Systematic reviews of moxibustion for LDH have identified ponderable evidence, especially for heat-sensitive moxibustion (HSM). Therefore, we designed and carried out the large sample trial to evaluate it. 456 patients were recruited from 4 centers in China and were randomly divided into three groups by the ratio of 1 : 1 : 1 to HSM (152) group, conventional moxibustion (152) group, and conventional drug plus acupuncture (152) group. Compared with usual care, there was a statistically significant reduction in mean M-JOA score at 2 weeks and 6 months for HSM  and conventional moxibustion . Compared with conventional moxibustion group, HSM group showed greater improvement in all the outcomes. The mean dose of moxibustion was 41.13 ± 5.26 (range 21–60) minutes in the HSM group. We found that HSM was more effective in treating patients with LDH, compared with conventional moxibustion and conventional drug plus acupuncture. This finding indicated that the application of moxibustion on the heat-sensitive points is a good moxibustion technique in treating disease. Artemisia vulgaris, at specific acupuncture and moxibustion point on the skin  . HoweverThe aim of this study is to assess the effectiveness of heat-sensitive moxibustion for treating LDH compared with conventional drug plus acupuncture as well as conventional moxibustion.An effect size on the M-JOA was sought when comparing the heat-sensitive with conventional moxibustion. In our previous pilot study, the effective rate in heat-sensitive moxibustion group is 65% and 45% in the other groups. An allocation ratio of 1 : 1 : 1 was chosen in order to increase power to detect statistically significant differences between the three groups. With 90% power and a two-sided significance level of 5%, the required group sizes were 126. Allowing for 20% attrition, the total sample size required was 456 :We performed a multicenter (four centers in China), randomized, assessor blinded, and positive controlled trial. Our trial was carried out in four hospitals in China, including the Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine (TCM) in Nanchang, the first Affiliated Hospital of Anhui University of TCM in Hefei, Jiangsu TCM Hospital in Nanjing, and Shanxi TCM Hospital in Xian. Patients were recruited through hospital-based recruitment and newspaper advertisements. After a baseline phase of one week, we used a central randomization system  to randomize patients . All stuPatients were recruited in China from December 30, 2011, to January 30, 2013. Informed consent was obtained from each subject, and the Ethics Committee of Affiliated Hospital of Jiangxi Institute of Traditional Chinese Medicine, China, approved the study protocol, authorization number: 2008(11).Inclusion criteria were a diagnosis of LDH according to the guiding principle of clinical research on new drugs (GPCRND) , at leasMain exclusion criteria were patients with serious life-threatening disease, such as disease of the heart and brain, blood, vessels, liver, kidney, and hematopoietic system, pregnant or lactating female, and psychotic patients. We also excluded patients with a single nerve palsy, or cauda equina nerve palsy, manifested as muscle paralysis or having rectum or bladder problems; complicated with lumbar spinal canal stenosis and space-occupying lesions or for other reasons; complicated with lumbar spine tumors, infections, tuberculosis; complicated with moxibustion syncope and unwilling to be treated with moxibustion; patients do not sign informed consent.We developed the study interventions in a consensus process with China acupuncture experts and societies. Physicians trained and experienced (at least five years) in acupuncture delivered the interventions. All treatment regimens were standardized between four centers practitioners via video, hands-on training, and internet workshops. In the moxibustion groups, 22 mm (diameter) × 120 mm (length) moxa-sticks  were used. The patient was usually in the comfortable supine position for treatment, with 24°C to 30°C temperature in the room.For the heat-sensitive moxibustion group, moxibustion treatment was defined as burning a moxa-stick with the patient lying on his or her back. The moxa-sticks were lit by the therapist and held over the region among two Dachangshu (BL25) and Yaoshu (Du 2) of patients. The moxa-stick suspended at an approximate distance of 3 cm was used to search for acupuncture points showing the heatsensitisation phenomenon. The following patients sensation suggested the special heat-sensitization acupuncture points: heat penetration, patients reporting heat penetrating from the skin into subcutaneous tissues; heat expansion, heat expanding away from the stimulation site to surrounding cutaneous and subcutaneous tissues; heat transmission, patients perceiving a stream of heat conducting in certain directions or perceiving heat in some body regions or into the joint cavity; nonthermal sensations, instead of thermal sensations, some patients perceiving aching, heaviness, pain, numbness, pressure, or cold in local or distant locations of stimulation. When such an acupuncture point was found, the therapists marked the point. We tried our best to seek all the special acupuncture points in each patient by the repeated manipulation.The therapists began to treat patients from the most heat-sensitive intensity acupoint. Treatment sessions ended when patients felt the acupoint heat-sensitization phenomenon had disappeared. Generally speaking, one point was selected each time. One point was treated 30~60 minutes. Patients received the treatment for two times daily in the first four days and for one time daily in remaining ten days. The whole treatment contained 18 sessions over 14 days.A licensed doctor performed fixed acupuncture point moxibustion. Common practices were similar to the first group. The different manipulation was that the therapists carry out warming moxibustion in traditional acupuncture point, selecting Dachangshu (BL25), Weizhong (BL40), and A-shi Xue. One point is treated 15 minutes a time. The whole process of moxibustion took about 45 minutes for each session. Patients usually felt local warmth without burning pain and might experience mild hyperemia in the local region. The sensation of acupuncture point heat-sensitization phenomenon was not pursued and not avoided in the treatment. Patients received the treatment for two times daily in the first four days and for one time daily in the remaining ten days. The whole treatment contained 18 sessions over 14 days.For conventional drug, patients received the 20% mannitol  and Voltaren tablets  in the first 3 days. Voltaren tablets were continued to be used in the subsequent 11 days. At the same time, acupuncture needles were used and acupuncture points selected from Bladder Meridian of Foot-Taiyang and Gallbladder Meridian of Foot-Shaoyang. Acupoints included Dachangshu (BL25), Yaojiaji (EX-B2), Huantiao (GB30), Weizhong (BL40), Yanglingquan (GB34), Xuanzhong (GB39), and Qiuxu (GB40). We selected bilateral acupoints located in waist and ipsilateral acupoints located in lower limbs. Needles remained in acupuncture point for 30 minutes. Patients received the acupuncture needle treatment one time/day in two weeks for a total of 14 sessions over 14 days.Our primary outcome measure was the M-JOA. The JOA has proposed a series of criteria to define patient response in the context of clinical trials of LDH. M-JOA scale is a modified edition of JOA Back Pain Evaluation Questionnaire. According to these criteria, a patient with LDH is assessed for pain, the ability to conduct daily life and work, functional impairment, and particular clinical examinations. M-JOA scores range from 0 to 24, with LDH considered mild (0–9), moderate (10–20), or severe (21 and above). The M-JOA was used as a preference-based measure of health outcome. All patients were assessed before randomization (baseline phase), 2 weeks after randomization, and 6 months after the last treatment. This trial also recorded adverse effects reported by patients during treatment.We ensured assessor blinding in this trial. Patients were informed not to tell outcome assessors the treatment they received. The outcome assessor was not involved in treatment administration.t-tests used to assess changes between baseline and 2 weeks within each arm. ANOVA was used to compare these changes among the three treatment arms of the trial. Where a significance difference was found among the three groups, pair-wise tests were used to determine specifically which groups differed significantly. Student-Newman-Keuls was used for pairwise comparison. All adverse reactions manifested were listed with detailed explanations. A significance level of 5% was used in all analyses.Data were analysed on an intention-to-treat (ITT) basis including all randomised participants with at least one measurable outcome report. The statistician conducting the analyses remained blinded to treatment groups. All analyses were conducted using SPSS 11.5. The groups were compared on 2 weeks, with P > 0.05, Fisher exact test).Participants were recruited from outpatients and inpatients in the four study centers. Patient flow in the trial was presented in P < 0.01). ANOVA test showed significant difference in the three groups at both time points. Mixed-effects model analysis (q-test) showed that subjects in the heat-sensitive moxibustion group had significantly greater reduction in M-JOA scores than those in conventional moxibustion group or conventional drug plus acupuncture group at 2 weeks and 6 months; however, there was no significant difference between conventional moxibustion and conventional drug plus acupuncture at both time points . The importance of the therapeutic relationship providing heat-sensitive acupuncture point should not be underestimated in the moxibustion therapy. Therefore, the success of this project is more than providing the efficacy of heat-sensitive moxibustion as a treatment modality in patients with LDH. The findings will be helpful to provide better therapeutic options to enhance the efficacy of moxibustion and to perfect acupuncture point heat-sensitive theory.


