification Test (UPSIT), Connecticut Chemosensory Clinical Research Center (CCCRC) olfactory test, and “Sniffin Sticks” olfactory test. Each includes odors common in the local culture; however, performance may be affected by various semantic and cultural factors . For insThe neural substrates of OI dysfunction in AD and MCI patients are still unclear. Several neuroimaging studies have found associations between OI dysfunction and structural abnormities in brain regions that contribute to olfactory processing, such as the olfactory cortex (OC). The OC is the center of olfactory processing and functional and structural anomalies are strongly implicated in OI dysfunction . HippocaAlthough several neuroimaging studies have implicated OC, hippocampal, and (or) amygdala abnormalities in OI dysfunction among AD and MCI patients, multiple studies have also reported contrary results. For example, Servello and colleagues  found noIn this study, we first compared OI performance among AD, MCI, and normal aging in the Chinese population using the CSIT. Subsequently, we conducted correlational analysis of CSIT scores and regional GMV at both ROI and whole-brain voxel levels to explore the neural substrates of OI.A total of 94 right-handed participants  sudden onset, (b) early occurrence of gait disturbances, seizures, or behavioral changes, (c) focal neurological features such as hemiparesis, sensory loss, or visual field deficits, and (d) early extra-pyramidal signs or other severe disorders such as trauma, major depression, severe cerebrovascular disease, or metabolic abnormalities .Participants with MCI were clinically diagnosed by experts according to Peterson’s criteria  and NINCThe NCs fulfilled the following criteria: cognitively normal, no neurological or psychiatric disorders, no psychoactive medication use, MMSE score of 28 or higher, and CDR score of 0.White mater hyper-intensities (WMHs) were graded according to the Fazekas scale  based onIn order to rule out any confounds that could adversely influence the study results, we excluded subjects who engaged in long-term smoking and drinking as these behaviors have been shown to affect cognition and olfaction . We alsoFinally, 37 AD and 27 MCI patients with initial diagnosis and currently not treated with a cholinesterase inhibitor  were enrolled. Detailed background information on all groups is summarized in All participates underwent a clinical evaluation and neuropsychological assessment. The following neuropsychological test battery was administered to each subject for the purpose of establishing a clinical diagnosis as described previously . (i) GenThe Chinese smell identification test (CSIT) developed by the Institute of Psychology, Chinese Academy of Sciences, was applied to evaluate OI performance . The CSIThe second part (CSIT-OI) includes olfactory tests of 40 familiar and easy to recognize odors such as strawberry, jujube, haw, and sesame oil. Subjects were required to name each odor from a list of four alternatives. The CSIT-OI test method is similar to the 40-item UPSIT , but CSIStructural MRI images were acquired on a General Electric HD 750 w 3.0 T MRI scanner with an 8-channel head-coil . Structural imaging included T1-weighted three-dimensional (3D-T1), axial T2-weighted, and fluid-attenuated inversion recovery images. According to axial T2-weighted and fluid-attenuated inversion recovery images, subjects with abnormalities other than atrophy, or leukoaraiosis were excluded. The 3D-T1 images were collected using a fast-spoiled gradient recalled echo sequence . The total scan duration for 3D-T1 image acquisition was 4 min, 30 s.1, a software package based on statistical parametric mapping software (SPM 82) was used for VBM analyses. VBM8 was used to calculate GMV corrected for total intracranial volume, age, sex, and education level. T1-weighted images were segmented into GM, white matter, and cerebrospinal fluid using a fully automated algorithm in SPM 8. Images were normalized by diffeomorphic anatomical registration through exponentiated Lie algebra normalization, and transformed to Montreal Neurological Institute space to preserve local differences in anatomy across subjects, thereby allowing quantification. Finally, the normalized GM images were smoothed for statistical analysis -based structural ROI method for I signal , and usepost hoc Bonferroni test for multiple comparisons. For non-normally distributed data [denoted by Md ], we used Kruskal–Wallis analysis of variance (ANOVA) to evaluate differences among groups followed by a Dunn–Bonferroni test for post hoc comparisons. The effectiveness of CSIT for identifying AD, MCI, and NC was assessed by receiver operating characteristic curve (ROC) analysis. The sensitivity and false positive rate (1 – specificity) of CSIT-OI, CSIT-self, and CSIT-OI + self was calculated for ROC curves, and the area under the ROC curve was used to determine classification accuracy. A α < 0.05 (two-tailed) was considered significant for all tests.Statistical analyses of behavioral data were conducted