The diagnosis of peripheral pulmonary lesions (PPLs) is a challenging task for pulmonologists, especially for small PPLs. Conventional localization of these small PPLs, which are > 1 cm away from the visceral pleura in operation, is quite difficult. Currently used methods inevitably damage the visceral pleura and may cause a series of complications, such as pneumothorax and hemothorax. Hence, the present study aimed to find out an intraoperative localization method with no damage to the visceral pleura.We retrospectively reviewed 21 patients with PLLs who underwent electromagnetic navigation bronchoscopy (ENB)-guided biopsy plus a new methylene blue staining with the help of massage (Massage Staining) in our department between August 2017 and December 2018.rate of successful biopsy was 76.2%, and the rate of excellent or satisfactory of Massage Staining was 81.0%, while all lesions of these 21 cases were included in the range of staining, and the median distance from the edge of the stained site to the edge of the lesion was 29 ± 18 mm. The duration of ENB-guided biopsy plus Massage Staining was 26.7 ± 5.3 min, and the intraoperative blood loss was 3.3 ± 1.5 ml. No pneumothorax, hemorrhage, and tracheal injury occurred intraoperatively.The median age of these 21 patients was 51.3 ± 2.1 years. The diameter of the PPLs was 8.2 ± 2.3 mm. The The ENB-guided biopsy combined with Massage Staining is an innovative one-stop strategy designed to enhance the precision of thoracic surgery. The Massage Staining avoids damage to the visceral pleura, causes the low incidence of complications, but yields precise localization of PPLs. With the widespread application of computed tomography (CT), more cases with peripheral pulmonary lesions (PPLs), e.g., ground-glass nodules (GGNs), have been detected. Superficially localized solid nodules with pleural indentation can be visualized or palpable during surgery. However, the detection of pure GGNs and sub-solid nodules (SSNs) accompany with challenges. During operation, the approximate location of the pulmonary nodules can be determined by preoperative CT scan images. However, there is often a deviation in terms of location of lesions before and after the collapse of the lungs, causing that lesions cannot be accurately located, leading to tremendous medical risk. Other conventional positioning methods include CT-guided implantation of hookwire or coils and CT-guided methylene blue staining. Nevertheless, those methods damage the visceral pleura and induce pneumothorax, hemothorax, detachment, or movement from the localized object s.Electromagnetic navigation bronchoscopy (ENB) is a promising technology that increases the diagnostic accuracy of peripheral lung and mediastinal lesions and is taken as a supplement to traditional bronchoscopy, endotracheal ultrasound, and endotracheal biopsy techniques into consideration . PreoperIn this study, we initially and innovatively conducted a novel Massage Staining without damage to the visceral pleura, combining with ENB-guided biopsy for diagnosis of PPLs.Patients were screened between August 2017 and October 2018 to undergo ENB-guided biopsy and Massage Staining. The inclusion criteria were as follows: (1) patients who did not undergo pathological diagnosis and anti-tumor treatments before operation; (2) being resistant to ENB-guided biopsy; (3) the distance from the edge of the lesion to the visceral pleura ≤ 15 mm; (4) patients who did not have any contraindications for pulmonary surgery, i.e., distant metastasis, bleeding tendency, blood clotting disorders, cardiopulmonary insufficiency, severe arrhythmia or hypertension, pulmonary hypertension, and acute respiratory infection. This study was approved by the Ethics Committee of the Daping Hospital, Army Medical University . Besides, all the patients signed a written informed consent form.The Super-D electromagnetic navigation system was purchased from Covidien , including a 1.9 × 1070-mm edge positioning