Research has shown that microRNAs are promising biomarkers that can be used to promote a more accurate diagnosis of cancer. In this study, we developed an integrated multi-step selection process to analyze available high-throughput datasets to obtain information on microRNAs as cancer biomarkers. Applying this approach to the microRNA expression profiles of prostate cancer and the datasets in The Cancer Genome Atlas Data Portal, we identified miRNA-182, miRNA-200c and miRNA-221 as possible biomarkers for prostate cancer. The associations between the expressions of these three microRNAs with clinical parameters as well as their diagnostic capability were studied. Several online databases were used to predict the target genes of these three microRNAs, and the results were confirmed by significant statistical correlations. Comparing with the other 18 types of cancers listed in The Cancer Genome Atlas Data Portal, we found that the combination of both miRNA-182 and miRNA-200c being up-regulated and miRNA-221 being down-regulated only happens in prostate cancer. This provides a unique biological characteristic for prostate cancer that can potentially be used for diagnosis based on tissue testing. In addition, our study also revealed that these three microRNAs are associated with the pathological status of prostate cancer. Prostate cancer (PCa) is the second most frequently diagnosed cancer and is the sixth highest cause of cancer-related death among men worldwide . It is aStudies have suggested that microRNA (miRNA), a type of endogenous, small, non-coding RNA with an approximate length of 22 nucleotides , may be Today, high-throughput technologies have produced a large amount of cancer data, so it is desirable to use these data to identify miRNAs and the aberrations that are associated with different types of cancers. However, the analyses of these high-throughput data face difficulties. One difficulty is the lack of homogeneity among different sets of miRNA data due to the fact that different platforms were used to acquire them. Different sets of miRNA data that are expressed differently tend to show inconsistencies with each other. Among the methods developed to address this problem, the robust rank aggregation (RRA) method, which defines the rank vector for each gene based only on the datasets where it is present, has been shown to provide statistically significant miRNA meta-signatures , 12. TheIn this study, we applied a new multi-step selection approach to the existing high-throughput data of PCa for the purpose of identifying miRNAs as PCa biomarkers. We first applied the RRA method to select potential miRNA biomarkers in prostate tumors using 11 published miRNA expression profiles. Then, we used The Cancer Genome Data Atlas (TCGA) to further verify the selected miRNAs in multiple ways by the Wilcoxon rank sum test. We found that the combination of two up-regulated miRNAs  and one down-regulated miRNA (miRNA-221) is unique in PCa. This suggests that this combination of miRNAs and their expression levels could potentially provide additional effective diagnostic indicators for PCa.http://www.ncbi.nlm.nih.gov/geo/) [http://www.ebi.ac.uk/arrayexpress/) repositories [Homo sapiens and (d) viral miRNAs and non-miRNA probes were excluded.The information on miRNA expression profiling studies on PCa was systematically searched in PubMed, Embase and Highwire databases, using the search string (prostate and (cancer* OR tumor* OR tumour*) and (mirna* OR microrna * OR mir-*)). In addition, we obtained miRNA expression profiles for PCa through searching the Gene Expression Omnibus (GEO) (ov/geo/)  and Arrasitories . The seaIn this study, the non-tumor tissues included prostate tissues adjacent to a tumor and tissues from independent healthy donors, but did not include BPH. The data extracted from each study included: first author, Gleason score, region, assay type, the number of miRNA probes and the number of samples. The lists of miRNAs with statistically significant expressions were either extracted from the publications or obtained from the authors directly.http://gdac.broadinstitute.org/). Reads per kilobase of exon model per million mapped (RPKM) normalized values for mRNA expressions and reads per million miRNA mapped (RPM) normalized values for miRNA expressions were further log2-transformed. Fold changes in miRNA expressions between tumors and normal tissues were calculated using median-centered RPM values. Based on raw read counts, differential mRNA expression analysis was performed by the DESeq Bioconductor package in R [2 fold changes were greater than 1 and the false discovery rates (FDR) were < 0.1.The data including normalized miRNA-HiSeq expression values, raw read counts of mRNA-seq and clinical information for PCa were downloaded by the “TCGA-Assembler” package in R . Level 3age in R , which uTarget gene predictions of differentially expressed miRNAs were performed using TargetScan , miRwalkp-values (less than 0.05 was considered significant). If the p-value was not reported, we used the fold change (FC) instead. Leave-one-out cross-validation (LOOCV) was performed after the RRA analysis to assess the stability of the acquired p-values. We found that the p-values stabilized after 10,000 runs of this analysis. We thus used 10,000 tests as the cutoff, excluded one random miRNA list for each test, and used the average p-value of each miRNA as the final p-value.The RRA method we used was adopted from a previous study . We intep-values of the miRNAs were estimated using the “cor.test” function in R. The relationships among clinical features and the expressions of the miRNAs were evaluated by Wilcoxon rank sum or Kruskal-Wallis non-parametric test using the “wilcox.test” function or the “kruskal.test” function in R, respectively. The diagnostic capabilities of miRNAs were assessed using the “pROC” package in R. All statistical calculations were performed on log2 transformed expression levels.Then, the Spearman’s rank correlation coefficients and the two-tailed p-value was tested, miR-375 (p = 0.053 > 0.050) and miR-25-3p (p = 0.074 > 0.050) were excluded, and the other six meta-signature miRNAs (p < 0.050) were kept for further analysis. These six selected miRNAs included two up-regulated miRNAs  and four down-regulated miRNAs .Our selection criteria see  resultedp -value cut-off was set to 0.05 and the FC cut-off was set to 2 in order to provide more rigorous identification indicators to differentiate miRNAs. Based on the analysis of 498 PCa samples and 52 normal samples, we found that three statistically significant miRNAs were in agreement with the analysis result of the expression profiling by RRA , and they were negatively correlated with down-regulated miR-221 . Therefore, we carried out further analysis to confirm these three miRNAs and their unique combination of expression levels in PCa.Next, we used the Spearman rank correlation to test the association among the three identified miRNAs in PCa. We found that miR-182 and miR-200c showed a positive correlation  and miR-200c  showed overexpressions and miR-221  showed no significant difference in the series GSE24201 [2 FC = 1.187, p = 0.010) and miR-200c  were also up-regulated and reached p < 0.05, while miR-221  did not show a different trend.To explore the expression levels of miR-182, miR-200c and miR-221 in PCa blood or serum, we used GEO2R  to analyGSE24201 , which iGSE24201 , and we p < 0.05 for all comparisons) (see The clinicopathologic features of the PCa patients obtained from TCGA are shown in ons) see , and it p < 0.0001) showed a higher diagnostic capability than miR-182  and miR-221 . When a logistic regression approach was used for the combination of these three miRNAs, the ROC curve revealed a much better diagnostic accuracy than if they were used individually, the results showed an AUC of 0.972 . In the analysis, the optimal cutoff value was set at a maximal sum of sensitivity and specificity. The diagnostic sensitivity and specificity of the combination of these three miRNAs were found to be 94.2% and 92.6%, respectively.We conducted response operating characteristic (ROC) analyses to evaluate the use of the three selected miRNAs as potential biomarkers to differentiate PCa tissues from normal tissues , and we RIMS3) consistently overrepresented with all three miRNAs.First, we identified 800 up-regulated and 1698 down-regulated different genes based on a model using the negative binomial distribution for 374 prostate cancers samples and 52 normal controls from TCGA . Second,To understand the general biological functions of these three miRNAs, we conducted Protein Analysis Through Evolutionary Relationships (PANTHER) pathway analysis in DAVID via the target genes. The results showed that these three miRNAs did not share the same pathways, but they were frequently related to cell signaling pathways .In recent years, many studies have been devoted to the discovery of miRNA biomarkers and their biological functions (or molecular mechanism) for PCa. In this study, we applied an integrated multi-step selection approach to analyze the cancer datasets from GEO and TCGA and identified three miRNAs and their unique expression combination pattern that only showed in the PCa datasets.The notion that miRNAs are implicated in cancer is supported by the evidence that miRNAs are located largely in genomic regions associated with cancer or at fragile sites , 37. Forvitro and vivo [FOXF2, which decreases PCa cell invasion and migration [SNAI2, which has been demonstrated to be a repressor of proliferation in PCa cells [FOXF2 and SNAI2. The miRNA-200 family, consisting of five members , is treated as a tumor suppressor as it inhibits epithelial-to-mesenchymal transition, tumor cell invasion and metastasis [JAGGED1, which may be important for metastases [p27/kip1 and inhibit several cyclin-dependent kinase complexes [It was also reported that miRNA-182 is over-expressed in PCa and plays an active role in proliferation and invasion in and vivo , 42. It and vivo . AssociaCa cells , 44. Ourtastasis . The miRtastases . It has tastases  that miRtastases . In additastases . Also, momplexes , 51. AllIncreasing in age is correlated with PCa risk , 53, sinSince tumor cells can release miRNAs into the body’s circulation , it is dRIMS3 was present simultaneously with the three identified miRNAs. This has not been reported for PCa before. Also some of the pathways associated with PCa in which the three identified miRNAs participate have been reported before. For examples: (1) It has been reported that, together with TMPRSS2-ERG, the PI3-kinase pathway activates prostate oncogenesis [Through our analyses of target genes and correlations, we found that down-regulated ogenesis ; (2) Wntogenesis ; and (3)ogenesis . All of To the best of our knowledge, this study is the first to identify and analyze combinations of multiple miRNAs as a potential biomarker for PCa based on the analysis of miRNA microarray and TCGA datasets. Our study shows that an integrated analysis method based on RRA and followed by other statistical analyses and verifications can effectively identify combinations of multiple miRNAs as potential cancer biomarkers. Our results suggest that the three selected miRNAs and their uniquely combined expression pattern in PCa could be of clinical value in addition to existing testing methods. Finally, although we only applied the proposed multi-step selection approach to study high-throughput datasets for the purpose of identifying potential biomarkers for PCa, we believe that this approach can also be applied to investigate other cancer types using similar high-throughput datasets.S1 PRISMA Checklist(DOC)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.