using SPSS for Windows . Sex ratios were compared across diagnostic groups  using the chi-square test. Demographic variables such as age, education level, neuropsychological features, and CSIT scores were compared across groups by one-way analysis of covariance (ANCOVA)  followedP < 0.001 and a cluster-level threshold of P < 0.05.Spearman correlation analysis was performed between CSIT scores and mean GMV of each ROI . Significant correlations between CSIT and GMV of each ROI were corrected for false discovery rate. The mean GMV of each ROI was also compared between groups by ANCOVA with sex, age, and education as covariates. At the whole-brain voxel level, behavior – neuroimaging correlation analysis was conducted using SPM 8 with sex, age, and education as covariates. The statistical maps were thresholded using the Gaussian random field (GRF) correction with a voxel-level threshold of p < 0.001).The demographic and baseline clinical characteristics of the study subjects are summarized in p < 0.001) and CDR (p < 0.001) differed markedly, with significantly lower scores in the AD group compared to MCI and NC groups. In addition, there were significant inter-group differences in AVLT . Daily functions were significantly impaired only in AD patients; both MCI and AD groups showed a gradual decline, but a worse performance was observed in the latter patients , and lower mean score in AD than MCI ; however, the correlation did not reach significance within individual diagnostic groups due to lack of statistical power (p > 0.05).There were significant differences in CSIT-OI score among groups, with significantly lower scores (indicating poorer OI) in the AD and MCI group compared to NCs  . Using aAccording to ROC analysis, CSIT-OI score and CSIT-OI plus CSIT-self score also distinguished AD from MCI, while again CSIT-self score did not . Using ar = 0.38, p = 0.046), with a trend observed for the left hippocampus   and left precentral gyrus   . At the We demonstrate that OI as measured by the CSIT is impaired in AD and MCI patients compared to age-matched healthy controls in the Chinese population, and that the severity of OI dysfunction can distinguish AD and MCI patients from controls and AD from MCI with high sensitivity and specificity. In addition, CSIT scores were significantly associated with specific memory assessment outcomes but not with measures of general cognitive function such as MMSE score. Based on ROI-level GMV analysis, the OI of AD patients was significantly correlated with left amygdala volume, with a similar trend observed for the left hippocampus ; meanwhile, a voxel-level analysis revealed that the OI of AD patients was also associated with the volumes of precentral gyrus-L and IFG-L. In contrast, no such CSIT-GMV associations were found in the MCI and NC groups. Therefore, OI dysfunction as assessed by a culturally appropriate test (the CSIT) may be a convenient AD screening tool.This study confirmed that OI is impaired in ethnic Chinese AD and MCI patients, consistent with previous studies in Western populations. In all three diagnostic groups , OI decreased progressively with age . There wAlthough CSIT is based on UPSIT, group differences in scores were greater than in past studies using the UPSIT, suggesting better discriminative efficacy. Our ROC analysis suggests that the CSIT can distinguish MCI and AD patients from normal elderly individuals and accurately distinguish AD from MCI. This level of discrimination is similar to cerebral spinal fluid (CSF) biomarkers, only slightly inferior to amyloid imaging and structural MRI, and significantly greater than UPSIT  . The OI Volumes of left IFG and precentral cortex were significantly associated with OI performance in AD patients, a finding that to the best of our knowledge has not been reported previously. While previous reports have indicated that OI is related to the olfactory bulb, OC, hippocampus, and parahippocampus, few have found associated changes in motor-related areas. The precentral cortex is an important component of the motor network . In addiThis study has several limitations. First, our AD patient group included both early-onset and late-onset patients, which may introduce heterogeneity to morphometric changes. A previous study found that patients with early-onset AD showed bilateral reductions in the medial temporal lobes, inferior parietal lobules, precuneus and perisylvian cortices, and cingulate cortices, as well as in the right inferior frontal gyrus, whereas late-onset patients with AD showed atrophy only in bilateral medial temporal cortices. Second, we did not compare differences between AD patients with and without olfactory dysfunction, and such a comparison could also reveal mechanisms underlying OI disorders. Third, this is a cross-sectional rather than a longitudinal study, so we do not know if these differences were caused by the progression of AD. Fourth, we excluded subjects who engaged in long-term smoking and drinking, as these behaviors affect cognition and olfaction . This coOur