Medicago stativa (Ms)] and five common weed species  in the North China Plain. The five weeds had a lower plant carbon content than Ms. while the opposite was true for plant nitrogen and phosphorus concentrations. The Shannon diversity of bacterial and fungal communities of the five weeds were significantly lower than in Ms. Soil pH and PO43−-P were identified as the most important factors in shaping the relative abundances of bacteria (Sphingomonadaceae) and fungi (Pleosporaceae), respectively. Importantly, the weeds greatly inhibited the growth of pathogenic fungi (Nectriaceae and Pleosporaceae). Bacterial co-occurrence networks depended on specific species, indicating that Ms. harbored co-occurrence networks that were more complex than those in the bacterial communities of other weed groups. Our study examines how soil nutrients and the soil microbial community structure of five weed species changed in an Ms. field. This analysis of the microbial ecological network enhances our understanding of the influence of weeds on the soil microbiome in agroecosystems.The competition between weeds and crops for soil nutrients is affected by soil microorganisms, which drive diverse ecological processes and are critical in maintaining the stability of agroecosystems. However, the effects of plant species identity, particularly between forage and weed, on soil microbial diversity, composition, and association are not well understood. Here, we investigate the soil physicochemical properties and bacterial/fungal communities in an agroecosystem with native alfalfa [ Medicago sativa L. (Ms)] is a high-quality legume forage crop with high productivity, palatability, and quality. The alfalfa industry has developed quickly in China since 2008 in response to extensive protein feed shortages nationwide  on the abundance and community composition of soil microbial functional groups  relative to crop species  fixation or phosphorus (P) mobilization in the soil, outperform competitors in environments with low N and P availability . In turnl groups . Jordan  species . Thus, i species . HoweverThere is a close relationship between soil microorganisms and soil physicochemical properties, which determine plant health and soil fertility, as soil microbial communities largely determine the diversification of soil properties, such as pH and carbon (C) and N nutrient levels . MicrobiTriticum aestivum L.) and summer maize (Zea mays L.) are rotated with two harvests per year, which has been common practice for several decades (The North China Plain (NCP) is an important grain-production area located in China where winter wheat , established in Autumn 2019. This area has a warm, temperate, continental, semihumid monsoon climate characterized by 686 mm annual average rainfall and a mean air temperature of 14°C. The soil type is brown earth. The application rates of N (urea) and P  fertilizer in the experimental field were 50 kg N ha2 in July 2020. First, the weeds were investigated by the serpentine sampling method (six plot), and the number of the first five dominant weeds in each plot (3 m × 3 m) were counted. Crabgrass [Digitaria sanguinalis L. (Ds)], barnyard grass [E. crusgalli L. (Ec)], copperleaf [Acalypha australis L. (Aa)], purslane [Portulaca oleracea L. (Po)], and lambsquarters  were identified with relative abundances of 33.3, 26.8, 17.8, 12, and 10.1%, respectively . Fresh soil samples were leached by 2 mol L−1 KCl solution, and the content of soil NH4+-N and NO3−-N in the filtered extracts were measured with an autoanalyzer . The soil available P (PO43−-P) was extracted by shaking 2.5 g dry soil for 30 min at 20°C in 50 ml of a 0.5 mol L−1 NaHCO3 solution  were measured using a glass electrode in a suspension with a soil-to-water ratio of 1:5. The plant C and N and soil total C and N were analyzed by dry combustion using an automated CHNS element analyzer . Plant P and soil total P were determined following HpH, 8.5; .® SPIN Kit for soil . A total of 0.30 g soil was accurately weighed from each treatment. Soil DNA integrity was then detected by 0.8% agarose gel electrophoresis. The V3-V4 regions of the bacterial 16S rRNA gene were amplified using 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-GACTACHVGGGTWTC TAAT-3'). The noncoding region of the fungal internally transcribed spacer (ITS) was amplified using the ITS1 (5'-CTTGGTCATTTAGAGGAAGTAA-3') and ITS2 (5'-GCTGCGTTCTTCATCGATGC-3') primers  following the standard protocols of the Majorbio Bio-Pharm Technology Co., Ltd. . Processing of the raw data and diversity indices, including Shannon and Chao1 richness, was conducted using QIIME ver. 1.3.0 . The rea4+-N, NO3−-N, and PO43−-P), and bacterial and fungal α-diversity indices (Shannon and Chao 1) of the six species were assessed using a one-way analysis of variance (ANOVA) in PAST (ver. 3.25). The significance threshold was p < 0.05 using Tukey’s pairwise test.Plant indices , soil indices  based on Bray–Curtis similarity matrices was performed to identify the soil microbial β-diversity among the six species at the family level. The significance was tested by an analysis of similarities (ANOSIM) using the vegan package in R statistical software (ver. 3.6.3).4+-N, NO3−-N, and PO43−-P) and the soil bacterial and fungal communities at the family level were determined by a redundancy analysis (RDA) using R statistical software (ver. 3.6.3). The significance of the effects of each variable was defined using Mantel tests (permutations = 999). The resulting significance level was tested by Mantel r statistic and p values. A Spearman correlation heat map was constructed to show the relationship between bacterial and fungal classifications and environmental variables (plants and soil). Network analysis is a powerful method for studying complex community organization, principles of community organization, and interactions among community members  field (c S1). Crabgrass (Ds) had the highest relative number of the five weeds, which occupied 33.3%, followed by barnyard grass (Ec), copperleaf (Aa), purslane (Po), and lambsquarters (Ca).Ca  affected by species, and Gaiellales was the dominant family  affected by weed species, and Piskurozymaceae was the dominant family . The relative abundance of Pesudonocardiaceae significantly increased as soil TP and PO43−-P increased (p < 0.05). The relative abundances of Sphingomonadaceae and Chitinophagaceae significantly increased as soil pH increased (p < 0.05). The relative proportions of Pleosporaceae and Nectriaceae positively correlated with SDW, RDW, and R/S ratio, and plant P, soil TP, and PO43−-P significantly positively correlated with the relative abundances of Piskurozymaceae. Additionally, pH negatively correlated with the relative abundances of Mortierellaceae and Aspergillaceae  and plant traits (shoot and root biomass and R/S ratio). The abundances of pathogenic fungi were suppressed by the weeds, including Nectriaceae and Pleosporaceae, but were enriched in the rhizosphere soils of root-affected Ms. plants. Furthermore, weeds simplified the microbial co-occurrence network and possibly contributed to the higher assembly between microbial species. An integrated understanding of community assemblage will further clarify how weed species and soil microorganisms cooperate in weed–crop competition. Our work serves as an important resource for biological control in weed management practices as well as those of Ms. cultivation.Changes exerted by five common weeds on the soil microbial community diversity, community composition, and microbial co-occurrence networks were investigated in an Ms. field in the NCP. Weeds with high stoichiometric flexibility, which have a high capacity for altering the elemental composition of their tissues/organs in response to environments with varied nutrient availability, generally have a greater potential to adapt or survive successfully. These weeds dramatically decreased bacterial and fungal diversities when compared with Ms. Shifts in the structural community compositions because the five weeds were tightly associated with changes in edaphic parameters  can be found in the article/CY, WT, and JS designed the study. JS, HG, SS, and FM participated in sample collection. CY performed the experiment and wrote the manuscript with the help of JQS, GY, YZ, and ZW. All authors contributed to the article and approved the submitted version.This study was funded by the China Agricultural Research System (No. CARS-34), the National Natural Science Foundation of China (32101434), the Natural Science Foundation of Shandong Province (ZR2020QC188), the Start Up Funds for High Level Talents of QAU (1120025), and the “First class grassland science discipline” program of Shandong Province, China.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


There is no consistent conclusion on which adiposity measure is best to predict cardiovascular risk factors in youth. The present study aims to assess the performance of body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR) in predicting abnormal left ventricular structure in Chinese hypertensive youth. A total of 1180 youth aged 6–17 years with hypertension from the China Child and Adolescent Cardiovascular Health Study were included in this study. Logistic regression model, receiver operator characteristic (ROC) curve analysis and net reclassification improvement (NRI) method were used to assess performance of BMI, WC, and WHtR in predicting left ventricular hypertrophy (LVH) and left ventricular geometry (LVG). A 1-standard deviation increment in any of three indexes in predicting LVH and LVG were similar, e.g., with the odds ratios and 95% confidence intervals of 1.34 (1.16–1.55), 1.25 (1.08–1.45) and 1.40 (1.20–1.62), respectively. In addition, ROC analysis and NRI method confirmed the similar performance of three adiposity indexes in predicting LVH and LVG. In conclusion, BMI, WC and WHtR had similar performance in predicting abnormal left ventricular structure in Chinese hypertensive youth, but all three indexes had limited value in prediction. WHtR is a simple and convenient adiposity index for screening youth at high risk of target organ damage. It is well established that childhood obesity is associated with not only short-term risk of target organ damage2, but also long-term risk of cardiovascular disease and premature mortality in adulthood3. Thus, it is important to identify youth with obesity to prevent related harmful health outcomes.The prevalence of pediatric obesity is increasing globally during recent decades4. Additionally, the use of BMI defining obesity should be based on sex- and age- specific percentile cutoffs. Waist circumference (WC) represents the