Patients with TRD had increased right-thalamic fALFF values compared with patients without TRD. The percent change in HRSD17 scores negatively correlated with fALFF values in patients with non-TRD. In addition, patients with TRD showed increased fALFF values in the right inferior frontal gyrus (IFG), inferior parietal lobule (IPL) and vermis, compared with patients with non-TRD and HC subjects. Our results show that spontaneous activity in the right thalamus correlates with antidepressant treatment response. We also demonstrate that spontaneous activity in the right IFG, IPL and vermis may be specifically implicated in the neural pathophysiology of TRD.Despite novel antidepressant development, 10–30% of patients with major depressive disorder (MDD) have antidepressant treatment-resistant depression (TRD). Although new therapies are needed, lack of knowledge regarding the neural mechanisms underlying TRD hinders development of new therapeutic options. We aimed to identify brain regions in which spontaneous neural activity is not only altered in TRD but also associated with early treatment resistance in MDD. Sixteen patients with TRD, 16 patients with early-phase non-TRD and 26 healthy control (HC) subjects underwent resting-state functional magnetic resonance imaging. To identify brain region differences in spontaneous neural activity between patients with and without TRD, we assessed fractional amplitude of low-frequency fluctuations (fALFF). We also calculated correlations between the percent change in the Hamilton Rating Scale for Depression (HRSD Using independent component analysis, patients with depression showed increased network functional connectivity in the subgenual cingulate and the thalamus,24 Thalamic metabolism in depressive patients decreased along with their remission25 and resting-state FC between the medial thalamus and dorsal anterior cingulate normalized following treatment with sertraline in depressed patients.26 Another study demonstrated that treatment resistance was associated with increased KCC-ReHo values in the right insula, bilateral anterior cingulate cortex and bilateral medial frontal gyrus.27 Treatment resistance was also associated with decreased KCC-ReHo values in the left lateral frontal gyrus, bilateral IPL and left superior parietal lobule.27 A study utilizing 2-[18F]fluoro-2-deoxy-d-glucose positron emission tomography revealed that in the rostral cingulate gyrus, drug treatment in nonresponsive patients with MDD decreased glucose metabolism, whereas this parameter increased in treatment-responsive patients.28 A voxel-based morphometry study showed that differences in the frontal, temporal, parietal, occipital and subcortical regions are strong predictors of treatment responsiveness before antidepressant use,29 and associated with the time between initiation of treatment and recovery.30 A separate study demonstrated that stronger task-related activations in the anterior mid-cingulate in addition to the pregenual and subgenual anterior cingulate cortices were associated with a more rapid decrease in depressive symptoms.30 Moreover, hippocampal activation in response to images depicting faces with happy emotions correlated with treatment responsiveness after 8 weeks of treatment.31 In addition, subgenual cingulate and parahippocampal region activation in response to images of sad faces predicted a strong response to antidepressant treatment.32In contrast, neuroimaging studies have also identified brain regions that are associated with responsiveness to pharmacotherapy in patients with MDD. For instance, in a resting-state fMRI study, disrupted FC between the frontal lobes and thalamus was associated with treatment resistance in patients with MDD.The results of these studies suggest that patients with TRD have different resting-state spontaneous neural activity compared with patients with treatment-sensitive depression. Moreover, these studies indicate that in some brain regions, spontaneous neural activity, neural responses to facial emotions and gray matter volumes predict antidepressant treatment responsiveness. We therefore hypothesized that different spontaneous regional neural activity in patients with TRD and non-TRD would be associated with antidepressant treatment responsiveness in the early phase of treatment. The purpose of this study was to not only determine brain regions in which spontaneous neural activity was altered in patients with TRD, but also to identify brain areas associated with antidepressant responsiveness in patients with non-TRD. In addition, we investigated the brain regions in which spontaneous neural activity differed between patients with TRD, patients with non-TRD and HC subjects.33 (b) diagnosis of neurological illness, (c) left-handedness, as defined by a score less than 0 on the Edinburgh handedness test.34Healthy volunteers were recruited in the control group. Volunteers were included if they were aged between 25 and 75 years. Subjects were excluded from the control group on the basis of the following criteria: (a) history of psychiatric illness, or current psychiatric symptoms, as determined by the Mini International Neuropsychiatric Interview,17)35 of 8 or more,36 (d) diagnosis of non-psychotic MDD and current depressive episode, as determined by an experienced psychiatrist according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR).1 The exclusion criteria for this group were as follows: (a) diagnosis of neurological illness, current or previous psychotic disorder, current high risk of suicide, current or previous substance abuse and serious somatic disease as determined by the Mini International Neuropsychiatric Interview33 conducted by trained valuators, (b) left-handedness, which was defined as a score less than 0 on the Edinburgh handedness test,34 (c) current pregnancy or nursing, (d) sufficient treatment and duration with one antidepressant to treat the current episode of depression, (e) use of two separate antidepressants for the current episode of depression, (f) use of mood stabilizers, antipsychotics or central nervous system stimulants, (g) treatment with electroconvulsive therapy within the past 3 months.The non-TRD group included patients who were either untreated or treated with a single antidepressant at an insufficient dose and duration and were recruited from the Hiroshima University and regional hospitals according to the following inclusion criteria: (a) age between 25 and 75 years, (b) outpatient status, (c) presentation of depressive symptoms, as determined by a score on the Hamilton Rating Scale for Depression  were recruited using inclusion criteria similar to those for patients with non-TRD. Patients with TRD were excluded using only criteria (a), (b) and (c) from the exclusion criteria used for patients with non-TRD. All the aforementioned inclusion and exclusion criteria were established before recruiting.Patients who were experiencing TRD according to the criteria established by European Medicines Agency guidelinesP<0.05, about 12 subjects were required to achieve 80% power at the single voxel level for a typical activation study.38 Consequently, we aimed to recruit more than 12 participants in each group.Because we used a liberal threshold of This study protocol was approved by the Ethics Committee of the Hiroshima University Graduate School of Biomedical and Health Sciences. Written informed consent was obtained from all the participants.1735 was used. Verbal intelligence was assessed by the Japanese Adult Reading Test.39 Patients with TRD were assessed for treatment resistance by using the Maudsley staging method (MSM).40 MSM assesses treatment resistance according to five factors: duration of presenting episode, severity of depression, antidepressant treatment failure, augmentation therapy use and electroconvulsive therapy use. In addition, patients with non-TRD were assessed by the HRSD17 following 6 weeks of selective serotonin reuptake inhibitor treatment to evaluate clinical improvement. Improvement was determined according to the percent change in HRSD17 scores, as calculated by the following formula: percent change=[{(HRSD17 score before medication)−(HRSD17 score after 6 weeks of treatment)}/(HRSD17 score before medication)] × 100.After receiving informed consent, we evaluated the severity of depressive symptoms for patients with TRD and non-TRD. To evaluate depressive symptoms, HRSDAfter clinical assessment, all the participants underwent a 5-min, whole-brain resting state fMRI and three-dimensional anatomical scans at the Kajikawa Hospital for evaluation of the spontaneous resting-state neural activity. MRI acquisition was performed using a Magnetom Spectra 3T scanner . A quadrature birdcage head coil was used to minimize the head movement. Before scanning, all the participants were instructed to remain motionless, keep their eyes closed, not think of anything in particular and refrain from sleeping to maximally reduce physiological noise in the fMRI data. A total of 112 volumes were recorded over 5 min using a gradient-echo T2*-weighted echo planar imaging sequence . Anatomical images were recorded over 8 min using a T1-weighted gradient-echo pulse sequence .41 software. The first 10 images for each session were discarded to allow for a steady state in longitudinal magnetization and participant habituation to the scanning environment. The remaining 102 images were realigned to the initial image to correct for movement. Subjects who had excessive head motion (>1.5 mm translation or 1.5° rotation) during the scan were excluded from further analysis. The functional images were then preprocessed, including slice timing correction and head motion correction, using a least squares approach with a six-parameter spatial transformation, and normalized to the Montreal Neurological Institute template (with a resampling voxel size of=3 × 3 × 3 mm). MRI images were then smoothed with an isotropic Gaussian kernel  and linear image trends were removed.Data were preprocessed using the Data Processing Assistant for Resting-State fMRI41 as defined previously.21 Although reporting both ALFF and fALFF values is recommended,10 we used only the fALFF method for the following three reasons. First, fALFF minimizes artifacts due to body motion, respiration and cardiac noise;42 it has been noted that low-frequency oscillation approaches are vulnerable to these effects.42 Second, compared with ALFF, fALFF has moderate intra- and inter-session test–retest reliability and a high sensitivity for detecting spontaneous neural activity in gray matter.10 Third, fALFF is more sensitive to spontaneous neural activity in the default mode network (DMN) than ALFF. Of relevance to this is a recent meta-analysis that reported hyper-connectivity in the resting-state in MDD.43 This technique has also been successfully used to detect altered spontaneous neural activity in patients with MDD.44 In many resting-state fMRI studies, preprocessed data is band-pass filtered in the low-frequency range (0.01–0.08 Hz) to reduce ultra-low-frequency drift and high-frequency respiratory and cardiac noise.45 In this study, fMRI signal time series data for each voxel were transformed to the frequency domain, and the power spectrum of the full band (0–0.25 Hz) was obtained. This approach was used because fALFF values were defined as the ratio of the power of each frequency at the low-frequency range (0.01–0.08 Hz) to that of the entire range (0–0.25 Hz). The square root was calculated for each power spectrum frequency, and the amplitude sum across 0.01–0.08 Hz was divided by that of the entire frequency range. We determined the aforementioned parameters for fALFF preprocessing and calculation according to previous research using this approach.44 Although we do not report ALFF analysis here, we do so in the To determine spontaneous neural activity, we calculated the fALFF using the Data Processing Assistant for Resting-State fMRI,For demographic and clinical variables, statistical analysis was carried out using the Statistical Package for the Social Sciences software, version 20 . We conducted the Shapiro–Wilk test and Levene's test to assess normality of distributions and equity of variance among variables. Subsequently, we conducted analyses of variance to test for differences for these variables among groups.http://www.fil.ion.ucl.ac.uk/spm), with age and sex as covariates. We then conducted the two-tailed, two-sample t-tests  for differences in fALFF values undetected by ANOVA. Because fMRI data in each group may not be equally distributed, even after removing the effect of artifacts by the above procedure, we conducted statistical analysis for fALFF under the assumption of unequal distributions. In addition, although the mean ages of three groups were not statistically different in this study, as a previous study showed an association between age and fALFF values,46 we included age as a covariate in the statistical analysis. Then, we set an uncorrected significance level of P<0.005 and a cluster size of k≥10, accounting for type I and type II errors.47 Brain regions with statistically significant differences in fALFF values were labeled using Anatomical Automatic Labeling software.48 To determine the brain regions associated with clinical improvement and treatment resistance, first we conducted partial correlation analysis between fALFF and percent change in HRSD17 scores in patients in the non-TRD group, using R49 software ver. 3.1.1. and ppcor, removing the duration of the current episode (month). Second, to determine the brain regions linearly associated with the spontaneous neural activity and treatment resistance, we conducted partial correlation analysis between fALFF and MSM scores in the TRD group, using R49 software and ppcor. Third, to determine the brain regions nonlinearly associated with fALFF and MSM scores, we calculated the maximal information coefficient50 using R49 software and minerva (http://mpba.fbk.eu/cmine). Mean fALFF values were extracted for the brain regions identified by two-sample t-tests (TRD compared with non-TRD groups) using the Mars Bar toolbox,51 because these regions include the effect of medication, duration of illness and treatment nonresponsiveness. We obtained the P-value for maximal information coefficient for a sample size of 20 from an online table . Statistical significance for two-tailed no correlation analysis was set at P<0.05.After preprocessing imaging data, we performed group analyses. To test our hypothesis that patients in the