guide wire, a 2.8 × 1050-mm extended working channel for the locatable guide, 1.8 × 1050-mm biopsy forceps, and a 1.8 × 1000-mm hollow cannula. Video-assisted thoracoscopic surgery (VATS) was performed using the STORZ thoracoscopic system , and the robotic surgery was conducted by using the da Vinci Surgical System .The patients received general anesthesia and single-lumen intubation. After exploration with a bronchoscope, the locatable guide wire was inserted via an extended working channel to dilate the working channel, where the virtual and actual bronchoscopy images were matched by Super-D electromagnetic navigation system. A navigation map to the target area was then automatically generated Fig. a, b. UsiP value < 0.05 was considered statistically significant. Statistical analyses were carried out by using SPSS 23.0 software .Continuous variables were presented as median values. The chi-square test was applied to compare categorical variables among the groups, and A total of 21 patients with PPLs  underwent ENB-guided biopsy combined with the Massage Staining. There were 8 (38.1%) cases with solid nodules, 8 (38.1%) cases with mixed ground-glass nodule (mGGN), 3 (14.3%) cases with pure ground-glass nodule (pGGN), and 2 (9.5%) cases with cavitary lesions. The diameter of the PPLs was 8.2 ± 2.3 mm. There were 6 (28.6%) cases in the right upper lobe, 1 (4.8%) case in the right middle lobe, 4 (19.0%) cases in the right lower lobe, 5 (23.8%) cases in the left upper lobe, and 5 (23.8%) cases in the left lower lobe  were consistent with postoperative pathology, which accompanied the successful biopsy rate of 76.2%.P > 0.05)  Table . AccordiIt is noteworthy that the correct diagnosis of pulmonary lesions is vital for the selection of an appropriate surgical approach. In the preoperatively diagnostic process, sensitivity and specificity of positron emission tomography-computed tomography (PET/CT) were 88% and 77%, respectively . MeanwhiDifferent from the above methods, ENB employs a three-dimensional reconstruction of CT scan data and sensor location technology to guide a steerable endoscopic probe to PPLs . MulticeSmall peripheral pulmonary lesions (PPLs), which are 1 cm away from the visceral pleura, are usually invisible and untouchable during surgery. Conventional positioning and staining methods, e.g., CT-guided implantation of hookwire or coils, inevitably damage the pleura, potentially causing a series of complications such as pneumothorax and hemothorax. Kleedehn et al. compared methylene blue staining and hookwire localization and found that the incidence rates of complications reached 54% and 46%, respectively . Our intIn order to find out an appropriate dye marking, methylene blue, indigo carmine, and fibrin glue might be helpful. Indigo carmine dye has an advantage in that it can be visible at least 3 days after marking, while methylene blue is dissipated after several hours of staining. Therefore, indigo carmine should be the first choice for patients who cannot promptly undergo surgery after staining . The mixFurthermore, the details are supposed to be focused on the following: (1) The results of Massage Staining were based on the distance from staining spot to the visceral pleura, rather than size and imaging features of PPLs. (2) The dose of the dye should be as small as possible. The point-like rubbing between the cannula and the pleura can effectively mark the position of the lesion and reduce the spread of the dye.The ENB-guided biopsy plus Massage Staining is characterized by no damage to the visceral pleura, small damage to lung tissue, low incidence of complications, and high precision, as well as “one-stop” diagnosis and localization. Besides, this proposed strategy improves the rate of successful resection of PPLs in minimally invasive surgery. However, due to the small size of samples, it is essential to perform further multiple-center study to improve and optimize the proposed surgical technique.