accumulation of fat mass at the abdomen, which is suggested to predict cardiovascular disease and type 2 diabetes better than BMI in adult population5. However, the use of WC also should be based on sex- and age-specific cutoffs.Body mass index (BMI) is a commonly used index for assessing pediatric obesity globally. However, BMI represents obese status in the whole body and it cannot distinguish fat mass from lean mass6, which is simpler and more convenient to use than BMI and WC in pediatric clinical practice. However, although one recent meta-analysis showed that WHtR performs equally well as compared with BMI and WC in identification of children with cardiovascular risk factors7, it is unclear whether WHtR has the similar performance with BMI and WC in predicting abnormal left ventricular structure in pediatric population.Waist-to-height ratio (WHtR), which corrects height of a child, is another useful index to assess central obesity. A main advantage of WHtR is that it requires only one cutoff (0.50) to define central obesityIn the present study, we aimed to assess the performance of BMI, WC and WHtR in predicting left ventricular hypertrophy (LVH) and left ventricular geometry (LVG) in Chinese hypertensive youth.8. In brief, a stratified cluster sampling method was used to select at least 600 school-age children per age group in each cluster (300 boys and 300 girls). Children and adolescents were recruited from six provinces  covering geographical areas in North, East, Northeast, Northwest and Southwest regions of China. A total of 44,793 children and adolescents with complete data on sex, age, weight, height, WC and blood pressure (BP) were included. Then, 1979 children and adolescents with elevated BP  on three different occasions were diagnosed as having hypertension. However, data on left ventricular structure were available for 1180 children and adolescents. Exclusion criteria were: (1) the inability to give informed consent; (2) use of any drug known to affect BP; (3) having chronic diseases and (4) pregnancy.Our data were part of the China Child and Adolescent Cardiovascular Health (CCACH) Study, which is a large, nationwide study designed to examine cardiovascular risk factors among Chinese children aged 6–18 years. Detailed information on CCACH study has been described elsewhere2).In each of six centers, physical examination followed a standardized protocol by trained staff. Weight and height were measured in light clothes without shoes. Weight was measured twice to the nearest 0.1 kg, and height was measured twice to the nearest 0.1 cm. The two values of height or weight were averaged for data analysis. BMI was calculated as weight divided by the square of height , which has been clinically validated in children and adolescents3 − 3] + 0.613. Left ventricular mass index (LVMI) was calculated as LVM (in grams) divided by height (in meters)2.7 to correct for body size14. Left ventricular hypertrophy (LVH) was defined as LVMI ≥ age- and sex-specific 95th percentile values15. The relative wall thickness (RWT) was calculated as  / left ventricular end-diastolic internal dimension. Increased RWT was defined using the cutoff of ≥ 0.3616. Based on LVMI and RWT, children and adolescents were then divided into four groups: normal geometry , concentric remodeling , eccentric hypertrophy  and concentric hypertrophy (increased LVMI and increased RWT)17.Left ventricular structure was assessed using Sonographers performed Doppler ultrasonography device  with a 2.5–4.0 MHz probe. Left ventricular end-diastolic internal dimension, interventricular septal thickness and posterior wall thicknesses were measured. Left ventricular mass (LVM) was calculated using the Devereux`s formula LVM (g) = 0.8 × [1.04 × 19. We performed subgroup analyses by sex and age group. P < 0.05 indicates statistical significance.All data analyses were performed using SAS 9.3 . BMI, WC and WHtR have different units, and it is incomparable between three adiposity indexes using the original units. Thus, we calculated sex- and age- specific z-scores based on the study sample. We calculated odds ratios (ORs) and 95% confidence interval (CIs) of LVH and LVG for BMI-z score, WC-z score and WHtR-z score, respectively, using binary and multinomial logistic regression models with adjustment for systolic and diastolic BP. We also assessed the performance of three adiposity indices in predicting LVH and LVG, respectively, using receiver operator characteristic (ROC) curve analysis and the area under the curve was calculated. Differences in area under the curves (AUCs) between any two indices were compared using Z test. Net reclassification improvement (NRI) was calculated to determine the extent to which adiposity indexes  improves the predictive ability for LVH or LVGNo patients or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.All the subjects’ parents provided signed informed consent.The study was approved by the Research Ethics Committee of the Capital Institute of Paediatrics of China . We confirm that all methods were carried out in accordance with relevant guidelines and regulations.p < 0.0001). However, there was no difference in LVMI, RWT, and prevalence of LVH and LVG between boys and girls . The prevalence of LVH increased significantly across three categories  of each of three adiposity indices , and then we performed subgroup analyses. The associations were more pronounced in boys than in girls for three adiposity indexes (Table The increased risk of LVH or LVG for 1-standard deviation (SD) increment of three adiposity indices were similar Table . For exaes Table . For LVHes Table .Table 2Op > 0.05). In addition, the NRI was also not significant for WC-z score or WHtR-z score, as compared with BMI-z score (p > 0.05). The subgroup analyses by sex and age are shown in Table Performance of BMI-z score, WC-z score and WHtR-z score to predict LVH or LVG was also similar using ROC analysis or NRI method Table . For exaTo our knowledge, this is the first study investigating the associations of three different adiposity indexes  with left ventricular remodeling in Chinese hypertensive youth. Our findings suggest that the performance of three adiposity indexes is similar in predicting presence of LVH or LVG. It seems that WHtR, as a simple and convenient adiposity indexes, can replace with BMI and WC that require sex- and age- specific percentile cutoffs.7. To our knowledge, no studies have assessed the performance of WHtR as compared to BMI for predicting abnormal left ventricular structure in hypertensive youth. A study of 156 patients aged 2–20 years  found that WHtR was positively associated with both LVM and LVMI20. Analysis of 1723 visits of 593 children (mean age: 12.2 years) with chronic kidney disease showed that WHtR performed better than BMI for predicting not only cardiovascular risk profile  but also higher LVMI21. In all, all three studies above showed that WHtR has similar or better performance than BMI in predicting higher LVMI. Since WHtR only requires static cutoffs (without consideration of a child’s age and sex) to define central obesity, WHtR is convenient to use in clinical practice, as well as in other settings such as in schools for school nurses and at home for parents, respectively, to monitor central obesity status of a child.The findings on which adiposity index performs best in predicting cardiovascular risk factors in children and adolescents have been inconsistent. A recent meta-analysis including 34 studies based on general pediatric population indicated that WHtR performed equally well compared with BMI and WC in predicting cardiovascular risk factors including hyperglycaemia, elevated blood pressure, dyslipidemia and metabolic syndromeWe found that the overall ROC values of three adiposity indices were not high enough (0.55–0.60), suggesting pediatric obesity alone has limited predictive ability for abnormal left ventricular structure. In addition, other predictors such as hypertension, dyslipidemia, and hyperglycemia may play additional important roles in predicting left ventricular remodeling. We also found that the ROC values for predicting LVH and eccentric hypertrophy were much lower in children aged 6–12 years  than in adolescents aged 13–17 years , suggesting that adiposity indices are not useful to predict LVH and eccentric hypertrophy in children but may be useful in adolescents. In contrast, for predicting concentric remodeling, the ROC values were much higher in children aged 6–12 years (0.68–0.72) than in adolescents aged 13–17 years (0.50–0.52). These results above suggest that adiposity indices have different predictive ability to determine different LV left ventricular remodeling patterns in different age groups of youths.24. A cross-sectional study including 16,914 Chinese children and adolescents aged 7–17 years demonstrated that the optimal cutoffs of WHtR should be 0.47 for boys and 0.45 for girls23. Another cross-sectional study including 3057 Korean adolescents aged 10–19 years showed that the optimal cutoffs of WHtR should be 0.44 for boys and 0.43 for girls24.As for the optimal cutoff of WHtR for screening cardiovascular risk factors in Asian children and adolescents, the findings have also been inconsistent. But most studies support that the cutoff of WHtR should be lower than 0.50This is the first study to examine the associations of BMI, WC and WHtR with left ventricular remodeling in Chinese youth with hypertension. Our study has four limitations. First, the cross-sectional design of this study impeded us to make casual inference. Second, we assessed the performance of three adiposity indexes in hypertensive youth, and the generalizability of our findings to general healthy youth should be made with caution. Third, although body fat composition might be superior to three adiposity indices studied in current study, information on body fat composition is unavailable. Fourth, more novel and sensitivity cardiac markers such as LV diastolic parameters e', contractility s' or LV filling pressure E/e' are widely used in clinical studies. However, these data are unavailable currently. In addition, left atrium (LA)/ left ventricle (LV) index is useful for assessing sub-clinical LA/LV dysfunction. However, LA was not measured in the dataset.Our study shows that three adiposity indexes  have similar performance in predicting abnormal left ventricular structure in Chinese hypertensive youth. However, it should be noted that each of three adiposity indexes has limited prediction value. The more simplicity and convenience of WHtR as compared with BMI may be useful in screening youth at high risk of target organ damage.Supplementary Information.