TRD, non-TRD and HC groups have different spontaneous neural activities, we conducted one-way ANOVA for fALFF at each voxel using Statistical Parametric Mapping 8 software  were analyzed. Age, sex, age of onset and intelligence quotient were not statistically different among the three groups. Duration of current episode was longer in patients with TRD than non-TRD . Both groups of patients showed moderate severity of depressive symptoms and depression severity (according to the HRSD17) was not significantly different between the patients with TRD and non-TRD before selective serotonin reuptake inhibitor treatment. Depressive symptoms among patients with non-TRD significantly decreased after 6 weeks of medication (t(15)=3.34, P<0.01). Demographic and clinical characteristics of the participants, including data for patients with non-TRD after 6 weeks selective serotonin reuptake inhibitor treatment, are shown in Data from 26 healthy volunteers, 16 patients with non-TRD and 16 patients with TRD (resistance severity according to Thase and Rush:n=5) had mild, 56.3% (n=9) had moderate and 12.5% (n=2) had severe treatment resistance. Treatment-resistance characteristics in patients with TRD, as assessed by the MSM, are shown in F-value and t-value maps of one-way ANOVA and two-sample t-tests for each fALFF value . t-test results for fALFF value comparisons .Compared with patients in the non-TRD group, patients with TRD showed increased fALFF values in the right IFG, right middle occipital gyrus, right thalamus, right IPL and vermis. Compared with those in the non-TRD and HC groups, patients with TRD showed increased spontaneous neural activation in the right IFG, right thalamus, right IPL  and vermis. In addition, compared with HC subjects, patients with MDD (those in the TRD and non-TRD groups) had common fALFF value increases in the precuneus and angular gyrus, and common decreases in the pre- and postcentral gyri. Moreover, patients with TRD showed decreased fALFF values in the bilateral calcarine cortex and left para- and pre-central gyri compared with HC subjects. Patients with non-TRD also had decreased fALFF values in left pre- and postcentral gyri compared with HC subjects. 17 scores =−2.187, P=0.029; 17 =1.277, P=0.224; middle occipital gyrus: r=0.376, t(13)=1.463, P=0.143; supramarginal gyrus: r=−0.043, t(13)=−0.155, P=0.876; vermis: r=−0.087, t(13)=−0.315, P=0.753).In patients with non-TRD, only the right thalamus showed negative correlation between mean fALFF values and percent change in HRSDr=0.083, t(13)=0.299, P=0.765; middle occipital gyrus: r=−0.275, t(13)=−1.030, P=0.303; thalamus: r=0.201, t(13)=0.741, P=0.459; supramarginal gyrus: r=−0.332, t(13)=−1.269, P=0.204; vermis: r=0.143, t(13)=0.521, P=0.602). In addition, nor were there any significant nonlinear correlations .No significant linear correlations were detected  with a prospective follow-up of the non-TRD group. Through this approach, we provided novel evidence that higher spontaneous resting-state neural activity in the thalamus might be a marker for treatment resistance. In the right thalamus, patients with TRD showed increased spontaneous neural activity compared with those with non-TRD. In addition, patients with non-TRD who had higher spontaneous neural activity showed lower clinical improvement on the HRSD54 Patients with MDD have been reported to exhibit greater neuronal density in the thalamus,52 greater regional cerebral blood flow therein53 and right-thalamic fALFF positively correlates with depressive symptoms.22 Thalamic metabolism decreases along with remisson,25 but increases after tryptophan depletion.54 Thus, although previous studies have suggested a possible association between thalamic hyper-spontaneous activity and TRD, the study reported here is the first to demonstrate this relationship directly using fALFF. Furthermore, in the context of resting-state FC, patients with TRD show decreased FC within the thalamo–cortical circuit24 and increased FC within the thalamus-subgenual ACC,8 which normalize after selective serotonin reuptake inhibitor treatment.26 These previous reports suggest alterations in functional connectivity and spontaneous neural activity in the thalamus in TRD in the resting state. The thalamus has a key role in the basal ganglia–thalamo–cortical circuits.55 In the context of mood disorders, three major prefrontal–striatal–pallidal–thalamic networks have been recognized.56 The first originates in the orbital/ventrolateral prefrontal cortex and appears to manage the emotional salience of external stimuli, as this network has strong connections to a variety of sensory brain regions. The second originates in the medial prefrontal cortex and appears to modulate internal mood states, given its connections to hypothalamic and consequently autonomic areas that presumably underlie emotions. The third originates in the dorsolateral prefrontal cortex, appears to be reciprocally linked to emotional networks and may consequently underlie cognitive and executive symptoms of depression.57 Taken together with the above results, it appears that thalamic hyperactivity is associated with treatment resistance in patients with TRD and non-TRD. Underlying this are the emotional salience network, emotion modulation network and cognitive/executive network, in which the thalamus is an integral part of the circuitry.Recently, evidence of an association between the thalamus and MDD has been reported. For instance, in the context of resting-state neural activity, previous studies have suggested an association between spontaneous neural activity in the thalamus and TRD.t-tests (TRD compared with non-TRD and TRD compared with HC groups) revealed that patients with TRD had increased resting-state spontaneous neural activity in the right triangular part of the IFG and vermis than non-TRD and HC subjects. Previous studies have shown right frontal gyrus activity impairment in depression.59 Consistent with mood disorders, IFG is thought to contribute to emotion regulation.60 The right IFG is more strongly involved in ‘suppression', which can decrease negative affect significantly but prolong neural activity in the amygdala and insula.61 Thus, hyperactivity of the right IFG in patients with TRD may indicate an altered state of emotion regulation, which may cause a greater need to voluntarily regulate emotion in the resting-state, or more use of a ‘suppression' strategy to decrease negative affect. Furthermore, the right IFG has a crucial role in localized attention and suppression of responses to new stimuli,63 suppression of memory and emotion65 and comprehension of affective prosody.69 Considering these reports, our data suggest that patients with TRD are likely to have changes in attention, emotion, sensitivity to new stimuli and prosody comprehension during resting states.Regarding the specific pathophysiology of TRD, we showed that patients with TRD had stronger spontaneous neural activity in right triangular part of the IFG, right IPL and vermis compared with patients with non-TRD and healthy participants. Two-sample 71 which consists of areas that are active when an individual is awake and alert, but not actively involved in an attention demanding or goal-directed task, and which deactivate during the performance of cognitive tasks.72 The DMN is detectable using task-free functional connectivity MRI and has been implicated in self-referential activity, episodic memory retrieval and emotion modulation.72 Altered DMN activity is thought to be a result of rumination in MDD.73 Thus, one interpretation of our results is that patients with TRD are highly self-referential and exhibit a ruminative state during wakeful rest. Furthermore, previous research suggests that the supramarginal gyrus is associated with phonological working memory rather than visual working memory.74 These combined results predict that patients with TRD may have altered phonological and prosodic processing reflected by right IFG and supramarginal gyrus spontaneous neural activity. The results of this study are also consistent with previous reports suggesting that patients with MDD and a long history of antidepressant use have increased anterior vermis volume compared with HC subjects.75 Although the anterior vermis is thought to have a role in sensorimotor functions,77 recent research also suggests that this region may mediate non-motor functions such as verbal learning and memory,78 social cognition,79 reward systems80 and addiction.80 Thus, our findings suggest that treatment resistance may be associated with alterations in the vermis, which affects both motor and cognitive functions.The IPL is a brain region involved in the DMN,t-tests revealed that patients with MDD (both TRD and non-TRD) had stronger spontaneous precuneus and right IPL  neural activity than HC subjects. Precuneus and IPL are associated with the DMN,71 which is involved in self-referential activity, episodic memory retrieval and emotion modulation.71 Previous research has demonstrated that patients with MDD have hyperactivity in the DMN.73 A recent meta-analysis also concluded that hyper-connectivity in DMN brain regions is characteristic of patients with MDD,43 potentially as a result of excessive rumination.73 The common increased spontaneous neural activity identified in patients with MDD (TRD and non-TRD) in our study is consistent with previous reports. Thus, upregulation of DMN spontaneous neural activity may be a common characteristic of both MDD and TRD, reflecting this highly ruminative state during wakeful rest.As predicted by previous studies, two-sample 44 The recovery rate following 8 weeks of fluoxetine administration was found to be associated with occipital cortex gray matter volume.30 Thus, our findings provide further evidence that the occipital and calcarine cortices are associated with TRD.We also identified an additional set of brain regions with altered spontaneous neural activity in TRD. Patients with TRD showed increased fALFF scores in the occipital and calcarine cortices compared with non-TRD and HC groups. This finding is consistent with previous research reporting that fALFF values in this region can accurately distinguish patients with TRD from healthy control subjects (sensitivity: 81.8% specificity: 73.3%).19 Interestingly, patients with TRD showed decreased voxel-mirrored homotopic connectivity in the postcentral gyrus.81 Thus, our study provides further evidence of alterations in this brain region. Because KCC-ReHo scores are similar for neural activity time series of particular brain areas and neighboring regions,17 this result may reflect activity instability in the postcentral gyrus.Among patients with non-TRD, we identified decreased fALFF values in left postcentral gyrus. Previous research reported that patients with MDD show decreased KCC-ReHo values in this region.17 scores in patients with non-TRD (see 82 that demonstrated patients with treatment-nonresponsive MDD increase spontaneous neural activity in the right lingual gyrus compared with patients having treatment-responsive MDD. There was also a positive correlation between decreased HRSD17 scores and mean ALFF of the right lingual gyrus in patients with treatment-nonresponsive MDD.82 To reveal the function of low-frequency oscillations, further studies should use both measures. Fifth, although patients in this study were recruited according to Thase and Rush criteria, additional research is required to reveal the effect of severity of symptoms according to other criteria, as used elsewhere.20 Finally, although we used thresholds recommended for balancing type I and type II error,47 our findings must be considered preliminary because we did not use multiple comparisons. We propose the following future lines of inquiry: first, comprehensive research coupling resting-state neural activity, task-related activation and network analysis would be needed to reveal alterations of emotion regulation in patients with TRD. Second, to prevent treatment resistance, it is necessary to develop an accurate diagnostic classification system for treatment response, such as machine learning using multiple clinical variables encompassing different modalities.Several limitations should be considered when interpreting the present study results. First, on the basis of small sample size, we cannot conclude definitively that the results of this study reflect the trait marker of TRD. Second, we used a cross-sectional design, making the distinction between identifying treatment-resistance vulnerability markers and progressive occurrences during the course of the illness difficult. However, by adding a prospective follow-up of the non-TRD group, we provide indirect evidence that higher resting-state spontaneous neural activity in the thalamus may be a valid vulnerability marker for treatment resistance, rather than an artifact occurring during the course of TRD. Third, medication use may be a potential confounding variable, as patients with TRD had differing antidepressant regimens during fMRI scans. An appropriately designed study, which is prospectively designed, medication-controlled and uses a larger sample size is therefore needed to test the reliability of these results and determine the trait marker. Fourth, because of our interest in the DMN, we did not use ALFF values in this study. However, in the right lingual gyrus, patients with TRD showed lower spontaneous neural activity compared with non-TRD, and lower activity correlated with lower percent change in HRSD-TRD see . This re83 Thus, resting-state fMRI along with fALFF could be used as complementary assessment methods for predicting treatment response in the early phase of treatment. Further, fALFF may allow the suggestion of suitable treatments based on the neurological profile of individual patients.In summary, this study reveals that spontaneous thalamic hyperactivity has a key role not only in patients with TRD, but also in the early phase of antidepressant treatment resistance among patients with MDD. In addition, we show that spontaneous activity in the right IFG, IPL and vermis may contribute to the specific neural substrates underlying TRD. These results suggest altered cognitive and emotion regulation neural circuits in patients with TRD. Regarding clinical relevance, the results of this study might contribute to personalized treatment in patients with MDD, based on neurobiological features. Resting-state fMRI is much easier to acquire in a routine clinical setting than standard fMRI paradigms.