DMDmdx, Largemyd, DMDmdx/Largemyd, with variable histopathological alterations. Similar results were observed in the dystrophic models, which maintained normal levels of PAX7 expression, retained the Pax7-positive SCs pool, and their proliferation capacity. Moreover, elevated expression of MYOG, an important myogenic factor, was also observed. The ability to form new fibers was verified by the presence of dMyHC positive regenerating fibers. However, those fibers had incomplete maturation characteristics, such as small and homogenous fiber caliber, which could contribute to their dysfunction. We concluded that dystrophic muscles, independently of their degeneration degree, retain their SCs pool with proliferating and regenerative capacities. Nonetheless, the maturation of these new fibers is incomplete and do not prevent muscle degeneration. Taken together, these results suggest that the improvement of late muscle regeneration should better contribute to therapeutic approaches.Satellite cells (SCs) are the main muscle stem cells responsible for its regenerative capacity. In muscular dystrophies, however, a failure of the regenerative process results in muscle degeneration and weakness. To analyze the effect of different degrees of muscle degeneration in SCs behavior, we studied adult muscle of the dystrophic strains:  SCs reside between the basal lamina and the muscle membrane4 and although quiescent in normal adult muscles, SCs can be activated by specific signals upon muscle injury. In diseases characterized by chronic degeneration process, such as muscular dystrophies, SCs are constantly activated, leading to the depletion of the SCs pool and consequent failure of the regenerative process5.Skeletal muscle tissue represent about 40% of total body weight and is responsible for almost all voluntary movements. It has a high regenerative capacity after injury, which is directly linked to the presence of satellite cells (SCs). These cells are the main muscle stem cells and have a key role in muscle development during embryogenesis7.Muscular dystrophies are a heterogeneous group of genetic diseases that cause a progressive loss of motor ability. Defects in components of the dystrophin-glycoprotein complex (DGC) are responsible for different forms of muscular dystrophies. The DGC links the myofiber cytoskeleton proteins and the extracellular matrix, providing mechanical support to sarcolemma during myofiber contractionDMD) gene can lead to Duchenne/Becker muscular dystrophies8. DGC proteins are linked to extracellular matrix proteins by post-translational glycosylation of α-dystroglycan (α-DG) protein. Mutations in the LARGE1 gene, which encodes a glycosyltransferase that acts in the α-DG, lead to congenital muscular dystrophy type 1D11.Mutations in the dystrophin , independently of the degree of muscle degeneration . Dystrophic animals showed the following proportions of activated SCs: DMDmdx 17.4%, Largemyd 25.1% and DMDmdx/Largemyd 16,.7% , nor Myf5 . In spite of that, we observed a significant elevation  in Myog expression among the four mice models. Using the post hoc analysis with Bonferroni method of correction, a significant difference on Myog expression was found between wild-type and Largemyd (p < 0.001), wild-type and DMDmdx/Largemyd (p = 0.0023), and wild-type and DMDmdx (p = 0.0067). No significant differences were found between the dystrophic strains .As data presented great variance in the expression of the myogenic factors, the non-parametric Levene’s test was done to verify the equality of variances in samples. We observed that they were not significantly different (Dmdmdx strain presented the greatest number of dMyHC+ fibers (17%). Largemyd and Dmdmdx/Largemyd showed respectively 7% and 10% of new fibers . In addition, the size of these new dMyHC+ fibers was more homogeneous, as shown by the coefficient of variation within normal values (lower than 250) in Dmdmdx (150), Largemyd (245) and Dmdmdx/Largemyd (155). These results suggest that in the dystrophic models, these new fibers remain small and do not complete the regenerative process.The number and diameter of new muscle fibers were evaluated by detection of developmental myosin heavy chain isoform (dMyHC) Fig. . Immunofmyd Fig. , with di18.To confirm our results, the muscles from the different dystrophic strains were compared to regenerating normal muscle subjected to acute lesion by electroporation, at days zero, five, and 60 days post-injury Fig. . In wildChi-square = 81.21, df = 3, p < 0.001). The differences among the three dystrophic strains were also statistically significant , with a higher proportion in the more severely affected muscles: Dmdmdx/Largemyd > Largemyd > Dmdmd.  