Purpose: The aim was to investigate the effect and underlying mechanism of anti-vascular endothelial growth factor (anti-VEGF) in diabetic macular edema (DME) by optical coherence tomography angiography (OCTA).Methods: Twenty-five eyes in 18 treatment-naïve patients with DME were included. All eyes were imaged by OCTA at baseline and 1 week after monthly intravitreal aflibercept injection (IAI). Visual acuity was measured as best corrected visual acuity (BCVA). Additional parameters were evaluated by OCTA, including central macular thickness (CMT), the number of hyperreflective foci (HRF), foveal avascular zone (FAZ), vessel density (VD) in the deep capillary plexus (DCP), the en-face area of cystoid edema in DCP segmentation, and subretinal fluid (SRF) height.Results: The mean time between baseline and final follow-up by OCTA was 79.24 ± 38.15  days. Compared with baseline, BCVA was increased significantly after the 3rd IAI, while CMT was decreased significantly from the 1st IAI. SRF height and the area of cystoid edema in DCP segmentation were decreased significantly after the 2nd IAI compared with baseline. The number of HRF was decreased significantly after the 1st IAI (8.87 ± 9.38) compared with baseline (11.22 ± 10.63). However, FAZ’s area and perimeter as well as VD in DCP showed no significant changes post-treatment.Conclusion: Anti-VEGF is effective in treating DME, improving visual acuity and decreasing macular edema. The decreased HRF indicates anti-inflammatory effects of aflibercept to deactivate retinal microglia/macrophages. The decreased cystoid edema and SRF height indicated improved drainage function of Müller glial cells and retinal pigment epithelium after IAI. Diabetic macular edema is a major cause of visual loss in patients with diabetic retinopathy, seriously affecting the patient’s quality of life.Currently, anti-VEGF treatment has become the standard of care and first-line therapy for the treatment of diabetic macular edema.The inflammation and drainage dysfunction accounts for the pathogenesis of diabetic macular edema.via binding vascular endothelial growth factor (VEGF) and placental growth factor (PlGF), effectively decreased macular edema by deactivating microglia and improving the drainage function of retinal Müller glia and retinal pigment epithelium.Aflibercept, Diabetic macular edema (DME), a major cause of central visual loss in patients with diabetic retinopathy, has a prevalence of 4.8% and seriously affects the patient’s quality of life . The patIn recent years, intravitreal injections of anti-vascular endothelial growth factor (anti-VEGF) drugs have revolutionized the treatment of DME, becoming the standard of care and first-line therapy . CurrentRecently, hyperreflective foci (HRF) in the retina have been considered active inflammatory cells, especially microglia and macrophages, when examined with optical coherence tomography (OCT), indicating inflammation also plays an important role in the pathogenesis of retinal diseases. HRF were first described by via the corresponding ion and water channels, thus maintaining the retina in a relatively dehydrated, transparent state. The dysfunction of RMG and RPE results in fluid retention in the retina, i.e., intraretinal cystoid edema due to RMG drainage dysfunction, and subretinal fluid (SRF) accumulation due to RPE dysfunction  treatment indicated that aflibercept might deactivate microglia/macrophages. The decreased cystoid edema indicated aflibercept improves the drainage function of RMG; the reduced SRF suggested that aflibercept improves the drainage function of RPE and maintains the barrier integrity of the BRB. These data jointly indicated that besides maintaining BRB to reduce DME, aflibercept treatment could also decrease retinal inflammation and improve drainage function in both RMG and RPE Twenty-five treatment-naïve eyes in 18 patients with DME were diagnosed by comprehensive ophthalmologic examinations in the Department of Ophthalmology, Shanghai General Hospital affiliated to Shanghai Jiao Tong University, Shanghai, China, between July 2020 and December 2020. This study was approved by the Clinical Research Ethical Committee of Shanghai General Hospital affiliated to Shanghai Jiao Tong University (Permit No. 2020KY205) and adhered to the principles of the Declaration of Helsinki. All individual participants provided written informed consent.All patients underwent routine ophthalmological examinations before and after treatment, including best corrected visual acuity (BCVA), intraocular pressure (IOP), slit-lamp biomicroscope, and fundus examinations. Visual acuity was presented as the logarithm of the minimum angle of resolution (logMAR). Re-evaluation was performed 1 week after each IAI.Inclusion criteria were: >18 years of age; central macular thickness (CMT) >300 μm; intravitreal injections administered monthly until OCTA showing no significant macular edema; and OCTA performed at baseline and 1 week after each IAI, with complete and high-quality images.Exclusion criteria were: other retinal diseases, including severe proliferative diabetic retinopathy, retinal vein occlusion, neovascular age-related macular edema, and high myopia; recent history of intraocular surgery for cataract, glaucoma or eye trauma, vitreous hemorrhage, or tractional or rhegmatogenous retinal detachment.An experienced ophthalmologist performed all intravitreal injections, at the temporal limbus through the eyeball’s pars plana under aseptic conditions. All patients received monthly intravitreal injections of aflibercept (2 mg/0.05 ml) using a 30-gauge needle. A 1-week variation was allowed for every injection interval.The retina was imaged on the RTVue XR Avanti OCT system , and the parameters of interest were acquired with the AngioVue software. The scan covered an area of 6 mm × 6 mm centered on the fovea. Enface images of the superficial capillary plexus (SCP), deep capillary plexus (DCP), FAZ, vessel density (VD), etc., were automatically recorded and analyzed by the OCTA auto-segmentation software. The analysis was performed as previously described by our team .The following parameters were evaluated: CMT, the number of HRF, en-face area of cystoid edema in DCP segmentation, the height of SRF, FAZ, and VD in DCP . CMT, FAt test was performed for between-group comparisons. Correlations were assessed by Pearson correlation analysis. Value of p < 0.05 was considered statistically significant.The SPSS 23.0 software was applied for statistical analysis. Data are mean ± SD. Independent samples Eighteen (18) treatment-naïve patients with DME, including eight females (44%) and 10 males (56%), were enrolled in this retrospective cohort study, in which 25 eyes were analyzed, i.e., 10 eyes from females (40%) and 15 eyes from males (60%). The patients were 63.2 ± 12.55 years old . One eye in 11 patients and two eyes in seven patients were enrolled. DME was classified as cystoid macular edema (17 eyes) and subretinal fluid (eight eyes). Totally 25 (1st IAI), 19 (2nd IAI), seven (3rd IAI), and four (4th IAI) eyes completed the follow-ups, respectively, 1 week after each intravitreal injection .p < 0.05) and 4th  injections.p < 0.05) after the 1st (328.48 ± 91.00 μm), 2nd (321.41 ± 98.08 μm), 3rd (289.43 ± 66.03 μm), and 4th (264.00 ± 83.92 μm) injections. Statistical analysis between 1st IAI and subsequent IAIs was also performed. The results showed that, compared with 1st IAI (328.48 ± 91.00 μm), CMT was decreased after the 2nd , 3rd , and 4th  injections. Although there was a trend for CMT decrease after the following IAIs, no statistical difference for CMT change was found between 1st IAI and subsequent IAIs, indicating no additive effect of IAI on CMT improvement possibly due to the small sample size and variations for CMT change in each patient.n = 8), the heights of SRF were decreased gradually after IAI, i.e., 104.43 ± 51.17 μm , 11.98 ± 29.36 μm , 28.53 ± 49.42 μm , and 45.00 ± 63.64 μm , respectively. Surprisingly, the heights of SRF were increased after the 3rd and 4th injections compared with the 2nd injection, possibly due to the small sample size (n = 2–3) and the recurrence of SRF in these patients.SRF reflects RPE dysfunction, including outer BRB breakdown and decreased drainage function. To examine whether aflibercept could enhance the clearance of SRF, the heights of SRF were measured before and after IAI. As shown in + channel 4.1 (Kir4.1) and aquaporin 4 (AQP4), affected by diabetes , 87927.18 ± 10398.46 pixels , 91020.40 ± 13764.53 pixels , and 79397.17 ± 11293.08 pixels , the number of HRF was decreased after anti-VEGF treatments to 8.87 ± 9.38 , 6.53 ± 8.59 , 6.57 ± 5.86 , and 5.00 ± 3.56  and decreased macular edema. Currently, anti-VEGF treatment has become the standard of care and the first-line therapy in DME , and theIn this study, several inflammatory and microvascular parameters were evaluated by OCTA and compared between baseline and after IAI in patients with DME. The preliminary results showed that aflibercept treatment significantly improved visual acuity, decreased central macular thickness and cystoid edema, and reduced the height of subretinal fluid as well as the number of hyperreflective foci; meanwhile, no obvious effects on the area and perimeter of FAZ, and vessel density were detected. The decreased CMT and improved BCVA by IAI demonstrated that aflibercept effectively attenuates the leakage of retinal blood vessels, i.e., BRB breakdown, through binding of VEGF-A and PlGF.in vivo and in vitro in further study.Cystoid edema often reflects the drainage defect of RMG due to the misdistribution and reduction of ion and water channels on RMG caused by diabetes, while SRF accumulation usually indicates the dysfunction of RPE, including both outer BRB breakdown and the drainage defect of RPE due to channels and pump dysfunction. However, the present study was an observational study that the decreased cystoid edema and reduced SRF by IAI were indicative of improved drainage function of both RMG and RPE after aflibercept treatment. The functional measurements for the drainage function in RMG and RPE are suggested to be explored both Although the formation of cystoid edema remains debatable, the role of RMG in the formation of cystoid edema was evidenced by OCTA . RMG, asvia upregulating the expressions of Kir4.1 and AQP4 through binding VEGF-A. It could also increase the protein expression of sodium-potassium-ATPase (Na+-K+-ATPase), thus decreasing the intracellular sodium level to reduce the osmotic pressure of Müller cells, consequently preventing intracellular edema. In addition, other ion channels and aquaporins might also be involved in ionic homeostasis and water regulation of RMG and RPE. Thus, it merits further study to explore the detailed mechanisms of anti-VEGF reagents on the drainage function of both RMG and RPE.The RPE, besides being a component of the outer BRB, plays a major role in the fluid homeostasis of the outer retina, pumping the SRF to the choroids by active transport. As a highly polarized epithelium, any retinal stress, e.g., diabetes, could alter not only the tight junctions, leading to outer BRB breakdown, but also the proper distribution of membrane transporters of RPE cells, causing drainage dysfunction, which jointly result in SRF accumulation. The reduced tight junctions  and alteHRF in the retina detected by OCT or OCTA have been