study confirms that the CSIT is a culturally appropriate olfactory recognition test for the Chinese Han population that can effectively distinguish among AD, MCI, and healthy aging. Gray matter voxel-based MRI analysis demonstrated that OI is more strongly related to the left cerebral hemisphere  than to the right hemisphere . We also found that the left-precentral gyrus and left-IFG may be involved in OI. We therefore speculate that language processing may contribute to the expression of olfactory recognition, and that AD patients are impaired in this domain. However, further research is needed to elucidate the precise mechanisms for OI dysfunction and relationships to other aspects of MCI and AD pathology.All datasets generated for this study are included in the manuscript and/or the supplementary files.The AD and MCI patients were recruited from the Dysmnesia Outpatient Department at the First Affiliated Hospital of Anhui Medical University, Anhui Province, China. The NCs were recruited from the local community through advertisement or were the spouses of the study patients. This study was approved by the Research Ethics Committee of the First Affiliated Hospital of Anhui Medical University. All subjects gave written informed consent in accordance with the Declaration of Helsinki.XW performed the analysis and wrote the manuscript. ZG, SZ, LW, WZ, and YY helped to collect the behavioral and imaging data. TB and G-JJ helped in MRI data analysis. YT and KW designed and supervised the study.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Background: Tuberculosis disease, caused by Mycobacterium tuberculosis, is a major public health problem. The emergence of M. tuberculosis strains resistant to existing treatments threatens to derail control efforts. Resistance is mainly conferred by mutations in genes coding for drug targets or converting enzymes, but our knowledge of these mutations is incomplete. Whole genome sequencing (WGS) is an increasingly common approach to rapidly characterize isolates and identify mutations predicting antimicrobial resistance and thereby providing a diagnostic tool to assist clinical decision making.Methods: We applied machine learning approaches to 16,688 M. tuberculosis isolates that have undergone WGS and laboratory drug-susceptibility testing (DST) across 14 antituberculosis drugs, with 22.5% of samples being multidrug resistant and 2.1% being extensively drug resistant. We used non-parametric classification-tree and gradient-boosted-tree models to predict drug resistance and uncover any associated novel putative mutations. We fitted separate models for each drug, with and without “co-occurrent resistance” markers known to be causing resistance to drugs other than the one of interest. Predictive performance was measured using sensitivity, specificity, and the area under the receiver operating characteristic curve, assuming DST results as the gold standard.Results: The predictive performance was highest for resistance to first-line drugs, amikacin, kanamycin, ciprofloxacin, moxifloxacin, and multidrug-resistant tuberculosis (area under the receiver operating characteristic curve above 96%), and lowest for third-line drugs such as D-cycloserine and Para-aminosalisylic acid (area under the curve below 85%). The inclusion of co-occurrent resistance markers led to improved performance for some drugs and superior results when compared to similar models in other large-scale studies, which had smaller sample sizes. Overall, the gradient-boosted-tree models performed better than the classification-tree models. The mutation-rank analysis detected no new single nucleotide polymorphisms linked to drug resistance. Discordance between DST and genotypically inferred resistance may be explained by DST errors, novel rare mutations, hetero-resistance, and nongenomic drivers such as efflux-pump upregulation.Conclusion: Our work demonstrates the utility of machine learning as a flexible approach to drug resistance prediction that is able to accommodate a much larger number of predictors and to summarize their predictive ability, thus assisting clinical decision making and single nucleotide polymorphism detection in an era of increasing WGS data generation. Mycobacterium tuberculosis bacteria, remains a major global public health challenge, with over 10.0 million people infected with TB and an estimated 1.6 million deaths in 2017 (M. tuberculosis strains resistant to at least RIF and INH are termed multidrug-resistant (MDR-TB), with >550,000 new resistant cases in 2017 (Tuberculosis (TB), caused by  in 2017 . An incr in 2017 . First-l in 2017 . M. tube in 2017 . Additio in 2017 . Convent in 2017 . Treatme in 2017 .M. tuberculosis via three main mechanisms: (i) blocking enzymes involved in the synthesis of components of the cell wall , (ii) disrupting protein synthesis at the level of the ribosomes  and (iii) hindering various processes at a DNA level such as RNA/DNA synthesis   or other polymorphisms  resulting in the modification of drug targets  or in the loss of an ability