Background: There is increasing evidence that bipolar disorder (BD) and conduct disorder (CD) are co-occurring disorders. Magnetic resonance imaging has revealed differences in the structure and function of the frontal cortex in these disorders when studied separately; however, the impact of BD comorbidity on brain structure in adolescents with CD has not yet been examined. Method: We conducted an optimized voxel based morphometry (VBM) study of juvenile offenders with the following diagnoses: conduct disorder with comorbid bipolar disorder , conduct disorder without bipolar disorder  and healthy controls . Participants were 13–17 years of age, in a residential treatment facility for repeat offenders. The three groups in this study were similar in age, gender, socioeconomic status and ethnicity. Results: We found CD-BD subjects had decreased volume relative to controls at the voxel level in the right medial prefrontal cortex (PFC). Using a Threshold-Free Cluster Enhancement (TFCE) technique, the CD-BD subjects had significantly decreased volumes of the right medial prefrontal cortex and portions of the superior and inferior frontal gyrus, anterior cingulate and temporal gyrus. The CD subjects did not have differences in brain volume compared to control subjects or CD-BD subjects. Conclusions: Our findings suggest the comorbidity between CD and BD is associated with neurobiological impact namely volumetric differences from healthy controls. Furthermore subjects with this comorbidity had poorer lifetime functioning, more mood and attentional dysfunction, and more medication exposure than subjects with CD who were not BD. Despite evidence that antisocial adults have decreased volumes in the frontal regions ,2,3,4 teA potential source of heterogeneity in CD adolescents may be co-occurring conditions. Although the co-occurrence of attention deficit hyperactivity disorder (ADHD)  and subsSupporting evidence of the co-occurrence of BD and CD also comes from studies that focus on BD samples. These studies find high rates of comorbid externalizing disorders  and ADHD) and/or delinquent behaviors ,24,25,26Although the co-occurrence of BD and CD is well recognized, little is known about the neural systems underlying this phenotype. In an earlier study we demonstrated neuropsychological deficits in adolescents with CD; however, the most robust neuropsychological deficits were accounted for by the presence of comorbid BD . In thisAll patients were assessed at a secure, post adjudication, residential treatment center where they arrived after a 4–8 week stay at the county juvenile detention center. Healthy controls (HC) were assessed at the outpatient clinical office of the principal investigator. Subjects were males and females between the ages of 13 and 17 years. To confirm diagnoses, all subjects underwent a semi-structured interview with the parent and child separately, then together to reconcile any discrepancies, administered by a board certified child and adolescent psychiatrist (Rene L. Olvera), with the Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime version (K-SAD-Pl) . The intA parent or legal guardian provided signed informed consent, and signed assent was obtained from each adolescent. Subjects were required to be between 13 and 17 years old, with no serious medical problems, and to meet the standard safety criteria for an MRI scan. Exclusion criteria for all subjects included a lifetime diagnosis of pervasive developmental disorder, mental retardation (IQ < 70), pregnancy or head injury with loss of consciousness. The institutional review board approved this study with a prisoner advocate included in the formal review. For ethical and safety reasons (subjects were transported to the University Research Imaging Center for the MRI portion) we did not withhold treatment from any subject and ensured that our subjects were clinically stable (YMRS < 12 and CDRS < 40) at the time of IQ testing and MRI.All MRI images were collected on a 3T Siemens Scanner , with a T1-weighted gradient echo sequence , repetition time (TR) of 20 ms, echo time (TE) of 5.15 ms, slice thickness of 1.0 mm, number of excitations (NEX) of 2, field of view (FOV) of 256 × , Base Resolution 256 mm × 100%, and pixel size 1 × 1 × 1 mm.The analysis was conducted using a voxel-based morphometry approach (VBM) ,41,42,43vs. CD).For IQ, socio-economic status (SES) and continuous clinical variables, we used an ANOVA with Bonferroni adjustment to compare our three groups. By definition the HC did not have any psychiatric diagnosis nor receive medication, therefore, we limited our Chi square analyses of these categories to our two clinical groups  at 0.05 were produced by computing, at each random realization of the model, the maximum test statistic observed across the image. In addition to these models for inference, we used a new technique called Threshold-Free Cluster Enhancement (TFCE) to increase sensitivity to regions of signal that are spatially extended [p-value can be computed by randomization, using standard permutation testing while maintaining strong family-wise error control. The areas of significant differences had their center of mass computed in MNI coordinates, then were converted to Talairach coordinates, and finally were named using the Talairach Client .The imaging statistical analysis was performed using a general linear model with the design matrix containing the three groups as predictors, with age and sex as covariates. Voxel inferences were done using permutation methods ,49. As pextended . In TFCEt = 5.92, df46, p < 0.001). On the Children’s Global Assessment Scales the control group had significantly higher current C-GAS compared to the CD-BD and CD , but the CD-BD and CD groups did not differ from each other. Comparing just the two clinical groups revealed the most severe past C-GAS was significantly lower for the CD-BD group compared to the CD only group . Of note the CD-BD and CD groups were similar on self-reported measures of depression and substance abuse but the CD-BD group was more likely to have a lifetime history of ADHD , Major Depressive episodes  and a trend for Oppositional Defiant Disorder  compared to the CD group. The CD-BD group was also more likely to be on mood stabilizers and atypical antipsychotic medications   At the voxel level, subjects with CD-BD had lower volume in the right medial frontal gyrus, Brodmann area (BA) 10 compared to controls (controls . Using Tcontrols .vs. Controls:Conduct Disorder (CD)  Significant volumetric differences were not found after correction for multiple testing, either at the voxel level or using TFCE between subjects with CD and controls.vs. CD:CD-BD  Significant volumetric differences were not found after correction for multiple testing, either at the voxel level or using TFCE between subjects with CD-BD and CD.Our study is the first imaging study to compare CD adolescents with and without BD. Although the CD-BD subjects had a higher score on the YMRS compared to the CD group they were still in the clinically stable range, and these groups did not differ on self-reported measures of depression and substance abuse. However on the C-GAS the CD-BD and the CD groups were still significantly lower than healthy control with scores suggesting continued moderate difficulty. Furthermore the CD-BD subjects had significantly lower C-GAS scores at “the most severe past episode” compared to subjects with just CD. The pattern of lifetime comorbid conditions  are also consistent with prior studies of the BD subjects ,26, and For the CD-BD subjects we found significant differences compared to healthy adolescent controls; specifically, CD-BD subjects had right hemispheric decreased volume in the anterior frontal lobe including BA 9, BA 10, BA 47 and BA 46 as well as the superior temporal gyrus (BA 39). Our volumetric findings in areas BA 9 and BA 10 of our CD-BD subjects include the medial and inferior frontal gyrus and portions of the dorsolateral prefrontal cortex (DLPFC). These regions are involved in higher integrative cognitive functions , as wellIn the temporo-parietal region we found lateralized differences relative to healthy controls where the CD-BD subjects had decreased volume in the right superior temporal gyrus (BA 39). These areas have been associated with cognitive operations such as episodic memory and visual spatial and facial perception and tend to show a lateralized pattern: With verbal stimuli being left-lateralized whereas facial and spatial encoding are right lateralized . The rigi.e., criminal behavior or manic episode are present. However given the overlap with ODD and ADHD, even relying on dimensional measures, would have been a challenge. In most BD children “mood instability”  or other possible manic symptoms (increased activity and poor concentration) are first attributed to the more common conditions of childhood namely ADHD and ODD. Future work with longitudinal samples prior to the age of illness will be necessary to better understand the contribution of individual symptoms and or disorders. Given the complexity of behavior and symptom overlap a larger sample and the use of multiple biological measures may be also prove productive [Important limitations of our study include the cross sectional nature, lack of a BD-only group, and the clinical presentation of our sample. However incarcerated youth represent a particularly challenging group ,21 thereoductive ,80.i.e., for assaultive behavior). We therefore felt it was unethical to interrupt their clinical care in any way. Without any unmedicated CD-BD subjects, we could not examine the effects of medication, however our rates of medication exposure are similar to other volumetric studies [et al. [In addition to high rates of comorbidity, our CD-BD sample had extensive medication exposure. Given the level of impairment associated with these disorders, medication management is usually required. Our study was conducted in a correctional treatment setting where symptomatic patients run the risk of extending their length of stay, or accruing new charges ( studies ,82,83,84 [et al.  found re [et al. . In many [et al. . A recen [et al. , which sThe use of VBM entails certain strengths and limitations. VBM is a procedure that involves spatially normalizing high-resolution neuroanatomic images into a common stereotactic space. A voxel-wise comparison of the local concentration probability of gray or white matter is conducted and based on these measures, group differences are determined ,87. The In summary, we found distinct patterns of cortical alterations in CD-BD subjects compared to healthy controls. These CD-BD subjects had decreased volumes in brain areas associated with impulse control and mood. Furthermore CD-BD subjects had poorer lifetime functioning, more mood and attentional dysfunction, and more medication exposure than subjects with CD who were not BD. As all of our CD-BD patients met the threshold of having full DSM-IV criteria for BD type I, our findings suggests the neurological underpinnings of bipolar disorder are consistent with other BD samples despite differences in patient settings and comorbid conditions.