reported in many retinal diseases . AlthougIn this study, retinal vascular parameters, including the FAZ, FAZ perimeter, and VD, remained unchanged after intravitreal anti-VEGF injections, suggesting that anti-VEGF treatment is safe with no significant effect on existing retinal blood vessels, consistent with previous studies. However, the long-term effects of anti-VEGF on retinal blood vessels and neuroglia deserve further investigation.The limitations of this study include a limited number of evaluated eyes, losses to follow-up, and short-term observation and follow-up. Therefore, large sample multi-center studies are needed to clarify the long-term efficacy of aflibercept in the treatment of DME. Another limitation is that OCTA has high requirements on the fixation ability of patients, and small alterations could affect the direct comparison of treatment groups. Besides, OCTA cannot detect an active leakage as observed by fundus fluorescein angiography (FFA). Thus, further studies combining OCTA with other means of examination, e.g., FFA, are required.via binding of both VEGF-A and PlGF, thus repressing the downstream events of VEGFR1/2 in retinal cells  and adhered to the principles of the Declaration of Helsinki. All individual participants provided written informed consent.SQ, CZ, HQ, HX, DL, QQ, KL, and JingtZ contributed to the study concept and design, performance of the experiments, data review and analysis, and writing and editing of the manuscript. All authors contributed to the critical revision of the manuscript and gave their final approval of the submitted version. JingfZ and GX is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.This research was supported by the National Natural Science Foundation of China , the National Major Scientific and Technological Special Project for “Significant New Drugs Development” during the Thirtieth 5-year Plan Period (2019ZX09301113), the Science and Technology Commission of Shanghai Municipality (19495800700), and the Domestic Science and Technology Cooperation Project of Shanghai Municipal Science and Technology Commission (21015800700).The authors report no proprietary or commercial interest in any product mentioned or concept discussed in this article, as well as no conflict outside the current study.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


No hospitalizations for bronchiolitis occurred during the epidemic season of 2020–2021. No significant differences in RSV infections, oxygen therapy administration and PICU admissions across seasons were outlined. In conclusion, we report a severe decrease in hospitalizations for bronchiolitis at our center throughout the entire SARS-CoV-2 outbreak rather than only during the lockdown periods. This seems to suggest a pivotal role for the systematic implementation of cost-effective non-pharmaceutical interventions (NPIs) such as compulsory face masks and hand hygiene, which were deployed for the entire pandemic, in reducing the circulation of infectious agents.SARS-CoV-2 pandemic restrictions have deeply altered the common respiratory illnesses burden. The aim of this paper was to clarify how these measures may have influenced bronchiolitis epidemiology, exploring possible explanations. We studied 342 infants hospitalized for bronchiolitis at our center from four different epidemic seasons . March–April hospitalization rate, RSV  infection, pediatric intensive care unit (PICU) admission and oxygen therapy administration data were compared among different seasons to outline any changes during the SARS-CoV-2 outbreak. In March–April, 30 (23.1%), 28 (24.6%) and 5 (5.1%) infants were hospitalized for bronchiolitis, respectively, in 2017–2018, 2018–2019 and 2019–2020, with a lower rate in March–April 2020 ( Bronchiolitis is one of the most common respiratory illnesses, affecting about 20% of the annual birth cohort and leading to 2–3% of the hospitalization rate in children younger than 12 months every year. Presentation occurs in epidemics, mainly following the prevalence of the respiratory syncytial virus (RSV), with higher rates from November to March, with slight variability through the years . ConcomiWe retrospectively reviewed the medical records of children hospitalized with a diagnosis of bronchiolitis in the PED of the S. Orsola University Hospital  between 1 October and 30 April of the epidemic seasons of 2017–2018, 2018–2019, 2019–2020 and 2020–2021. The Pediatric Department of S. Orsola Hospital is the largest Tertiary Pediatric referral Center in the E-R region, with approximately 24,000 admissions per year and serving a metropolitan area of more than 1 million people. Patient eligibility was defined through a hospital database search for diagnosis coded by the physicians at discharge according to the ICD (9th revision) (diagnosis codes 466.11 and 466.19). Those with bronchiolitis, with or without RSV infection, were included. Only infants aged <12 months at hospitalization were enrolled in order to limit potential overlaps with other respiratory conditions. Hospitalization criteria were defined according to the most recently updated Italian Inter-Society Consensus for bronchiolitis [Oxygen therapy (either standard or high-flow) administration data were obtained. Respiratory syncytial virus (RSV) infection was routinely assessed at presentation in those suspected for bronchiolitis through a rapid antigenic test performed on swab specimens and subsequently confirmed by real-time PCR (rtPCR) by pediatricians and nurses with specific training in pediatrics. The proportion of bronchiolitis cases hospitalized during the March–April period within the corresponding season’s total was calculated and compared through Chi-square tests between the epidemic seasons of 2017–2018, 2018–2019 and 2019–2020. RSV infections and oxygen therapy administration rates for March-April 2017–2018, 2018–2019 and 2019–2020 were assessed and compared among seasons with Chi-square tests. Post-hoc season-to-season comparisons by Chi-square tests were subsequently performed where appropriate.p-values < 0.05 were considered statistically significant. Data analysis was performed with SPSS . Graphs were produced with GraphPad . The results are expressed as percentages or the absolute number of cases as appropriate; p < 0.001) was reported when comparing the March–April hospitalization proportion of the three seasons. Post-hoc tests among the seasons showed a significant difference between March–April 2020 and the same period in 2018 (p < 0.001) and 2019 (p < 0.001). The data are presented in A total of 342 children with diagnosis of bronchiolitis were included in our observation periods, specifically, 130, 114 and 98 for 2017–2018, 2018–2019 and 2019–2020, respectively. No infants with a diagnosis of bronchiolitis were hospitalized during the epidemic season of 2020–2021. Compared with the hospitalizations from October to April, patients hospitalized only in the March–April period were 30 (23.1%), 28 (24.6%) and 5 (5.1%) for 2017–2018, 2018–2019 and 2019–2020, respectively. A significant difference (p = 0.89).The rate of infants who tested positive for RSV was 94/130 (72.3%) in 2017–2018, 75/114 (65.8%) in 2018–2019 and 72/98 (73.4%) in 2019–2020. The RSV infection rate among those hospitalized with bronchiolitis during March–April for the seasons of 2017–2018, 2018–2019 and 2019–2020 was 63.3% (19/30), 57.1% (16/28) and 60% (3/5), respectively (p = 0.17). PICU admissions were 2.3% (3/130), 0% (0/114) and 1% (1/98) for the seasons of 2017–2018, 2018–2019 and 2020–2021, respectively. Moreover, no deaths for bronchiolitis before reaching hospital care or in children directly admitted to the pediatric intensive care unit occurred in the study period.The rate of infants who underwent oxygen therapy was 56/130 (43.1%) in 2017–2018, 42/114 (36.8%) in 2018–2019 and 41/98 (41.8%) in 2019–2020. The oxygen therapy administration rate in infants hospitalized with bronchiolitis during March–April in 2017–2018, 2018–2019 and 2019–2020 was 33.3% (10/30), 57.1% (16/28) and 60% (3/5), respectively , as compared with previous seasons (2017–2018 and 2018–2019), concordant with literature data worldwide. These findings were further confirmed by those from the epidemic season of 2020–2021, where no bronchiolitis cases were reported.The drop in bronchiolitis observed in our center during March–April 2020 seems consistent with the closure of schools, kindergartens and daycares imposed by the Italian government as of 24 February to react to the SARS-CoV-2 outbreak. The literature data support the effectiveness of similar restrictions that had already been deployed on a smaller scale during other pandemics such as the H1N1 outbreak (2009), when school lockdowns in Japan led to an estimated decrease in the number of students infected at peak by over 24% . As our The reduction in bronchiolitis hospitalizations observed during March–April 2020 may still not be fully explained only by the effect of pandemic-driven restrictions on infectious diffusion, as it has to be interpreted in the context of a dramatic reduction in PED attendance. It has been well documented how the SARS-CoV-2 pandemic has enhanced the perceived risk of gathering for hospital care, leading to an important decrease in the number of people seeking medical help. This reduction, similar to that described during previous outbreaks, such as SARS in 2003 , is suppFurthermore, our data from March 2020 appear to be in line with those from other European countries. A time series analysis by Angoulvant et al. in France reported a significant decrease in airborne-disseminated infectious diseases, including bronchiolitis −63.5%), during the COVID19 outbreak 3.5%, dur. Our datIn order to better understand the significance of our results, note that the general lockdown with stay-at-home orders was maintained in Italy from 9 March 2020 until 4 May 2020 and again during autumn and winter of 2020–2021, this time with different restrictions, depending on the geographical area considered. Schools remained closed from 24 February 2020 until the end of the school year, and again between 1 March 2021 and 6 April 2021. However there has been wide variability, depending on the kind of school considered, with kindergartens and primary schools open for the most of the pandemic, while high schools implemented full distance learning or mixed distanced/in presence teaching . As a coSeveral papers have raised some concerns on the possible occurrence of a so-called RSV “late peak” targeting a population of largely RSV-naïve infants, a hypothesis which seems to be supported by first-hand data from an Australian cohort described by Foley et al. . To dateIn conclusion, we report a significant drop in bronchiolitis hospitalizations in our center during the first SARS-CoV-2 lockdown (March–April 2020) and throughout the following epidemic season in 2020–2021. We could speculate that this phenomenon is to be ascribed mainly to the lower circulation of infectious agents, first of all RSV, secondary to the adoption of simple prophylactic measures such as hand hygiene and face masks rather than to lockdowns or school closures. Further evidence is needed to confirm and better quantify this trend, as careful and systematic adherence to these basic and cost-effective NPIs could play an even greater than thought role in controlling bronchiolitis epidemics in the years to come.