granules. Upon activation, miR-31 levels decrease and Myf5 mRNA is released to translation35. Therefore, this could explain the similar relative quantification of Myf5 mRNA in all strains.MYF5 is expressed in both activated and quiescent SCs, but quiescent cells can maintain their quiescence state using a post-transcriptional regulation by miR-31, which sequester 36. Upregulation of MYOD expression leads SCs to exit the cell cycle and to differentiate and fuse, forming myotubes33. In spite of the normal expression of MYOD in the muscle of these three dystrophic models, they showed regenerating fibers as observed by dMyHC staining  isoformDmdmdx/Largemyd, as compared to the other strains , Largemyd (n = 6), C57Bl/6 (n = 6) and double mutant DMDmdx/Largemyd .Histological, immunofluorescence, gene expression and protein analyses, were examined in the gastrocnemius muscle of adult male mice with 12–14 weeks of age. This muscle has about 51.5% of fiber type IID, 22.7% of fiber type IIA, 18.2% fiber type IIB and 7.6% of fiber type IDuring the experiments, animals were bred and housed in controlled temperature and light in the animal house of the Human Genome and Stem Cell Research Center (HUG-CELL).http://www.ib.usp.br/comissoesib/ceua/), of the Institute of Biosciences, University of Sao Paulo (protocol number 246/2016), which is the licensing committee of our Institution. In addition, the used methods were carried out in accordance with the relevant guidelines and regulations from the Brazilian National Council for Control of Animal Experimentation .All experimental protocols and methodologies were performed in accordance with and were approved by CEUA - COMISSÃO DE ÉTICA NO USO DE ANIMAIS . RNA was solubilized in RNase-free water (Ambion™) and quantified in a Nanodrop spectrophotometer (Thermo Fisher Scientific). The cDNA synthesis was performed using 1 µg of total RNA according to the M-MLV protocol (Invitrogen). Samples were amplified in triplicates using the MasterMix containing Sybr Green (Applied Biosystems) and primers (Table 42. The membranes were then incubated with the appropriated secondary horseradish peroxidase conjugated anti-rabbit or anti-mouse antibodies (Thermo Fisher Scientific) and revealed using the Novex™ ECL Chemiluminescent Substrate Reagent kit (Invitrogen). Images were obtained using photo-documenter , all images were collected after 450 seconds of standard exposure. For the reaction with the endogenous control, membranes were stripped with a stripping buffer (Thermo Fisher Scientific) and reprobed with GAPDH antibody 43. Bands were quantified by densitometry using the software ImageQuant TL . The protein of interest and the endogenous protein were probed and analyzed in the same membrane, both for experimental and control samples.Frozen muscle samples were homogenized in RIPA buffer (Sigma-Aldrich) supplemented with 1% of protease inhibitors (Sigma-Aldrich). Samples were kept on ice for 30 minutes and afterward centrifuged at 16.000 g for 20 min at 4 °C, followed by protein quantification using the Pierce BCA Assay kit (Thermo Scientific). Homogenized samples with 30 µg of protein were separated by SDS-PAGE in 4–15% Mini-PROTEAN® TGX™ Gels (BioRad) and transferred to nitrocellulose membranes using iBlot® Gel Transfer Device (Invitrogen) for 7 minutes. The obtained membranes were probed using the antibodies: PAX7  and Myogenin (Abcam ab15232)Transverse cross sections of the right gastrocnemius were stained with H&E and histopathological evaluation was performed by measuring muscle fibers to obtain the fiber diameter utilizing ImageJ software . The mean fiber diameter of each strain was obtained, and the variability coefficient was calculated according to the formula: Standard Deviation x 1000 ÷ Diameter Mean. Results were represented as mean variation and variability coefficient. The stained sections were observed under a light microscope (Zeiss).To identify regions with collagen deposition, indicative of connective tissue replacement and infiltration between muscle fibers, the slides were immersed in Bouin fixative for twenty minutes and washed in water. The sections were stained with Sirius red (0.2 g) dissolved in saturated picric acid (100 mL) for 60 minutes, dehydrated and mounted in Canada balsam. The differences were quantified by the positive area of staining, labeled in red, relative to the total area of the section, using ImageJ software . The stained sections were observed and photographed under the same exposure conditions (16 ms), using a light microscope (Zeiss) and the Axiovision software Release 4.8.2 SP3 (2013).The presence of SCs was