Objective. This study aimed to evaluate effects of type 1 diabetes mellitus and mini-implant placement method on the primary stability of mini-implants by comparing mechanical stability and microstructural/histological differences. Methods. After 4 weeks of diabetic induction, 48 mini-implants (24 self-tapping and 24 self-drilling implants) were placed on the tibia of 6 diabetic and 6 normal rabbits. After 4 weeks, the rabbits were sacrificed. Insertion torque, removal torque, insertion energy, and removal energy were measured with a surgical engine on 8 rabbits. Remaining 4 rabbits were analyzed by microcomputed tomography (micro-CT) and bone histomorphometry. Results. Total insertion energy was higher in self-drilling groups than self-tapping groups in both control and diabetic groups. Diabetic groups had more trabecular separation in bone marrow than the control groups in both SD and ST groups. Micro-CT analysis showed deterioration of bone quality in tibia especially in bone marrow of diabetic rabbits. However, there was no statistically significant correlation between self-drilling and self-tapping group for the remaining measurements in both control and diabetic groups. Conclusions. Type 1 diabetes mellitus and placement method of mini-implant did not affect primary stability of mini-implants. Primary stability is the most important factor for the survival of mini-implants and is obtained by mechanical interlock between bone and mini-implant –3. Most Placement of the SD mini-implant is simple and takes less time and thermal damage can be avoided. Moreover, there is no risk of the drill fracture. This system also enhances primary stability by compressing bones during implantation and contact surface of bone to implant is wider . TherefoMajority of orthodontic patients are healthy young individuals. But there has been dramatic increase in the number of adults seeking orthodontic treatment in the last 20 years. And more diabetic patients are getting orthodontic treatment. According to the U.S. Department of Health and Human Services, 15,600 young people (younger than 20 years of age) were newly diagnosed with type 1 diabetes annually in the United States during 2002–2005. Therefore, clinicians are required to understand effects of diabetes in orthodontic treatment, especially when placing mini-implants.Although it is still controversial, diabetes is known to disrupt blood supply by inducing microvascular complications and delay wound healing and increase susceptibility to infection and periodontal disease , 12. It The SD system, which can enhance bone-implant contact by compressing bone, is advantageous to obtain good primary stability. However, it has not been studied how the SD mini-implant affects stability in diabetic patients. The purpose of this study was to evaluate effects of type 1 diabetes mellitus (DM) and mini-implant type (self-drilling and self-tapping) on primary stability of mini-implants. Comparative analysis between intentionally induced diabetic rabbits and control group was performed 4 weeks after placement in mechanical stability and microstructural/histological differences.Six healthy controls and six diabetic New Zealand white rabbits were used in this study. Mini-implants were placed 4 weeks after diabetic induction. Those mini-implants were removed in 4 control rabbits and 4 diabetic rabbit following 4 weeks of healing. Micro-CT and histological analysis were performed in four rabbits. The experiment protocol was approved by the Institutional Animal Care and Use Committee. (CUMC-2010-0093-01).For the alloxan injection, rabbits were lightly anesthetized with ketamine hydrochloride 30 mg/kg and xylazine 3 mg/kg (IM). Alloxan monohydrate  was dissolved in sterile normal saline to achieve concentration of 5% (W/V), and 100 mg/kg was immediately administered intravenously via marginal ear vein for 2 minutes. 4, 8, and 12 hours after alloxan injection, 10 mL of glucose (5% W/V) was administered subcutaneously . Four daOrthodontic mini-implants  used in this study were modified cylindrical type  with cutting flutes at the apex . Two impμm) were obtained using SkyScan-1172 high-resolution micro-CT  after fixation of specimens by 10% neutral formalin for 2 days. 3D images were reconstructed with software . Tissue volume (TV), bone volume (BV), bone-volume fraction (BV/TV), trabecular thickness, trabecular number, and trabecular separation in 1 mm area around implants were analyzed with CTAn  software. Cortical bone and bone marrow was separately analyzed . Maximum removal torque and total removal energy were similar regardless of placement method and diabetics . Bone volume/tissue volume and trabecular thickness were smaller in a diabetic group. In both diabetic and control groups, the SD system showed higher bone volume/tissue volume and trabecular thickness. But there was no statistical significance. The number of trabecular was smaller in diabetic group than in control group. In diabetic group, trabecular was more abundant in SD system and in control group. No significant difference was found between SD and ST mini-implants  was also smaller in diabetic group .Diabetes is related to bone disease such as osteoporosis and bone fracture. Bone formation and osteoid volume are reduced early in the course of this disorder , 20. TheStability is determined by the shape of mini-implant as well as bone quality, primary stability, and surgical protocol –21. WhenIn micro-CT image analysis, cortical bone did not show significant difference between SD and ST groups and diabetic and control groups. In bone marrow, however, amount and thickness of trabecular bone was decreased significantly in this study. This coincides with the previous study that reported advanced trabecular bone resorption in a diabetic state . SimilarBone to implant contact (BIC%) and bone density (BV/TV%) were smaller in diabetic state than the controls. This resulted from reduced direct bone contact due to decreased total amount of bone in diabetic rabbits. When using the self-drilling system, however, it showed more bone contact between mini-implant and surrounding bone. This concurs with the previous studies by Heidemann et al. , 7.P < 0.001). This can be considered that more energy was absorbed into surrounding bone when using SD system. There was no significant difference between diabetic and control groups in maximum removal torque and total removal energy, regardless of placement system. Small sample size due to difficulties of maintaining diabetic state and variability of measurements make it difficult to confirm the statistical significance. Therefore, further studies with more animals and longer duration of diabetic condition are required. Moreover, further study needs to be conducted in regard to potential effect of loading on these mini-implants, since they will be loaded ultimately.Maximum insertion torque was less in diabetic group. It can be assumed that the mechanical property of cortical bone was weakened due to diabetes, however, no statistical difference was found. In this study, the results came from four weeks after placement. If this period increases, the effect of diabetes on bone will increase as well. The self-drilling and self-tapping systems showed a similar result in maximum insertion torque in both controls and diabetic group. This result did not match with the expectation that the self-drilling system, which has no predrilling procedure, will have higher maximum insertion torque than the self-tapping system. Total insertion energy is the energy absorbed by bone from the beginning to the maximum torque value of mini-implant insertion. It can be calculated by measuring torque value continuously during insertion . In bothIn this animal study, higher bone to implant contact ratio and bone density were observed through the self-drilling method than self-tapping method in controls and diabetic rabbits. This result had no statistical significance, but it can be assumed that bone was damaged or deformed due to absorbed energy. In this study, diabetes was induced through medication with animals; thus it might differ from the actual responses in human body. However, it can be concluded that the presence of diabetes and placement system do not affect the initial stability of orthodontic mini-implants.