to activate prodrugs  (inhA promoter for INH and ethionamide (ETH) resistance]  . While Mfor PZA) . Mutatioistance] . A resisractions .Drug resistance is traditionally diagnosed using bacterial culture and phenotypic testing, where uncovering resistance to first-line treatments leads to an assessment of second-line regimens. However, this approach is relatively slow and expensive, and it has inherent inaccuracies and reproducibility challenges . Whole gof M. tuberculosis clinical isolates undergoing WGS and the expanding numbers of loci implicated in resistance, machine learning offers a complementary approach to regression-based GWAS, as it has a superior capability to adapt to the growing body of clinical and biological data. Compared with regression, nonparametric machine learning methods such as classification trees (CTs) and gradient-boosted trees (GBTs) have few underlying model assumptions related to the distribution and functional relationships between the included covariates or predictors. They potentially provide greater flexibility for problems of prediction in high-dimensional variable spaces, when each individual covariate may contain limited information and covariate interactions are important , including for less often used ones such as para-aminosalisylic acid (PAS), cycloserine (CYS), and ETH. Not only do we focus on known drug-resistance SNPs or genes, but we also analyze (640K) genome-wide SNPs with an opportunity to inform new variant discovery. Therefore, our dataset provides a unique opportunity to evaluate machine learning methods, which could be rolled in a clinical setting, based on actual M. tuberculosis “big data.” Second, we use a combination of CTs and GBTs to optimize resistance prediction and SNP discovery , which cover the four main lineages . Across the isolates, 642,580 high-quality genome-wide SNPs were identified, with the majority in genic regions . The majority of SNPs (98.9%) have low minor allele frequencies (< 1%). We also included covariates representing the aggregation of nonsynonymous mutations by locus within our machine learning approach. A phylogenetic tree constructed using all genome-wide SNPs revealed the expected clustering by lineage Figure 1Laboratory drug susceptibility testing (DST) of anti-TB drugs found that 35.5% of isolates had a resistance phenotype  or genome wide (CT-ALL). One GBT model was fitted to datasets with all genome-wide SNPs (GBT-ALL). All of these three models  excluded known co-occurrent resistance markers. We fitted one additional approach (GBT-CRM) that included all genome-wide SNPs and, therefore, potential co-occurrent resistance markers in the model. Finally, for the purpose of comparison, we fitted a logistic regression (LR) model on the SNPs in genes known to be linked to drug resistance (LR-KDG). For all approaches, we also included the aggregated count of all nonsynonymous mutations per gene in the dataset, to allow the models to use this covariate as a potential starting point and potentially cover known resistance mutations that have low frequency . It shouThe predictive performance of the machine learning approaches was assessed by calculating the sensitivity and specificity and the area under the receiver operating characteristic curve (AUC), assuming the laboratory DST result was the gold standard Table 2.Owing to the inclusion of co-occurrent resistance markers, the GBT-CRM model was almost always the best in terms of predictive accuracy and AUC, with a marked improvement for PZA and PAS  , there iThe predictive performance of the GBT models, and especially the GBT-CRM model, is similar or higher than that of the models developed in other studies . The perAs expected, the overall predictive ability of INH, RIF, and MDR-TB resistance across the machine learning approaches was high (∼90% sensitivity) because the underlying mutations and loci involved are well established. However, 10% of resistance cases were not identified by the models. The genotypic–phenotypic discordance, as measured with the GBT-ALL model, was higher for other first-line  and second-line drugs , and large discrepancies point towards unknown genetic factors. However, other factors potentially have an effect, including laboratory DST errors or misspecified or truncated drug assay breakpoints , efflux-In summary, our approach has shown that machine learning can robustly predict drug resistance and inform on its underlying mutations. Furthermore, such approaches will be scalable when WGS becomes routine and increasingly “big data” analyses are required.bwa-mem software, and SNPs and insertions and deletions (indels) called from the consensus of GATK and samtools software. The final set of SNPs  and indels included those with low levels of missing genotypes (<2%) and excluded those in the hypervariable PE/PPE gene families. Missing values were imputed using a nearest neighbor imputation approach. The dataset was augmented with covariates that aggregated the number of nonsynonymous mutations isolated in a locus.The dataset consists of 16,688 isolates (lineages 1–4) with WGS data and phenotypic DST data see ]. The raN = 1,421 SNPs; “CT-KDG”) and the other using all SNPs in the dataset . CT algorithms produce only one easy to interpret tree as output. GBT models , and the Xgboost implementation (version 0.70) was used to construct the CTs and GBTs   (261868591). SCa is funded by Medical Research Council UK grants . TC is funded by the Medical Research Council UK  and BBSRC (Grant no. BB/R013063/1).Author WD was employed by the company Dalberg Advisors in Switzerland. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Clinicians are less likely to prescribe guideline-recommended treatments to people with multimorbidity than to people with a single condition. Doubts as to the applicability of clinical trials of drug treatments (the gold standard for evidence-based medicine) when people have co-existing diseases (comorbidity) may underlie this apparent reluctance. Therefore, for a range of index conditions, we measured the comorbidity among participants in clinical trials of novel drug therapies and compared this to the comorbidity among patients in the community.n = 122,969 participants) for 22 index conditions. Community patients were identified from a nationally representative sample of 2.3 million patients in Wales, UK. Twenty-one comorbidities were identified from medication use based on pre-specified definitions. We assessed the prevalence of each comorbidity and the total number of comorbidities (level of multimorbidity), for each trial and in community patients.Data from industry-sponsored phase 3/4 multicentre trials of novel drug therapies for chronic medical conditions were identified from two repositories: Clinical Study Data Request and the Yale University Open Data Access project. We identified 116 trials (In the trials, the commonest comorbidities in order of declining prevalence were chronic pain, cardiovascular disease, arthritis, affective disorders, acid-related disorders, asthma/COPD and diabetes. These conditions were also common in community-based patients.Mean comorbidity count for trial participants was approximately half that seen in community-based patients. Nonetheless, a substantial proportion of trial participants had a high degree of multimorbidity. For example, in asthma and psoriasis trials, 10–15% of participants had ≥ 3 conditions overall, while in osteoporosis and chronic obstructive pulmonary disease trials 40–60% of participants had ≥ 3 conditions overall.Comorbidity and multimorbidity are less common in trials than in community populations with the same index condition. Comorbidity and multimorbidity are, nevertheless, common in trials. This suggests that standard, industry-funded clinical trials are an underused resource for investigating treatment effects in people with comorbidity and multimorbidity. Drug treatments that have been recommended in evidence-based clinical guidelines are less likely to be prescribed to people with multimorbidity (defined as people with two or more conditions) –5. One rComorbidity (the presence of other conditions in addition to a specified index condition)  may inflHowever, most studies examining clinical trial representativeness have done so by analysing routine clinical practice data  to which trial eligibility criteria have been applied –17. SincWe examined the prevalence of comorbidity and multimorbidity among 122,969 participants from 116 industry-funded trials of novel drug therapies for 22 index conditions and compared these results with comorbidity and multimorbidity prevalence in 2.3 million patients living in the community.This cross-sectional analysis compares the distribution of comorbidity and multimorbidity in participants enrolled in 116 industry-sponsored trials and a representative community sample from the UK. All analyses were pre-specified , had a start date on or after 1 January 1990 , were phase 2/3, 3 or 4, recruited ≥ 300 participants, had an upper age limit ≥ 60 years (or no maximum) and evaluated drugs for a selected set of chronic conditions  from industry-sponsored trials from two repositories: the Clinical Study Data Request (CSDR) and the Yale University Open Data Access (YODA) project . From this set, trials were selected according to a pre-specified protocol (Prospero CRD42018048202) . BrieflyA community sample was identified using the Secure Anonymised Information Linkage (SAIL) Databank, which is a repository of health and administrative data covering 70% of Wales’s population of three million . This saFor trial data, index conditions were defined by the treatment indication, described in the trial registration. Trials were then grouped by index condition.For the community sample, we used codes from the Read classification system to identify people with each index condition. Read codes are a coding scheme used in UK primary care electronic health records . The indMedical history data was frequently redacted in the trial datasets to maintain patient confidentiality, and even when provided, different terminologies were