determined by double staining with antibodies against PAX7 and laminin to label the basal lamina. Activated SCs were determined by double staining with antibodies against PAX7 and KI67. Primary antibodies used: PAX7 , laminin  and KI67 . Secondary antibodies were Cy3-labeled sheep anti-mouse IgG  and FITC-labeled donkey anti-rabbit IgG . The 6 µm muscle sections were fixed with 4% formaldehyde solution and submitted to antigen retrieval with hot citric acid buffer. Primary antibodies were incubated overnight at 4 °C. Secondary antibodies were incubated for one hour at room temperature. The slides were mounted with Vectashield with DAPI to counterstain nuclei. Immunofluorescence micrographs were obtained with a conventional fluorescence microscope (Zeiss). The proportion of PAX7 positive nuclei to the total number of fiber was quantified . The proportion of double stained PAX7 and KI67 were analyzed in approximately 200 PAX7 positive nuclei. All images were collected using a Zeiss microscope with epi-fluorescence, using specific filters for DAPI, FITC and for Cy3. The images were photographed using the Axiovision software Release 4.8.2 SP3 (2013), under the same exposure conditions (120–130 ms), standardized for the Cy3 staining for identification of dMyHC expression. The same software created merged figures automatically.44 and laminin 45, washed, and then incubated for 1 h with secondary antibodies Cy3-labeled sheep anti-mouse IgG  and FITC-labeled donkey anti-rabbit IgG , respectively. After washing, the slides were mounted with Vectashield and DAPI, to counterstain nuclei. Immunofluorescence micrographs were obtained with a conventional fluorescence microscope (Zeiss) and the percentage of dMyHC+/total fibers was quantified . The fiber diameter of dMyHC+ fibers of each strain was measured manually, and the variability coefficient was calculated according to the formula: Standard Deviation x 1000 ÷ Diameter Mean38. All images were collected using a Zeiss microscope with epi-fluorescence, using specific filters for DAPI, FITC and for Cy3. The images were photographed using the Axiovision software Release 4.8.2 SP3 (2013), under the same exposure conditions (120–130 ms), standardized for the Cy3 staining for identification of dMyHC expression. Merged figures were created automatically by the same software.The antibody Clone RNMy2/9D2 recognizes a myosin heavy chain (MHC) present during the embryonic and neonatal period in the development of skeletal muscle. The same MHC is expressed during regeneration of muscle fibres. The presence of dMyHC indicates myogenic activity by newly differentiated fibers labeled in red. The 6 µm muscle sections were double labeled with primary antibodies to dMyHC Differences among the groups were assessed using one-way ANOVA Test, and Kruskal-Wallis Test, for small and non-normally distributed set of data. Results are reported showing means and standard deviations in bar graphs and medians in boxplot graphs. The statistical analyses were calculated using GraphPad Prism version 7 for Windows , Minitab 17 Statistical Software  and IBM SPSS Statistics for Windows version 24.0  software and a p-value equal or less than 0.05 was considered to be significant. For the expression analysis, additional five animals  from each strain were added , to confirm and strengthen the statistical analysis.Supplemental information


Coagulase-negative Staphylococci (CoNS) have emerged as a major causative agent of blood-stream infections (BSI). Linezolid (LZD) is currently used for treating glycopeptide and methicillin-resistant staphylococci. It is important to understand the resistance mechanism and probable transmission of LZD resistant (LR) CoNS within the hospital.rRNA gene sequence analysis. Antimicrobial susceptibility and MIC of vancomycin and LZD were determined. LZD resistance mechanisms using PCR for the cfr gene and mutation in the V domain of the 23S rRNA gene were studied.Clinically significant LRCoNS from patients with BSI were characterized using MALDI-TOF and 16S cfr gene was identified in all the isolates. Sequence analysis of V domain region of 23S rRNA gene confirmed mutation in single copy among 12/15 isolates with novel mutations: G2614 T and C2384T. All infections were nosocomially acquired and LZD resistance was emerging in the absence of prior LZD use. Horizontal spread of resistant isolates and cfr gene among diverse species were the probable mechanisms of transmission.The MIC of LZD ranged from 8 to 32 μg/ml. LR was observed in three different CoNS species from diverse locations within the hospital. The The study highlights the novel mutations associated with LRCoNS and the