We examined temporal variation in AFB1 albumin adducts (AF-ALB) in HIV positive Ghanaians, and assessed the association with socioeconomic and food consumption factors. We collected socioeconomic and food consumption data for 307 HIV positive antiretroviral naive adults and examined AF-ALB levels at recruitment (baseline) and at six (follow-up 1) and 12 (follow-up 2) months post-recruitment, by age, gender, socioeconomic status (SES) and food consumption patterns. Generalized linear models were used to examine the influence of socioeconomic and food consumption factors on changes in AF-ALB levels over the study period, adjusting for other covariates. AF-ALB levels  were lower at baseline , higher at six months , and lower at 12 months . Participants with the lowest SES had the highest AF-ALB levels at baseline and follow up-2 compared with those with higher SES. Participants who bought less than 20% of their food and who stored maize for less than two months had lower AF-ALB levels. In the adjusted models, there was a statistically significant association between follow up time and season (dry or rainy season) on AF-ALB levels over time (p = 0.04). Asymptomatic HIV-positive Ghanaians had high plasma AF-ALB levels that varied according to season, socioeconomic status, and food consumption patterns. Steps need to be taken to ensure the safety and security of the food supply for the population, but in particular for the most vulnerable groups such as HIV positive people.The association between aflatoxin exposure and alteration in immune responses observed in humans suggest that aflatoxin could suppress the immune system and work synergistically with HIV to increase disease severity and progression to AIDS. No longitudinal study has been conducted to assess exposure to aflatoxin (AF) among HIV positive individuals Aspergillus flavus and A. parasiticus, that have been shown to impair the immune system in animal studies [The human immunodeficiency virus (HIV) suppresses the immune system leading to the development of Acquired Immune Deficiency Syndrome (AIDS). Most of the approximately 4.5 billion people living in developing countries, who are chronically exposed to aflatoxins in food , live in studies ,7,8,9,10 studies ,16,17,18 studies ,20. Chro studies , food co studies , growth  studies ,24 and a studies ,26,27,28 studies ,30.1, B2, G1 and G2) occur together in various foods in different proportions [1 is usually the predominant and most toxic form. Therefore, we measured AFB1-lysine adducts (AF-ALB) formed from the binding of AFB1 to the amino acid lysine in serum albumin. The presence of these adducts indicates long-term exposure (two to three months or longer) to aflatoxin [1-albumin adducts in the blood of people exposed to the toxin [1 adduct levels were shown to be lower during the rainy season than during the dry season [Although the major aflatoxins  and 12 (follow-up 2) months post-recruitment, and to examine association of AF-ALB levels with socio-economic and food consumption factors. No longitudinal study has been conducted to examine variation in AF-ALB levels among HIV positive people who are chronically exposed to aflatoxin in the diet, although changes in aflatoxin levels have been associated with immune suppression, increase in viral load, and HIV disease progression ,29,30. Wp = 0.0001). Most participants were currently married and Christians. Females (28%) were significantly less likely to belong to high SES groups compared with males (52%) (p = 0.001). The majority of the participants (62%) reported not growing any of their food, and 55% reported buying over 20% of their food. Almost 70% reported storing maize for short periods (0–2 months), and the majority of participants (67%) reported storing less than 25% of their maize. About 66% reported consuming maize at least two times a week, although a higher percentage of females reported consuming maize at least two times per week compared with males . About 45% of participants reported consuming groundnuts at least two times a week.est = −0.30, p = 0.02), and participants in the middle and highest SES groups also had lower aflatoxin levels compared with the lowest SES group although the differences were not statistically significant. Participants who reported purchasing over 20% of their food , those who reported storing over 25% of their maize , and those who reported storing their maize for three to five months  all had significantly higher aflatoxin levels over the follow up period. In addition, participants who reported consuming groundnut  two to three times a week had significantly higher AF-ALB levels.p = 0.04). versus rainy season during follow-up 1 , although this difference was not statistically significant after Bonferonni adjustment. There were also no differences in AF-ALB levels comparing dry and rainy seasons at baseline or follow-up 2. However, there was a statistically significant change in AF-ALB levels comparing follow-up 1 with baseline  in the rainy season, but not between follow-up 2 and baseline in the rainy season. There were no significant differences over follow-up time in the dry season for follow-up 1 or follow-up 2. After accounting for the interaction between follow-up time and season, none of the food consumption or socioeconomic variables or CD4 count (p < 0.05) restricted to the rainy seasons, but not between baseline and follow-up 2.In adjusted models, we observed a statistically significant association between follow-up time and season (dry or rainy season) on AF-ALB levels over time  in 2009 and 2010, respectively, while samples for follow-up 1 were collected during August 2009 through January 2010. AF-ALB levels usually reflect two to three months of aflatoxin exposure [Our observed values of AF-ALB indicate that study participants have high levels of this AFBexposure ,33. The exposure . Lower levels of AF-ALB at baseline and follow-up 2 (February–July) on the other hand, seem to reflect consumption of fresh crops harvested at the end of the September to November rainy season but also consumption of crops kept under less humid conditions during the drier months of December through March. Thus, these data seem to reflect seasonal changes at the end of the growing seasons that favor more complete drying of grains and storage under less humid conditions that keep fungal growth in check and thus less aflatoxin production. Our overall findings are similar to those of a longitudinal study conducted in Benin where the highest AF-ALB blood contamination in children was observed at the end of the rainy season in October . In the In our unadjusted analysis, we found that participants in the middle and highest SES groups had lower AF-ALB levels compared with those in the lowest SES group, except at follow up 1 when the levels for all SES groups were not significantly different, and were higher than at either baseline or follow-up 2. Previously we found that certain socio-demographic factors, such as ethnic group, the village in which participants lived, and the number of individuals in the household, were significant predictors of high AF-ALB levels in Ghana . In anotIt is encouraging to see that AF-ALB levels do not remain at the high follow-up 1 levels but dropped again to slightly higher than baseline at follow-up 2. This seems to indicate normal formation and turnover of aflatoxin albumin adducts in these asymptomatic HIV positive individuals reflecting fluctuation in aflatoxin consumption in staple foods. However, with HIV disease progression or development of liver problems  chronic aflatoxin exposure could contribute to faster and more severe disease progression. Chronic hepatitis B infection and ingestion of aflatoxin contaminated foods have been shown to be major risk factors for development of liver cancer ,52. Ther1-lysine adducts, reflecting aflatoxin exposure in the previous two to three months, provided direct and valid measures of aflatoxin levels in the blood. Third, the relatively homogenous dietary pattern among study participants improves the generalizability of our findings to others in the population. In conclusion, HIV-positive Ghanaian adults had high plasma aflatoxin levels that varied according to season and food consumption patterns. Future studies with more detailed assessment of food harvest, storage and consumption patterns in conjunction with climatic data may help to clarify the observations. This study has certain limitations. The study sample was a convenience sample and is prone to all the limitations of convenience sampling. For instance, recruited participants are likely to be more concerned about their health and therefore healthier than non-participants inducing potential selection bias. However, we expect that this would lead to an underestimation, and not overestimation, of observed aflatoxin levels. Our inability to evaluate individual levels of maize and groundnut consumption and aflatoxin contamination at sampling time points are limitations of the study. There are also several strengths of this analysis. First, the longitudinal design enabled us to examine changes in aflatoxin levels over time, and examine seasonal patterns that would not have been possible with a cross-sectional design. Second, the use of plasma aflatoxin B3 of blood  were recruited and followed-up at 6 and 12 months post-recruitment. Blood samples (20cc) were collected at each time point and separated into plasma for AF-ALB  and peripheral blood mononuclear cells for immune analyses (data to be published elsewhere). Baseline recruitment was conducted from February–July 2009, follow-up 1 from August 2009 through January 2010 and follow-up 2 from February–July 2010.A longitudinal study among ART-naive HIV positive adults (≥18 years) was conducted in two hospitals  in Kumasi, Ghana from February 2009 to August 2010. The Kumasi South Regional Hospital (KSRH) is located between three cities  in the Ashanti Region and provides services to 56 communities, which consist of approximately 400,000 people. Bomso Hospital (BH) is a specialized 163 bed private hospital in Kumasi that has a comprehensive HIV care, treatment and support program. BH is in close proximity to, and works closely with, KSRH. Ethical approval for the study was obtained from the Institutional Review Board at the University of Alabama at Birmingham (UAB) and the Committee on Human Research, Publications and Ethics, School of Medical Sciences, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi. After informed consent was obtained, a standardized interviewer-administered questionnaire was used to collect sociodemographic and food consumption data. The interview was conducted in private rooms at the hospitals. The medical records of patients were reviewed to obtain HIV diagnosis date and CD4+ count. Three-hundred and seven antiretroviral therapy (ART) naïve HIV positive people with CD4+ T cell counts of >300 cells/mm1-lysine adducts, reflecting aflatoxin exposure in the previous two to three months, was measured by a modified High Performance Liquid Chromatography (HPLC)-fluorescence method [1-lysine adducts was monitored at wavelengths of 405 nm (excitation) and 470 nm (emission). The peak of authentic AFB1-lysine adduct standard or samples was co-eluted with the retention time around 12.7 min. The detection limit of this method is 0.5 pg/mL. The results of AFB1-lysine adducts concentration was adjusted by serum albumin level and reported as pg/mg albumin. Plasma aflatoxin Be method . Brieflyt-tests to compare group means for continuous variables. Longitudinal data analysis using mixed models in SAS was employed to determine statistically significant predictors of AF-ALB levels over time. Adjusted analysis using mixed models was conducted to adjust for socio-demographics and clinical confounders. Socio-economic status (SES) was determined using principal components analysis (PCA) on data measuring permanent household indicators such as housing type, plumbing, water and electricity. A composite SES score was obtained from PCA analysis by weighting each indicator by the coefficient of the first principal component, and each member of the household was assigned the same SES. SES was then categorized into tertiles ranging from lowest to highest SES. The distribution of AF-ALB level appeared skewed in descriptive analysis; therefore log- transformed aflatoxin values were used for all statistical models. For all analysis, p values ≤ 0.05 were considered as statistical significant, however, we used the Bonferonni adjustment to account for over-inflation of type 1 error due to multiple pairwise comparisons in the interaction analysis. All statistical analyses were performed with SAS 9.4 .Socio-demographic characteristics, food consumption patterns, and CD4 counts of participants were assessed using chi-square tests for categorical variables, fisher’s exact test for cell counts less than 5, and