used. In contrast, all the trials providing data on concomitant medication used the World Health Organization Anatomic Therapeutic Chemical (WHO-ATC) system, the de facto standard for drug coding in clinical trials . We therTrials either reported the ATC codes directly or reported preferred terms often along with the drug route. In the latter case, we used RxNorm (the US drug metathesaurus) , the UK For the community sample, we used the NHS Business Authority ATC to Read code lookup table (as processed by the OpenPrescribing project) . For druThe following comorbidities . Aggregated data and code required to run these models, along with full model descriptions, are available in Additional file https://github.com/dmcalli2/dynamic_protocols/blob/master/defining_comorbidities_SAIL.md.Data were prepared using Structured-query Language (SQL) and R . The Dirichlet sampling was performed using R, and the Poisson model was fitted in Just Another Gibbs Sampler  using descriptive statistics.Additionally, we compared data elements obtained from This project had approval from the University of Glasgow, College of Medicine, Veterinary and Life Sciences ethics committee (200160070). SAIL analyses were approved by SAIL Information Governance Review Panel (Project 0830).Of the 124 trials meeting our inclusion criteria and made available via the CSDR and YODA repositories, 116  provided concomitant medication data allowing us to identify comorbidities. We had initially planned to include trials from the NIH BioLINCC repository, but found that none of the 8 trials which met our eligibility criteria provided sufficient data on comorbidities to be included in the analysis  from seven industry sponsors of novel drug treatments for 22 common index conditions. We assessed the same comorbidities for the same index conditions from a nationally representative community sample of 2.3 million people. Comorbidity and multimorbidity were consistently lower in trial populations than in community patients, but were nonetheless common in both.Our estimates of comorbidity in the community are consistent with previous findings: comorbidity was common, and for some conditions (e.g. COPD and osteoporosis), it was almost ubiquitous , 28. To We also found that, although patients with comorbidity or multimorbidity were underrepresented in many trials, comorbidity and multimorbidity were nonetheless common. For around half of the index conditions, the proportion of trial participants with ≥ 2 comorbidities (i.e. with three conditions and therefore highly multimorbid ) was aboThis finding is important because of current uncertainty as to the treatment of people with multimorbidity. Guidelines on the treatment of multimorbidity express reservations about the applicability of trial evidence to people with multimorbidity . MoreoveThe first implication for triallists and guideline developers relates to making better use of existing evidence. One way of doing so is via individual participant-level data meta-analyses. For this reason, we agree with the Alltrials initiative, and others, that sharing of IPD from clinical trials is crucial. Such analyses have helped resolve previous controversies about the efficacy of drugs in different sub-groups, showing, for example, that aspirin is similarly efficacious in men and women –34. SimiHowever, compared to meta-analysis of published results, IPD meta-analysis is costly and challenging. If trials are to be widely used to inform clinicians and guideline developers as to the efficacy of different treatments in the presence of comorbidity or multimorbidity, trials must publish results according to comorbidity sub-groups. Doing so will be challenging, however, because there are multiple different potential patterns of comorbidity. This is true even if only a small number of comorbid diseases are considered. There are, for example, 64 different possible ways that six conditions can occur together. Whether important and clinically relevant patterns of comorbidity can be identified from among such combinations remains an active and unresolved research question . NonetheIn the absence of consensus on which patterns of comorbidity should be grouped together, we propose that trials report treatment effects according to the presence/absence of common comorbidities, as well as by multimorbidity counts. Ideally, comorbidities would be defined using medical history data collected in a systematic and standardised manner across trials. In the absence of standardised medical histories , 37, somDespite these challenges, using clinical trial data to estimate treatment effects in people with comorbidity or multimorbidity remains appealing because of limitations in the alternatives. For example, observational datasets rich in multimorbidity, such as electronic health records, are used to estimate treatment effects. However, despite methodological advances in this use of observational data, it remains controversial, as unmeasured confounding can result in apparent treatment benefits when none really exist , 38.The