importance of surveillance & transmission pathway within the hospital. It also systematically discusses the published information on LRCoNS. S. epidermidis has been increasingly recognized in recent years [S. epidermidis and S. haemolyticus are resistant to multiple antibiotics [Coagulase-negative Staphylococci (CoNS) are normal commensals of the skin and mucous membranes and have emerged as the important cause of hospital-acquired infections , 2. Theyibiotics . Methiciibiotics . Managemibiotics .S. aureus, coagulase negative staphylococci, penicillin-resistant Streptococcus pneumoniae and vancomycin-resistant Enterococci (VRE) [tRNA in the peptidyl transferase centre of 23S rRNA in the 50S ribosomal subunit [Linezolid (LZD) is a synthetic drug of oxazolidinone class of antibiotics, which is approved for treatment of severe bacterial infections in adults caused by drug-resistant gram-positive bacteria, such as multidrug-resistant ci (VRE) –7. When ci (VRE) , 9 Howevci (VRE) . Despiteci (VRE) . Resistaci (VRE) . The bacrRNA gene or the gene cfr (chloramphenicol florfenicol resistance) [cfr-mediated resistance and mutational resistance has also been documented and pose a therapeutic concern [23S rRNA is most common, other mutations including T2500A, G2603 T, C2534T, T2504A, G2447 T, G2215A, and G2631 T, have been reported among clinical staphylococcal isolates [cfr gene is also of great concern as it is usually plasmid-borne and can be easily disseminated to susceptible population [cfr gene also encodes resistance to a group of chemically distinct antibiotics such as phenicols, lincosamides, pleuromutilins and streptogramin-A leading to a multidrug-resistant phenotype [cfr gene was first seen in veterinary isolates of Staphylococcus warneri, Staphylococcus sciuri, Staphylococcus hyicus, and S. aureus perhaps associated with the veterinary use of phenicols [Resistance to LZD is primarily caused by mutations in the domain V of 23S istance) . Co-occu concern . The G25isolates , 12. Respulation . The cfrhenicols . There aIn the current study, species distribution, susceptibility profile and the mechanism of LZD resistance among LRCoNS were studied. An attempt was made to study the clinical profile of patients with BSIs caused by LRCoNS and study the probable transmission pathway within the hospital.Clinically significant LRCoNS (LZD MIC ≥8 μg/ml) isolated from patients with BSI admitted at Safdarjung Hospital, New Delhi, India from August 2013 to August 2015 were studied. Only one isolate per patient was included in the study. CoNS were identified by Gram stain, the presence of catalase, and negative tube coagulase test . StandarTo study the transmission pathway line listing of the patients with LRCoNS BSI was done using the date of admission, date of positive blood culture, date of discharge or outcome and is shown in Table S. aureus ATCC 29213 and S. aureus ATCC 700699 (Mu50) were used as a control strains for all susceptibility assays.Antimicrobial susceptibility testing was done using disc diffusion assay . All discs were procured from Oxoid Ltd., Basingstoke, UK. Minimum inhibition concentrations (MIC) of LZD  and vancomycin  were determined by resazurin dye based microbroth dilution. All assays were performed as per the guidelines of CLSI, 2015 , 14. S. g for 3 min and washed with 1X PBS twice at 8000X g, 3 min and suspended in 500 μl of 50 mM EDTA, lysostaphin (100 μg/ml) and lysozyme (100 μg/ml) for 1 h at 37 °C followed by manufacturer’s instructions.DNA was isolated using Wizard genomic kit  from 2 ml of overnight grown bacterial cultures in Mueller Hinton broth. Cells were pelleted at 10,000X cfr gene was amplified from the extracted DNA using forward 5′-TGA AGT ATA AAG CAG GTT GGAG-3′ and reverse 5′-ACC ATA TA A TTG ACC ACA AGC AG-‘3 primer set as described previously [eviously .V region was amplified using forward 5′-GCGGTCGCCTCCTAAAAG-3′ and reverse 5′-ATCCCGGTCCTCTCGTACTA-3′ primers. Amplification started with an initial denaturation at 95 °C for 5 min followed by 30 cycles of denaturing , annealing , and extending cycles ; and a final extension of 10 min at 72 °C. PCR products corresponding to 420 base pairs were confirmed by running on 2% agarose gel. The amplified product was eluted and purified using gel purification kit and sequenced. Five copies of the 23S rRNA gene were amplified using specific primers for each gene copy as described earlier [The domain  earlier . PCR proS. haemolyticus isolates as described earlier [Staphylococcus aureus ATCC25923 was used as a reference control. PFGE patterns were further analysed using a temporary BioNumerics evaluation license from Applied Maths. Permission