While such models provide robust tools in high-income countries, an ICU risk prediction model has not been validated in a low-income country where ICU population characteristics are different from those in high-income countries, and where laboratory-based patient data are often unavailable. We sought to validate the Mortality Probability Admission Model, version III , and Hosmer-Lemeshow chi-square statistic p = 0.154.Among 427 consecutive adults, the median age was 34 (IQR 25–47) years and mortality was 48.7%. Mechanical ventilation was initiated for 85.3%, and 41.9% received vasopressors. The MPM0-III has modest ability to predict mortality in a population of Rwandan ICU patients. The R-MPM is an alternative risk prediction model with fewer variables and better predictive power. If validated in other critically ill patients in a broad range of settings, the model has the potential to improve the reliability of comparisons used for critical care research and quality improvement initiatives in low-income countries.The MPM Intensive Care Unit (ICU) risk prediction models estimate expected hospital mortality based on patient characteristics. The models facilitate case-mix adjustment in research, across-institution benchmarking, and individual ICU quality improvement evaluations. The firsWhile the large majority of resource-intensive critical care occurs in middle- and high-income countries, critical care does occur in low-income countries, and critical illness disproportionately affects people in low-income countries.–17 Attem0-III) in patients admitted to two ICUs in Rwanda; this model was chosen because it is the only ICU risk prediction that has been validated in a large cohort and is not dependent on laboratory values. We then sought to develop a new model with better predictive ability in this population and with less data collection burden.In order to facilitate ICU research and quality improvement efforts in low-income countries, a risk prediction model that is calibrated for the population and relies on a parsimonious, easily collected set of variables is needed. In our study, we first assess the performance of the Mortality Probability Admission Model, version III  and the ICU at the University Teaching Hospital of Butare (5 ICU beds) were enrolled from August 19, 2013 to October 6, 2014. These hospitals are both public referral academic teaching hospitals, and contain all public adult ICU beds in Rwanda. For purposes of this analysis, we excluded all patients younger than 15 years old.2O, and occurrence within one week of a clinical insult.[pO2/FiO2, ≤ 315 (excluding any SpO2 >97%), based on a study that derived and validated the correlation between PaO2/FiO2 ≤ 300 and SpO2/FiO2, ≤ 315.[Sepsis was defined using criteria outlined by the most recent international consensus groups at the time of the study: at least two of four Systemic Inflammatory Response Syndrome (SIRS) criteria and suspected infection.–24 Severl insult. Since ar2, ≤ 315., 260-III variables were based on those from the initial MPM0-III development and validation study.[0-III consists of 16 variables assessed within one hour of ICU admission. As in the initial development and validation of MPM0-III, missing values were treated as normal. Discharge and death diagnoses were determined using information in the chart. Diagnoses were classified using the Agency for Healthcare Research and Quality’s (AHRQ) Clinical Classifications Software (CCS). This maps 17 broad diagnostic categories and a total of 260 specific diagnoses to ICD-10 codes.[Operational definitions for the MPMon study. MPM0-III10 codes.0-III variables (acute and chronic organ-specific conditions), laboratory values when available, variables to determine the presence of sepsis, severe sepsis, septic shock within 24 hours of ICU admission, and ARDS at any time during ICU admission, interventions performed during admission, discharge diagnoses, and in-hospital vital outcomes. Training in data collection, including detailed operational definitions for each data element, was provided to the data collectors prior to beginning data collection. Study investigators randomly selected and reviewed 3 charts each week for eight weeks  to assess for inter-rater agreement in interpretation of data elements. Throughout the study, data validation reports were produced to check data accuracy and to identify any areas of systematic error. Any inter-rater disagreement was corrected for the chart in question, and other prior charts with potential for similar disagreement were reviewed. Biweekly meetings addressed inconsistencies, and study investigators were available every day for data collector questions.One physician and one nurse at each site prospectively recorded data during each ICU admission. Data were collected on paper forms, and then entered into a web-based Research Electronic Data Capture (REDCap) electronic data capture tool. Data colThe primary outcome was in-hospital mortality. For descriptive variables, we calculated proportions for categorical variables and medians with interquartile ranges (IQRs) for continuous variables. We performed univariate analyses using Student’s t-tests and chi-square tests with significance set at p<0.05.0-III model, as well as additional variables: HIV status (positive/negative), HIV treatment status , age, insurance status , time prior to receiving care (in days), admission and ICU vital signs , reasons for ICU admission, presence or absence of sepsis, severe sepsis, or septic shock, presence or absence of ARDS, and blood laboratory values .Multivariate logistic regression models were used for in-hospital mortality prediction. We chose variables from the univariate analyses, based on their predictive power  as well as their ease of capture based on our experience, the proportion of missing values in our dataset, and their clinical significance. We tested each of the 16 independent variables in the MPM0-III. We completed all analyses using SAS software, version 9.3.The final parsimonious model became the Rwanda Mortality Probability Model (R-MPM). We used area under the receiver operating characteristic curve  to assess model discrimination (how effectively a model assigns a higher probability of death to a non-survivor than a survivor). GenerallThere were 427 patients admitted to the ICUs during the study period; we were unable to locate discharge vital status on two patients after extensive searching, so we had outcomes data on 425 patients. Patient characteristics are presented in The most common reason for ICU admission was respiratory failure or need for endotracheal intubation (72.8%) . CardiopSurgical interventions were performed for 69.3% of all patients . Mechani0-III predicted mortality with area under the ROC curve of 0.72  (0-III and R-MPM models by quartile.The MPM 0.024.)  The vari 0.024.) . Using t 0.024.) . Based oWhile the GCS is documented on most patients in our dataset and in our clinical practice , we recognize that it may not be documented in all settings, and there is frequent uncertainty about scoring of verbal domains for endotracheally intubated and sedated patients. We therefore also examined a model using altered mental status on ICU admission (present versus not present) in place of the GCS score, the simplified R-MPM. This model gave an area under the ROC curve of 0.76  . Other t0-III) 0.82 and 0.72 in a large validation cohort and single-center US study of 2596 patients, respectively.[In this observational study of consecutive critically ill patients admitted to two ICUs in the low-income country of Rwanda we found that a series of five easily obtained bedside clinical variables have reasonable predictive ability, discrimination, and calibration. Our model performance compares favorably to other more complicated and less accessible clinical prediction systems  0.85 and 0.80, and Mortality Probability Admission Model The development of a risk prediction model in a low-income country is important because: 1) predictive models are essential for interpreting research and assessing quality of care for critically ill patients;, 20, 31 0-III does not discriminate or calibrate well in our Rwandan ICU population. Even in gross comparisons, the Rwandan ICU population is markedly younger and more often has surgical diagnoses than the United States cohort on which MPM0-III was based. This is a common difference between low- and high-income settings, and is consistent with cohorts from other African ICUs.[While controversy exists about the value of risk prediction models for comparing ICUs to each other (ranking) in high-income countries, there is widespread agreement that the modeling of critical care outcomes is necessary for quality improvement efforts. Even amocan ICUs., 19 The can ICUs., is evencan ICUs.0-III respectively), but with time required to abstract the data in inverse relationship to model accuracy .[Our model is simple to use and performs well in our development population with over one year of data collection to minimize seasonal bias. The model has several limitations. First, its relative simplicity will presumably translate to lower discrimination and calibration statistics when it is validated in a separate population. The tension between data collection burden and performance is not specific to low-income countries settings. One study applied the most common risk prediction models to 11,300 patients in 35 Californian ICUs and found excellent discrimination for all models . Given thctively).0-III.[Second, our model is based on a small sample size in two ICUs in a single country, and we were only able to perform internal validation using our development sample. The current validated models used cohorts ranging from 16,784 admissions for SAPS 3, to 110,50-III. and the 0-III. Our modeThird, our model contains one variable, “suspected or confirmed infection within 24 hours of admission” that cannot be assessed at time of ICU admission. This is inconvenient in that it requires waiting 24 hours for full assessment. Its value may also be impacted by processes of care within the first 24 hours of ICU admission. While this is potentially problematic, ICU-acquired infections are generally not recognized within 24 hours, making this variable a marker of infections that began prior to ICU admission. Nonetheless, it may be worthwhile in future validations of the model to assess suspected or confirmed infection at the time of ICU admission.Fourth, our model suffers from the same challenges as other risk prediction models: lead-time bias, the impact of pre-ICU and post-ICU care on outcomes, and the need for ongoing recalibration. These arOur study is the first performance-test of an ICU risk prediction model in a low-income setting and the first development of a new context-specific model in over twenty-five years. Our model is both better fit to our population and has a lower data collection burden than models developed in high-income countries. Our small sample size requires external validation in our own ICUs, other ICUs in low-income countries and in critically ill patients outside of ICUs. We are already aware of ICUs in three African countries that could apply this model with currently collected data. Just as large cohort databases have developed in the United States and Europe, so too a network of ICUs and hospitals from low-income countries could provide the necessary cohort to validate and reassess the model over time. While this presents a formidable challenge, we believe that both low- and high-income countries need ICU predictive models in order to effectively pursue quality improvement and research. Our modeS1 Supporting Information(DOCX)Click here for additional data file.S1 Tablen = number of patients. IQR = interquartile range. HIV = human immunodeficiency virus. * Totals vary depending upon missing data for some patients. **We could not locate in-hospital vital outcomes for two patients after extensive searching, so the number of patients in the survivor and non-survivor columns add to 425.(DOCX)Click here for additional data file.S2 TableICU = intensive care unit. n = number of patients. IQR = interquartile range. GCS = Glasgow Coma Scale. CPR = Cardiopulmonary resuscitation. * Totals vary depending upon missing data for some patients. **We could not locate in-hospital vital outcomes for two patients after extensive searching, so the number of patients in the survivor and non-survivor columns add to 425.(DOCX)Click here for additional data file.S3 TableGCS = Glasgow Coma Scale. CPR = Cardiopulmonary resuscitation. *The high proportion of missing values for GCS is driven by the many patients who had received sedating medications and could therefore not have GCS assessed. Of the 155 participants with missing GCS value, 134 were missing due to receiving sedating medications and 21 were missing due to having no GCS recorded.(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.