Mycobacterium tuberculosis (TB), is notoriously hard to diagnose. Here we show that integrating cerebrospinal fluid (CSF) metagenomic next-generation sequencing (mNGS) with a host gene expression-based machine learning classifier (MLC) enhances diagnostic accuracy for TB meningitis (TBM) and its mimics. 368 HIV-infected Ugandan adults with subacute meningitis were prospectively enrolled. Total RNA and DNA CSF mNGS libraries were sequenced to identify meningitis pathogens. In parallel, a CSF host transcriptomic MLC to distinguish between TBM and other infections was trained and then evaluated in a blinded fashion on an independent dataset. mNGS identifies an array of infectious TBM mimics (and co-infections), including emerging, treatable, and vaccine-preventable pathogens including Wesselsbron virus, Toxoplasma gondii, Streptococcus pneumoniae, Nocardia brasiliensis, measles virus and cytomegalovirus. By leveraging the specificity of mNGS and the sensitivity of an MLC created from CSF host transcriptomes, the combined assay has high sensitivity (88.9%) and specificity (86.7%) for the detection of TBM and its many mimics. Furthermore, we achieve comparable combined assay performance at sequencing depths more amenable to performing diagnostic mNGS in low resource settings.The epidemiology of infectious causes of meningitis in sub-Saharan Africa is not well understood, and a common cause of meningitis in this region,  Tuberculous meningitis is difficult to differentiate from meningitis caused by other pathogens. Here, the authors combine metagenomics-based pathogen detection in cerebrospinal fluid with a host gene expression-based machine learning classifier for diagnosis. Mycobacterium tuberculosis (TB) affects 10 million (8.9–11 million) people worldwide and carries devastating consequences, including ~1.2 million deaths in 2019 alone. With significant public health resources required for the COVID-19 pandemic, the global TB burden will likely only increase in the coming years2. Meningitis is the most severe complication of TB, carrying a 50–60% mortality rate in persons living with HIV3. The diagnosis of TB meningitis (TBM) is notoriously difficult yet essential, as early and appropriate treatment is critical to prevent significant morbidity and mortality4. Due to the paucibacillary nature of TB infection in the central nervous system (CNS), culture and nucleic acid detection  are insensitive diagnostic tools7 compared to the TBM Uniform Case Definition (UCD), which was created to standardize research studies8. However, the clinical, radiological, and laboratory criteria that comprise the UCD lack specificity9. Thus, there is a double-edged problem of patients with delayed or missed diagnoses of TBM due to the inadequate sensitivity of available diagnostic tests as well as patients inappropriately diagnosed and empirically treated for TBM based on nonspecific clinical, laboratory, and radiologic criteria who have other infectious (and non-infectious) causes of meningitis12.13. We and others have published case reports and case series demonstrating that CSF mNGS can diagnose TBM . These studies suggest that mNGS is specific but only moderately sensitive for detecting the small amounts of TB nucleic acid in the CSF of patients with TBM16. Here, we investigated the clinical utility of CSF mNGS in a large population of prospectively enrolled Ugandan adults living with HIV and suspected TBM. We sought to enhance test performance by leveraging the human gene expression component of the CSF mNGS data to develop a complementary machine learning classifier (MLC) that categorizes patients as having TBM or not. While host-based MLCs derived from blood and respiratory fluids are increasingly available to distinguish between infectious and non-infectious diseases (including TB)21, the picogram quantities of RNA in a typical CSF sample have thus far stymied the development of analogous host-based classifiers for patients with neuroinflammatory disease.Metagenomic next-generation sequencing (mNGS) is a validated diagnostic assay for neuroinfectious diseases that can test for a wide variety of infections by amplifying all the genetic material (host and pathogen) from a cerebrospinal fluid (CSF) samplen = 95), the median was 41 cells/mm3 [IQR 13–80 cells/mm3]. At the time of admission, 181 patients (49.1%) were documented as being on antiretroviral therapy (ART), with 41/181 (22.6%) of these patients known to have commenced ART in the prior month. Median duration of headache prior to admission was 14 days [IQR 7–21 days]. Median Glasgow Coma Scale (GCS) at presentation was 14 [IQR 14–15], 185/362 (51.1% of those with known GCS) had GCS < 15. Mortality at discharge or last contact was 25.9% (94/362)  are listed in Table Sixty-three patients (17.1%) were classified as probable or definite TBM, of which 61.9% (39/63) had definite TBM and 38% (24/63) were classified as probable TBM. Definite TBM was diagnosed by Xpert MTB/RIF Ultra in 97.4% (38/39) of cases. 66.6% (12/18) of cases were TB culture positive (11/18 cases were positive for TB by both Xpert MTB/RIF Ultra and culture). 16.3% (60/368) of cases were classified as possible TBM. 11.9% (44/368) were categorized as indeterminate. Nine cases had missing data for their TBM UCD score, 6 of these cases had cryptococcal antigenemia, and 3 cases had a final hospital diagnosis listed as unknown. These patients were classified as indeterminate.n = 30) whereas in the test cohort, it was 6.3% (n = 8)  (see Methods) and were used for the training of the MLC. The learning curves generated show that further addition of samples would have diminishing improvements to performance of the classifier . The median RNA input mass was 4.37 pg (0.8–1326.4 pg). The median sequencing depth for the DNA-Seq libraries was 27,341,015 . Transcripts aligning to an average of 3212 protein-coding genes (113–5335) were detected. We did not observe a strong correlation between the total number of protein-coding genes for which transcripts were detected in each sample and RNA mass, cell count, and library preparation method   sequences (both through bioinformatic filtering and depletion of abundant sequences by hybridization (DASH))We evaluated all definite TBM cases that were mNGS positive for TB. The median number of sequences aligning to the TB genome was 24 in the DNA-seq libraries (2–552 sequences) (median 1.4 reads per million reads (rpM) sequenced, 0.1–25.2 rPM), and 3683 in the RNA-seq libraries  . No TB reads were detected in the 8 “no template” water controls. Given the very low abundance of TB reads in definite TBM CSF and the lack of any TB sequences detected in the “no template” water controls, detection of TB by mNGS was defined as 1 or more sequences, with the entire sequence aligning  to the TB genome with ≥98% nucleotide sequence identity.n = 32 and OND: n = 38) infections, identified in the training cohort, were used for the training of the MLC. Samples were sequenced across two batches (TBM: batch 1 (n = 12), batch 2 (n = 20); OND: batch 1 (n = 19), batch 2 (n = 19)) to mitigate batch effects. Sixteen thousand six hundred seventeen different iterations of the classifier were run to determine the optimal parameters: all combinations of five ML methods (with parameters) with/without feature selection, addition of the co-variate matrix variables before/after feature selection, class weights  and prior count for logCPM. We wanted to identify a small set of features to distinguish the TBM versus OND, and hence chose L1 regularization. The first round of iterations  of the classifier was bootstrapped 100 times. A second round of iterations (n = 567) to fine tune the classifier was run 1000 times. A cutoff of ≥50% was used for the diagnosis of TBM.Seventy microbiologically proven samples (TBM: Two support vector machine (SVM) classifiers, with L1 regularization (regularization parameter C = 0.1), independent of the co-variate matrix, with highest sensitivities were chosen: MLC1 and MLC2. MLC1 had a prior count = 75, and top 50 features, and MLC2 had a prior count = 75, top 40 features and class weight for TBM = 2. This class weight of 2 further increased the sensitivity of TBM detection in our training cohort.FTL (528/1000), NFKBIA (674/1000), SOD2 (999/1000), and GBP5 (1000/1000). For the OND group, three genes were used predominantly for classification: EEF1A1 (532/1000), TMSB4X (544/1000), and ACTB (717/1000). We created two additional classifiers  based on these top genes to classify TBM vs. OND: a four-gene  and seven-gene  classifier. In the main manuscript we only discuss results from MLC1. Performance of the MLC2, four-gene and seven-gene classifiers are described in the Supplementary Information (Supplementary Note MLC1 had an area under the receiver operator characteristics curve (AUC) = 0.94, sensitivity = 0.84 and specificity = 0.95 on the training cohort. Of the 10 resequenced samples, MLC1 classified 4/5 TBM and 5/5 OND correctly. A total of 15 genes were used to classify TBM and 111 genes to classify OND (bootstrap = 1000)  was 2,576,270 . The median sequencing depth for DNA-seq was 21,698,038  The median RNA mass was 7.03 pg (2.7–4750 pg).Toxoplasma gondii, one case of C. neoformans, one case of T. gondii and varicella zoster virus (VZV) co-infection, and one case of T. gondii and C. neoformans co-infection. The case with T. gondii and C. neoformans co-infection was a repeat CSF sample present in the training cohort which the authors were blinded to.There were nine cases of definite TBM and 13 probable TBM cases in the test cohort. mNGS detected TB in six of the definite TBM cases, for a sensitivity of 6/9 (66.6%). In the probable TBM group, mNGS detected one case of TB, one case of The MLC correctly called seven out of nine cases of definite TBM as TBM for a sensitivity of 77.7%. The MLC called three cases as TBM in the probable TBM group. One of these cases had TB detected by mNGS Fig. , and theM. tuberculosis were detected by RNA-seq, DNA-seq or both in 37 cases throughout the cohort , and one case of a T. gondii and C. neoformans co-infection in the training cohort.Sequences aligning to T. gondii in a case classified as probable TBM. Ten viruses were detected in the possible TBM and indeterminate groups (herpes simplex virus type 1 (HSV-1) n = 1, HSV-2 n = 1, VZV n = 3, cytomegalovirus (CMV) n = 2, human parvovirus B19 n = 1, rubella virus n = 1, Wesselsbron virus n = 1). Wesselsbron virus, a neuroinvasive flavivirus endemic to sub-Saharan Africa25, was identified along with TB (see Nineteen neuroinvasive viruses (other than HIV-1) were detected by mNGS across the entire cohort Table . As descn = 1, VZV n = 2, CMV n = 1, human parvovirus B19 n = 1, measles virus n = 1, John Cunningham (JC) virus n = 1, and Epstein-Barr virus (EBV) n = 2 were detected as co-infections in patients with CM . One case was in the possible / indeterminate group, and the other case was a co-infection in a patient with CM (Not-TBM group). mNGS also detected C. neoformans in this case (9.21 rPM).We identified nine cases of bacterial meningitis, including four cases of iae Fig. . All fouiae Fig. . One casT. gondii were identified. One case was identified as a co-infection in a patient with definite TBM, three cases were identified in patients classified as probable TBM, one case which also had C. neoformans identified on mNGS (CSF CrAg negative), and another with a co-infection