second implication for triallists relates to eligibility criteria and recruitment. For many indications, there was little difference in comorbidity counts between some trials and the community sample, whereas for other trials within the same indication the differences were large. This suggests that, even for standard industry-funded phase 3/4 trials, increasing the recruitment of comorbid participants is feasible. There is therefore potential for future trials to become more representative in terms of multimorbidity. In exploratory analyses, the differences in comorbidity between trials for similar indications were not related to start date, phase, sponsor or total number of exclusion criteria. Additional work is needed to identify the selection processes driving inclusion or exclusion of people with comorbidity so that trials can be made more representative. In addition, it will be important for future research to examine how conditions cluster in people with multimorbidity and whether this differs between clinical trial participants and people in the community in order to improve analysis and reporting of treatment effects as well as trial design.The strengths of our study include large numbers and that the comorbidity definitions and analyses were pre-specified before making comparisons. However, there are several limitations. First, the trials collected medical history data in a variety of incommensurable ways. Consequently, we used concomitant medications to define comorbidities. This meant that some important conditions that are not treated with specific medications (e.g. chronic kidney disease) could not be identified reliably, whereas some other conditions which share treatments (e.g. asthma and COPD) had to be combined into broader categories. The use of some medications was so heterogenous as to preclude meaningful categorisation, and we did not attempt to use such drugs in any definition (for example, since amitriptyline is widely used in the treatment of chronic pain , we did Clinical trial populations have a lower prevalence of comorbidity and multimorbidity than unselected community populations. Clinicians should exercise caution when applying disease-specific evidence and guidelines to people with comorbidity or multimorbidity. Nonetheless, comorbidity and multimorbidity are common in clinical trials. Given the limitations of observational data for estimating treatment effects, this suggests that standard industry-funded clinical trials are an underused resource for estimating treatment effects in multimorbidity. We would recommend that future disease-specific guidelines need to incorporate information concerning likely treatment effects in the context of the specific index condition and comorbidity or multimorbidity. To enable guideline developers to do so, triallists should at least report the prevalence of multimorbidity and a range of comorbidities among trial participants and should consider reporting treatment effect estimates stratified by comorbidity and/or multimorbidity. More general multimorbidity guidelines could also usefully include information in relation to this within any future guideline to permit more specific guidance for clinicians dealing with people with multimorbidity.Additional file 1. Selection-of-trials-protocol.pdf: Protocol for selection of clinical trials individual participant data and search of wider body of registered trials from clinicaltrials.gov.Additional file 2. Representativeness-of-community-data-sail.pdf: Analysis of the representativeness of the community sample.Additional file 3. Selection-of-patients-and-participants-from-primary-care-data-read-codes.pdf: Read codes used to identify index conditions.Additional file 4. Defining-comorbidity-protocol.pdf: Protocol detailing the identification of comorbidities from clinical trial data.Additional file 5. More-detailed-statistical-analysis.pdf: Model description and code required for analyses. Detailed description of statistical methods.Additional file 6. Trials-characteristics.pdf: Summary of characteristics of included trials.Additional file 7. Characteristics-of-primary-care-populations-with-each-of-the-trial-indications.pdf: Summary statistics of community sample for each index condition.Additional file 8. Summary-Statistics-Comparing-Ipd-Trials-To-Wider-Body-Of-Trials-From-Clinicaltrials.Gov.Pdf: Comparison of included trials with registered trials on clinicaltrials.gov for which individual participant data were not available.Additional file 9. Proportion-with-each-comorbidity-for-trials-and-sail.pdf: Analysis of the prevalence of each comorbidity, within each index condition, in trial participants and the community sample.Additional file 10. Comorbidity-counts-for-trials-and-primary-care.pdf: Summary comorbidity counts.Additional file 11. Explore-relationship-of-trial-mean-comorbidity-counts-to-trial-characteristics.pdf: Analysis of characteristics based on trial meta-data and relationship to comorbidity counts.Additional file 12. Figure-4-interactive.svg: Interactive version of Fig.