with VZV (as described above). Six cases of T. gondii were identified in patients classified as possible TBM, and four were identified as co-infections in patients with CM (Table Fifteen cases of CM Table .p = 0.02). The combined mNGS and host MLC displayed 88.9% (CI:51.8–99.7%) (8/9) sensitivity, 86.7% (CI:76.8–93.4%) (65/75) specificity, with an AUC of 0.87 (p = 0.0003). Concordance was 50% (11/22) against the UCD of definite and probable TBM and 61% (11/18) when cases of non-TB pathogens detected in the probable group were excluded. The combined assay detected three cases of TB in the probable TBM group and eight additional TBM cases initially classified as possible TBM. Improvement in specificity of the combined assay from the standalone host MLC classifier was due to eight cases that were classified as TBM by the MLC but were found to have other pathogens detected on mNGS, which overruled the MLC classification. These were three cases of N. meningitidis, three cases of C. neoformans, one case of VZV, and one case of EBV. One case of definite TBM had lumbar punctures (LPs) performed on days 1 and 3 post-initiation of TBM treatment. Day 1 CSF was used as part of the MLC training cohort, and TB was detected by mNGS. Day 3 CSF was used in the test cohort to which the authors were blinded. mNGS failed to detect TB in the day 3 CSF; however, the MLC still classified this sample as TBM. We considered this a failure of TB detection by mNGS when analyzing the test cohort data.The host MLC alone classified cases in the blinded test set as TBM vs OND with a sensitivity of 77.8% (CI: 40–97.2%) (7/9) sensitive and 76% (CI: 64.8–85.1%) (57/75) specific with an AUC of 0.74  as TBM, and 59/75 OND cases as OND at all subsampled depths down to 100,000 reads. The final AUC, sensitivity, and specificity for the MLC1 were calculated once the optimal threshold was determined for the mNGS assay.n = 25) had TB detected with 56% sensitivity (14/25) at 2,000,000 reads and 32% sensitivity at 500,000 reads (8/25). RNA-seq libraries in which TB was detected for the entire cohort at the original sequencing depth (n = 9) had TB detected with 100% sensitivity (9/9) at a subsampling depth of 2,000,000 reads and 88.9% sensitivity (8/9) at 100,000 reads  of pathogens were detected. When subsampling RNA-seq libraries at a depth of 2,000,000 or 100,000 reads, 6/6 (100%) of pathogens detected at the original sequencing depths were detected (Supplementary Table n = 84) at 100,000 reads for RNA-seq and 500,000 reads for DNA-seq for through the MLC and the open source, cloud-based metagenomics pipeline (Chan Zuckerberg ID or CZID)26 pipeline to assess AUC, sensitivity and specificity of our in silico low depth sequencing model. The MLC had an AUC of 0.76 (p = 0.01) with a sensitivity of 77.8 (CI:40–97.2%) and a specificity of 78.7 (CI:67.8–87.3%). The combined MLC and mNGS assay had an AUC of 0.88 (p = 0.0002) with a sensitivity of 88.9% (CI:51.8–99.7%) and a specificity of 88% (CI:78.4–94.4%)  Fig. .At a depth of 100,000 reads for the RNA library and 500,000 reads for the DNA library, one sample would require 600,000 total paired-end reads. An Illumina iSeq generates 8,000,000 paired-end reads per flow cell. Thus, at 600,000 reads per sample, it would be possible to evaluate 13 samples/run. The current cost of the iSeq cartridge at the time of writing was $USD495, or $38 per sample. With our current library preparation protocol, the total cost of reagents would be $28.25/sample at a 0.5× reaction volume with the NEBNext® Ultra™ II kits: NEBNext Ultra II RNA library kit (NEB Cat No. E7770) $15.75/sample, NEBNext® Ultra™ II FS DNA Library Prep Kit (E7805L) $12.50/sample. SPRIselect 450 mL reagent kit (B23319) $5/sample, and QIAseq FastSelect—rRNA HMR Kit (334386) at 1:100 dilution $4.60/sample. The overall cost/sample would be $75.85, excluding the upfront costs of the Illumina iSeq and ongoing service contracts.29. Because of the high prevalence of advanced HIV-1 and TB infections in Uganda, C. neoformans and TB are the most commonly diagnosed causes of meningitis. Yet, the etiology of many other cases of meningitis and/or encephalitis is never known30. Here, we utilized unbiased CSF mNGS to obtain a more comprehensive profile of neurologic infections (and co-infections) in Ugandan adults with subacute meningitis living with HIV. We identified many treatable and vaccine-preventable infections. In addition, we describe the first use of human transcriptomic data generated by a CSF mNGS assay to combine direct detection of pathogens with a host-based MLC that identifies patients with or without TBM.Characterizing the epidemiology of infectious and non-infectious etiologies of meningitis and encephalitis is inherently challenging and only compounded by the limited diagnostic capacity in sub-Saharan Africa16. Here, mNGS demonstrated 74.4% concordance against microbiologically confirmed cases of TBM  and 54.2% concordance against the UCD of definite and probable TBM after removing probable TBM cases in whom mNGS identified an alternate infection. We detected an additional five cases of TBM missed by conventional testing in the possible TBM and indeterminate groups and a sixth case in a patient diagnosed with CM (Not-TBM group), who did not undergo conventional testing for TBM.The diagnosis of TBM remains difficult due to the paucibacillary nature of the disease. While mNGS can detect TB in CSF, mNGS still has limited sensitivity, akin to TB PCR24 but with several reports of human infections, including neuroinvasive disease31. This virus was present in a patient co-infected with TB  that distinguishes active pulmonary TB from other infections39. Additional studies using GBP5 have also distinguished TB from non-TB pneumonia with >90% sensitivity and specificity41. The three other primary genes  used in our MLC have not been described in other TB host gene expression signatures. Interestingly, SOD2-mediated acidification of phagosomes has been shown to promote survival of TB in their host and serves as a marker for oxidative stress44. Ferritin has been recognized as an important factor in host immunity against TB, and ferritin heavy chain in particular has been shown to protect against TB in murine studies45. NFKBIA is a member of the NF-kappa-B (NF-κB) inhibitor family. NF-κB signaling dynamics, triggered via tumor necrosis factor-α binding to receptors on macrophages, has been shown to play a key role for survival of TB46. Particularly, Bai et al., reported that NF-κB inhibition, decreased survival of TB in macrophages47.Despite the relatively pauci-cellular content of many CSF samples in this cohort of immunocompromised patients, we recovered a rich human gene expression dataset, detecting transcripts to an average of more than 3000 genes/sample. Leveraging these data, we built a sensitive MLC to distinguish TBM from ONDs that can mimic TBM. Five of six definite TBM cases in the test cohort were classified correctly, improving upon the direct detection of TB in 3/6 cases by mNGS. Our MLC primarily uses four genes to classify TBM: N. meningitidis were classified as TBM. Fortunately, coupling case classification by the host MLC with direct detection of pathogens by mNGS enhanced the overall specificity of the assay. In other words, direct detection of a pathogen by mNGS overruled the MLC if a pathogen other than TB was detected. Training the MLC on a larger cohort of patients with a wider range of ONDs will further improve accuracy.Overall, the MLC was optimized for high sensitivity, but there was a trade-off with specificity. The MLC performed poorly classifying OND cases that had infections which were not present in the training cohort. For instance, three of four cases of 48. In addition to an estimated per sample cost of $75, the cost-effectiveness of this assay will likely be enhanced in low resource settings lacking numerous pathogen-specific PCR assays and other diagnostic modalities commonly at the disposal of clinicians in high resource settings. Thus, mNGS, may serve as a “leapfrog” technology in this context.Implementing these technologies in low resource settings is critical to improve prospective diagnoses and the institution of appropriate therapies. The utility of our diagnostic tool is not only its ability to detect TB, but also co-infections and infectious TB mimics with enhanced sensitivity for TB enabled by the host MLC analysis whose data are generated as part of the mNGS assay. Our in silico analyses suggest that only an average of 600,000 paired-end reads are required to achieve results that we and others have traditionally achieved with millions or tens of millions of reads/sample39. In addition, host responses may vary between patients with different genetic backgrounds and based on the virulence of the infecting TB strain. Although almost all LPs were performed prior to initiation of therapy, we did not have data about prehospital antibiotic treatment, which may have impacted CSF pathogen load and host gene expression profiles. As illustrated in the case vignettes  Panel was performed on a subset (n = 52) of CSF samples, regardless of presumed diagnosis based on the clinician’s discretion. For all enrollees, ~1 mL of CSF was collected in Zymo DNA/RNA Shield collection tubes  and subsequently frozen at −70 °C within 8 h of collection and shipped in batches to the University of California San Francisco (UCSF) for mNGS on dry ice , a prospective cohort study underway in Uganda. Although some patients studied here later enrolled into other unrelated clinical trials, the analyses in this study of baseline CSF specimens are not related to the outcomes of any therapeutic intervention. Research was approved by University of Minnesota (IRB Study ID STUDY00006856), Infectious Diseases Institute at Makerere University and the Mulago Hospital Research and Ethics Committee (IRB Study ID MHREC1246), and University of California San Francisco (IRB 13–12236). All HIV-positive patients presenting with signs and symptoms concerning for meningitis  to Kiruddu Regional Referral Hospital, Kampala, Uganda from March 2018 to March 2020 were screened for study inclusion. LP was performed during days 1–3 of admission utilizing a standardized diagnostic algorithm. All patients had extensive demographic, clinical, biochemical, and microbial data collected. However, CD4 + T cell counts and HIV viral loads were not available for the majority of patients. Clinical data included presenting signs and symptoms, prior TB history and prophylaxis, response to antimicrobial and adjunctive treatments, and hospital discharge status. Baseline CSF cell count, protein, glucose, microscopy, gram stain, bacterial cultures, CrAg, and fungal culture were obtained for all participants. If the CrAg was positive, the patient was treated for CM, and no further microbiological diagnostic testing was performed. If the CSF CrAg was negative, CSF Gene Xpert MTB/RIF Ultra and TB culture were performed to evaluate for TBM85, a pro8  via the Agilent Bravo or the Integra Viaflo 96, in batches of 40–96 samples and eluted into 50 uL of sterile water. The nucleic acid was then divided with half undergoing DNAse treatment to isolate RNA and the remainder being used for DNA sequencing (DNA-Seq). Total nucleic acid was also extracted from no-template water controls. ERCC RNAs were spiked into the RNA fractions at 25 pg to later back-calculate RNA mass and use as positive internal controls23.Total nucleic acid was extracted from 90 uL of CSF using the Zymo 51. DNA libraries were prepared using the New England Biolabs’ NEBNext® Ultra™ II FS DNA Library Prep Kit (E7805L) as per the protocol using the Echo Labcyte 525 and Agilent Bravo liquid handling robots. Host ribosomal RNA depletion was performed using the Qiagen QIAseq FastSelect RNA removal kit (Qiagen Cat No. 333180) at 1:100 dilution. The RNA-Seq libraries for 205 samples from both training and test cohorts underwent ERCC depletion using DASH. DASH is a CRISPR-Cas9 technology the removes abundant sequences from mNGS libraries. CRISPR-Cas-9 guide RNAs (gRNAs) were created to target ERCC sequences. DASH treatment is performed after ligation of adapters and unique barcoding of the RNA-seq library. gRNAs targe ERCC sequences. These regions in the library are cleaved, leaving only the fragments with intact adapters on both ends to be further amplified and sequenced22. Shallow sequencing was performed on an Illumina iSeq to calculate pooling volumes. Then pooled libraries were size selected using Ampure beads and sequenced on an Illumina Novaseq 6000 using 146 base pair paired-end sequencing.RNA sequencing (RNA-Seq) libraries were prepared using the New England Biolabs’ NEBNext Ultra II RNA library preparation kit (NEB Cat No. E7770) as per the protocol. Library preparation was performed in bulk using the Echo Labcyte 525 and Agilent Bravo or Integra Viaflo 96 liquid handling robots26. Raw sequencing files are uploaded to CZID (czid.org), which performs several processing steps prior to analysis of non-host data. The first step in the pipeline is an alignment to the human genome to remove host sequences. Remaining sequences after this initial human alignment step were deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) with the primary accession codes PRJNA773920 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA773920). As a result, these data files contain non-host sequences not only from pathogens but also from environmental contaminants from skin and the laboratory, including bacteria, fungi, nonpathogenic viruses, and even vertebrates. Subsequent steps in the CZID pipeline  further reduce contaminating and uninformative sequences from the nonhuman dataset. The remaining sequences undergo an assembly-based alignment using an indexed version of the NCBI’s GenBank database to identify the source of nonhuman sequences in the datasets. Additional computational steps detailed below that are specific to different types of pathogens were used to discriminate between likely environmental contaminants and candidate pathogens.A open source, cloud-based metagenomics pipeline CZID was used for mNGS analysis14. We therefore identified the optimal reporting threshold for TB using the training cohort and used this threshold on the test cohort. Regarding common causes of bacterial meningitis, sequences aligning to Streptococcus pneumoniae, Haemophilus influenzae, and Neisseria meningitidis can be found in low abundance in CSF mNGS datasets secondary to environmental, specimen handling, and/or water contamination. Reads aligning to these pathogens were normalized using compression ratios obtained from the CZID pipeline. The relative abundance for these organisms was calculated in log10(rpM) by RNA-Seq (x-axis) versus DNA-Seq (y-axis)  two-fold greater than the mean abundance of the entire cohort were presumed pathogenic within the context of patients with advanced HIV infection. Neuroinvasive viruses, bacteria, fungi, and parasites identified by mNGS were orthogonally validated with pathogen-specific PCR, clinical microbiological tests performed in Uganda  or repeat mNGS on an independent CSF aliquot.Given its paucibacillary nature, the abundance of TB DNA in CSF, as measured by rpM, can fall below the reporting thresholds in clinical mNGS assaysis) Fig. . When thWe created a training cohort to identify the optimal reporting threshold for TB and to build the MLC classifier. The cohort included patients with definite TBM and other neurological diseases (ONDs). Patients from the OND group were selected from the probable TBM, possible TBM / indeterminate and Not-TBM groups, including cases with viral, bacterial, or fungal pathogens, along with cases with only low abundance HIV, EBV, and/or CMV. This was done to prevent biasing the training set to a particular OND and to provide a more accurate representation of the overall patient population.v2.5.3a) from the CZID analysis pipeline26 using human genome assembly build 38 v23. Transcript counts for human protein-coding genes  from the samples comprising the training set were used as an input for building the MLC. Samples were normalized using the trimmed mean of M-values using calcNormFactors and logCPM functions from the edgeR R package that were adapted in python v3.6.2153. Additionally, age, sex, GCS, and CSF white cell counts were included as a priori suspected co-variates. As the number of genes used as input  was larger than the number of samples used to train the classifier, we used feature selection  to reduce the dimensionality of the input vector and identify the smallest set of genes most predictive of TBM and OND. Finally, five different ML methods  were evaluated to determine the best performing classifier. We chose to evaluate these MLC methods because they are “interpretable”. In other words, the genes used for classification are known so it is possible to extract decision rules. Final parameters for the MLC were chosen using cross-validation (80/20 split) to avoid overfitting. The classifier with the highest sensitivity  was chosen as the final classifier.The workflow for the training of the MLC is presented in Fig. A test cohort of patients with TBM and ONDs was created to assess the sensitivity and specificity of the host gene expression MLC alone and the combined mNGS and host-based gene expression MLC assay for diagnosing patients with or without TBM. UCSF investigators were blinded to any sample or case details. The final combined assay used both the mNGS (both DNA-seq and RNA-seq) and host MLC resulting in a single test for the detection of TBM versus OND. If the MLC classified a case differently than the pathogens detected by the mNGS assay (meeting preset mNGS thresholds), then the mNGS results would overrule the MLC due to the presumption that direct detection of a pathogen was more specific than categorization of disease based on a host response signature. Patients with TBM for whom there was evidence of a CNS co-infection found on mNGS (other than HIV) were not used in the final test set. The reference standard was a microbiological composite of conventional CSF testing  and orthogonally confirmed mNGS-identified pathogens (other than TB).seqtk (v1.2-r94) at the following sequencing depths: 100,000, 150,000, 200,000, 500,000, 1,000,000, and 2,000,000 reads. Subsampled files were then aligned to the human genome using STAR to obtain transcript counts, which were then run through MLC1 classifier. Subsampled files were also uploaded to the CZID pipeline for mNGS analysis.To assess whether similar results were achievable at shallow depth sequencing to better enable mNGS diagnostics in lower resource settings, we subsampled our test dataset at various sequencing depths  and analyzed the impact on the sensitivity and specificity of the assay for pathogen identification and the accuracy of the host gene expression MLC. First, all samples were aligned to the ERCC transcripts using STAR (v2.5.3a) to filter out any remaining ERCC sequences that were not removed through DASH. Next, files were each subsampled in triplicate using p-value for the AUC was calculated in GraphPad Prism, using a two-tailed test for hypotheses and assuming that the null hypothesis value for the AUC was 0.5. Baseline clinical characteristics and demographic data were compared between cohorts via Mann-Whitney U for continuous variables and chi-square test for categorical variables.Final diagnostic accuracy of the combined assay  was based on comparing the results against a composite of clinical diagnostic testing results and orthogonally confirmed mNGS-identified pathogens. The Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting Summary


Tuberculosis (T.B) is one of the major infectious diseases in the developing countries. The diagnosis of extrapulmonary T.B (EPTB) remains problematic and emergence of resistant strains poses a significant threat. Improved diagnosis of tuberculosis is a global priority for proper control. The study aimed to assess the diagnostic accuracy of GeneXpert MTB/RIF assay for diagnosis of pulmonary TB (PTB) and EPTB and to evaluate the performance of GeneXpert system for demonstrating rifampicin resistance among the studied patients.Mycobacterium tuberculosis (MTB) by both Xpert assay and the conventional methods including Ziehl-Neelsen (ZN)/acid-fast bacillus (AFB) smear microscopy and Lowenstein-Jensen (LJ) culture. Patients′ demographic, clinical characteristics and risk factors for acquiring rifampicin resistance were analyzed.A total of 582 clinical samples  and 133 extra-pulmonary origins; 26 pleural fluid, 62 CSF, 19 ascetic fluid, 12 pus and 14 urine) were collected from patients under clinical and radiological assessment of either PTB or EPTB who were admitted to Menoufia Chest Hospital over a period of three years. Clinical samples were processed and investigated for detection of The sensitivity, specificity, false- negative rate and total accuracy of AFB smear microscopy respectively were 72.1 %, 81.3 %, 27.9 and 78.8 % for PTB. However for EPTB, they were 63.2 %, 70.5 %, 36.8 and 68.4 % respectively in relation to LJ culture as the gold standard. GeneXpert MTB/RIF revealed better performance for PTB than EPTB. For PTB, it showed 90.2 % sensitivity, 86.9 % specificity, and 9.8 % false- negative rate. For EPTB, the assay showed a sensitivity of 81.6 %, specificity of 78.9 % and false- negative rate of 18.5 %. Multivariate regression analysis showed that presence of EPTB and contacts with known TB cases were independent risk factors for developing rifampicin resistance.GeneXpert MTB/RIF assay is a rapid and highly sensitive technique for diagnosis of PTB or EPTB. Its simplicity and accuracy make this new method a very impressive tool for diagnosis of MTB and rifampicin resistance. Tuberculosis remains a global health burden especially in developing countries. In 2019, an estimated 10 million people fell ill with TB worldwide, a total of 1.4 million people died from TB (including 208 000 people with HIV). Worldwide, TB is one of the top 10 causes of death and the leading cause from a single infectious agent (above HIV/AIDS)  and 133 extrapulmonary samples  were respectively collected from patients with PTB and EPTB after clinical and radiological assessment (one sample from each participant). Samples were either from culture-proved/confirmed cases, new cases or from patients with treatment failure or relapse.The study protocol was approved by the local ethics Committee of Faculty of Medicine, Menoufia University Hospitals and all the patients and/or their guardians have signed informed consent. The overall refusal rate was 9.5 %.There were no demographical or clinical differences between recruited cases and those who refused to participate.°C until processed by standard laboratory procedures and the Xpert assay. All the collected specimens were processed as follows:Acid- Fast Bacillus (AFB) smear microscopyFour hundred-thirty of the recruited cases with suspected PTB provided three consecutive early morning sputum samples over a 2-day period. For patients who were unable to expectorate the sputum, ultrasonic nebulizer technique was used for sputum induction. Another 19 BAL samples were also collected. For patients with suspected EPTB; pleural fluid, CSF, ascetic fluid, pus and urine samples were collected in sterile containers, held at 4Culture on Lowenstein-Jensen (LJ) MediaZiehl-Neelsen staining was performed on the first non-decontaminated sputum samples as well as BAL, pleural fluid, CSF, ascetic fluid, pus and urine sediments (liquid samples were first concentrated for 15min at 3000rpm and sediments were used). Purulent sputum was liquefied with N acetyl-L-cysteine as a mucolytic agent to increase the homogeneity of the sample before smear preparation. Smears were examined to explore the presence of acid-fast bacilli and graded as per the International Union against Tuberculosis and Lung Disease scale; negative for TB, scanty, + 1, +2, and + 3. A patient was considered positive if a minimum of one smear was graded scanty or higher. Culture-positive colonies were subjected to ZN staining to establish their acid-fast status .CultureoC for 2–8 weeks. Cultures were not discarded as negative except after 8 weeks. The grown isolates were identified as MTB using the standard biochemical tests, including production of niacin, nitrate reduction and catalase [Second sputum samples were decontaminated using N-acetyl-L-cysteine and sodium hydroxide (NALC-NaOH) (Petoff’s method) . Subsequcatalase .The third unprocessed sputum and other extrapulmonary samples were collected in specialized containers and tested directly using the GeneXpert assay.rpoB gene of MTB. Five different colored beacons are used, each covering a separate nucleic acid sequence within the amplified rpoB gene. The results of the assay are: a- TB positive rifampicin resistant, b- TB positive rifampicin non resistant, c- TB not detected and d-Invalid result [It is a fully automated cartridge–based molecular system that integrates sample processing, nucleic acid amplification and recognition of the target sequences. The assay uses nucleic acid probes that identify and report the presence or absence of the normal, rifampicin-susceptible, sequence of the d result .One ml un-concentrated specimens (without centrifuge) were used for Xpert MTB/RIF assay. Sample reagent (two volumes of 0.1M NaOH and 0.1M isopropanol) was added in a 2:1 ratio to unprocessed specimen in falcon tube and the tube was manually agitated twice during a 15min incubation period at room temperature. Subsequently, 2ml of the inactivated sample was transferred to the test cartridge by a sterile disposable pipette (provided with kits).Cartridges were labeled by the specimen ID and loaded into The Xpert MTB/RIF instrument. The cartridge contains the wash buffer, reagents for DNA extraction and PCR amplification, and fluorescent probes to do the assay automatically .An interviewer-administered structured questionnaire was used to collect primary data from the recruited cases, and a checklist for data collection from the clinical records. Univariate and multivariate logistic regression analyses were performed to assess the potential risk factors for the existence of rifampicin resistance.The data were collected, tabulated, and analyzed by SPSS  version 20.0 on IBM compatible computer.Categorical data was described as number and percentage and compared using Chi square and fisher’s Exact test accordingly. Quantitative data was described as mean, standard deviation and range, Shapiro Wilk test of normality was performed to check normality of the data, and it was analyzed by using Mann Whitney U test , diagnostic accuracy of AFB smear and GeneXpert MTB/RIF assay in relation to LJ culture as a gold standard, Receiver operating characteristics (ROC) curve was drawn to explore area under the curve of GeneXpert MTB/RIF assay. Multivariate regression analysis using binary logistic regression was used for independent risk factors for rifampicin resistance.P ˂0.001)  PTB and 133 (22.9 %) EPTB cases. Age, sex and residence showed no significant difference between both TB types; rate of contact with other TB cases was 50.1 % & 47.4 % in PTB & EPRB respectively with no significant difference between them while the rate of retreated cases was significantly higher among EPTB than in PTB (19.5 % versus 5.3 % respectively: AFB smears demonstrated positivity in 33.2 % (149/449) and 39.1 % (52/133) of PTB & EPTB cases respectively. On using LJ culture, 122/449 cases (27.2 %) with PTB and 38/133 cases (28.6 %) with EPTB were positive but with no significant difference (Table GeneXpert MTB/RIF assay demonstrated positivity in 24.7 % 111/449) and 30.1 % (40/133) for PTB & EPTB cases respectively. However, the assay proved to be invalid in 9.4 % (42/449) and 8.3 % (11/133) of PTB & EPTB samples respectively with no significant difference. According to GeneXpert assay, rifampicin resistance was detected in 12.6 % (14/111) and 32.5 % (13/40) of PTB & EPTB cases respectively with a significant statistical difference (P ˂0.005)  of male gender (69.9 %). Of all the collected extrapulmonary samples, CSF accounted for 46.6 % followed by pleural fluid (19.5 %). Hibah  found thAccording to the current results, the sensitivity and specificity of AFB smear microscopy were lower than those of GeneXpert MTB/RIF assay with either PTB (72.1 % vs. 90.2 %) or EPTB (63.2 % vs. 81.6 %) when using culture as the gold standard. Out of the 122 (27.2 %) true- positives samples that grew in culture, only 88/122 (72.1 %) were detected by AFB smear for PTB. As for EPTB samples, out of 38(28.6 %) true-positives samples, only 24/38 (63.2 %) were positives by AFB smears. These results came in parallel with previous studies conducted for evaluation of the Xpert assay performance , 24, 25.In a related matter, Muia et al.  documentIn our study, the false- negative rate of smear microscopy reached 27.9 % with PTB and 36.8 % with EPTB samples. Similarly, Meawed and Shaker 2016 confirmed that, ZN smear microscopy carries the risk of false-negative results and incompetency to discriminate between drug- susceptible and drug- resistant strains of MTB owing to poor sample quality coupled with a need for an experienced specialist. Meanwhile, culture being the gold standard, it proceeds for weeks up to months to yield results, and depends on sophisticated laboratory facilities and skilled technicians .Although smear microscopy has the advantages of being rapid, simple and lower cost procedure, this study revealed that smear microscopy is prone to misdiagnosis MTB infection. Misdiagnosis of either PTB or EPTB has grave implications of continued transmission, higher mortality rates and delayed appropriate therapy .For diagnosis of PTB, the GeneXpert MTB/RIF assay proved overall sensitivity, specificity, PPV and NPV of 90.2 %, 86.9 %, 99.1 and 95.9 % respectively in relation to LJ culture. Out of the 122 27.2 %) isolates that grew in culture, 110 (90.2 %) were identified by GeneXpert as MTB and were true- positives for PTB. Gawish et al. 2019  reported.2 % isolIn a recently published research article in Egypt by Hefzy et al.  the deteThe first analytical study to validate the GeneXpert technology noted that the assay had 100 % sensitivity and specificity for diagnosis of PTB . In a muThe false- positive results detected by Xpert MTB/RIF from one patient with PTB whose BAL sample was culture negative, may be due to the presence of residual DNA of old dead organisms owing to previous history of TB or a sub-clinical relapse of the disease. False- positive outcomes yielded by Xpert assay could be one of justifications due to cross-contamination, added to the fact that GeneXpert can detect intact bacteria and cannot explore free DNA fragments .As for EPTB, overall sensitivity, specificity, PPV and NPV of Xpert assay were 81.6 %, 78.9 %, 77.5 and 91.5 % respectively in relation to LJ culture. Allahyartorkaman et al. found 76.5 % sensitivity, 95.9 % specificity, 62 % PPV, and 97.9 % NPV for diagnosis of EPTB by Xpert MTB/RIF assay. The author declared that Xpert MTB/RIF assay proved to be highly sensitive, specific and comparable to standard conventional methods for the diagnosis of PTB. However, the sensitivity and specificity for EPTB specimens were highly variable .As we compared the ability of smear microscopy with Xpert MTB/RIF assay in PTB detection, smear microscopy detected 88/122 MTB cases while Xpert assay detected 110/122 including all true -positive cases of smear microscopy plus 22 positive cases among subjects with smear- negative results. Thus Gene Xpert MTB/RIF out performed AFB microscopy and established a diagnosis of presumptive PTB for few cases with smear- negative TB, which came in accordance with other previous studies , 16.According to current results, the false- negative specimens were higher with EPTB than PTB for the Xpert assay (18.5 % vs. 9.8 %).It is evidenced that, the GeneXpert technique had variable performance for different biological samples compared with the optimal sputum samples in PTB. Extrapulmonary samples like CSF, ascetic fluid and urine samples had lower bacillary burdens; the diagnostic efficacy are quite variables in different studies .The higher cost of the Xpert assay must be compared to the benefit from avoiding poor sensitivity and specificity of AFB microscopy. To reduce the relative high cost of the assay, it is important to decrease the chance of getting failed or invalid test result. The present study found that an Xpert MTB/RIF- based strategy is more effective than smear- based one for both PTB EPTB as well as for evaluation of transmission potential of infected cases.As for rifampicin resistance, our study included analytical data that highlighted the most significant risk factors for development of rifampicin resistance among the studied cases. The univariate analysis revealed that all of the site or type of TB, previous exposure to anti-TB therapy, a positive smear and a history of contact with a known TB case were statistically significant factors (P < 0.05).This observation was consistent with previous studies conducted elsewhere and indicated that previous exposure to anti-TB treatment might be the most significant risk for MDR-TB , 36.The acquired rifampicin resistance can occur when there is a history of incomplete treatment regimens lasting at least 1 month . Prior iLack of Mycobacteria growth indicator tube (MGIT) as a rapid liquid culture methods and the potential impact on the study, lack of evaluating the diagnostic precision of the GeneXpert assay on samples other than those tested in this study  and finally shortage of studying the impact of the assay on patient’s outcomes; are the most recognized limitations of this study.The out performance of Xpert MTB/RIF detected in current work is in agreement with other researchers who established the diagnosis in a significant proportion of cases. Moreover, the relative gain and more case detection by means of Xpert recommend performing Xpert as the first diagnostic test, to avoid extraordinary work load. High sensitivity of GeneXpert MTB/RIF detected in the current work allows ruling out the disease with a high degree of confidence.