to publish the PFGE data was obtained from Applied Maths.PFGE was performed for nine  earlier . Brieflyn = 3), medicine (n = 1), oncology (n = 1), obstetrics and gynaecology (n = 1), respiratory medicine (n = 1) ward and ICU (n = 8)  of the patients.LZD resistance was observed among 15 clinically significant CoNS isolates from patients with clinical sepsis. The clinical profile and outcome of patients with LRCoNS BSI are shown in Table S. haemolyticus (n = 9), S. cohnii (n = 3) and S. arlettae (n = 3). The PFGE analysis of the S. haemolyticus isolates revealed that 6 out of 9 isolates showed a distinguishable pattern from each other  were also resistant to cefoxitin, erythromycin, clindamycin, trimethoprim/sulfamethoxazole as determined by disc diffusion method. Resistance to chloramphenicol, gentamicin, and ciprofloxacin was observed in 80% (12/15), 90.3% (14/15) and 90.3% (14/15) isolates respectively. However, all isolates were susceptible to vancomycin (MIC range 1-4 μg/ml) and only 33% of the strains were susceptible to teicoplanin.cfr gene with a specific amplification of 746 base pair. Mutations were detected in 12/15 isolates in a single copy of 23S rRNA gene  and S. arlettae . C2384T mutation was observed in one isolate of S. cohnii (LZD MIC 8 μg/ml) and two mutations were observed (G2614 T & C2384T) in one isolate of S. arlettae (LZD MIC 16 μg/ml). Among 3 isolates  no mutation was identified (LZD MIC 8-32 μg/ml).All strains were positive for Infections caused by CoNS are endogenous and skin and skin and mucous membranes colonization are the key source of infections . These oS. haemolyticus, followed by S. cohnii and S. arlettae. This is the first report of LZD resistance in S. arlettae mediated by both cfr gene and novel mutations in domain V region of the 23S rRNA gene. The most commonly observed mutation, G2576 T in domain V region of the 23S rRNA gene, was not detected in our study. However, novel mutations were observed and included G2614 T in S. haemolyticus and S. arlettae and C2384T in S. cohnii and S. arlettae.In the current study, CoNS sepsis was observed predominantly in ICU patients and all patients had co-morbid conditions with invasive procedures. LZD resistance was observed in 3 different CoNS species; most common being V region of the 23S rRNA gene, the location of the target for LZD [23S rRNA gene, which may explain the low MIC. However, novel mutations  were detected and their effect on LZD MIC was difficult to determine because of the presence of the cfr gene, which was detected in all isolates. The heterogeneity of MIC even among isolates with the same number of mutant alleles suggests that other mechanism of LZD resistance might be affecting linezolid susceptibility.The LZD MIC among LRCoNS ranged from 8 to 32 μg/ml and there was no correlation between MIC, mechanism/mechanisms of resistance, type of mutation observed Table . This is for LZD . A gene  for LZD . In the S. arlettae isolates, two were isolated from patients admitted to pediatric wards in the month of May 2013 and July 2013 , 5/9 isolates were from ICU and other 4/9 isolates were from diverse locations in the hospital. Seven different PFGE banding pattern were observed among the nine S. haemolyticus isolates obtained from various departments. Only three isolates obtained from patients  showed a similar pattern indicating emergence from the same clone. These findings suggest that different clones of LRCoNS may be circulating in the hospital which is in contrast to other published studies where all isolates showed similar PFGE banding pattern [The LRCoNS were isolated from diverse locations within the hospital Table . Among t pattern , 23, 24.S. haemolyticus. Antibiograms of isolates patient 3 and 4 differed from patient 7,10 and 11, however all isolates have a G2614 T mutation and cfr gene. The PFGE suggested all isolates except those from patient 3 and 4 were indistinguishable. Moreover, the time line of events from admission to discharge/death suggests probable spread through cross transmission within the ICU due to a breach of infection control practices. The prolonged ICU stay, associated comorbid conditions and use of invasive devices may have further contributed to cross transmission. Patient 8, 14 and 15 , S. arlettae (cfr gene without mutation) and S. cohnii (cfr gene without mutation) which were phenotypically distinct and with different LZD resistance mechanism. The possibility of horizontal transmission among different CoNS species mediated by cfr gene from LR S. haemolyticus cannot be ruled out. These findings suggest that linezolid resistance emerged in ICU both due to cross-transmission between patients and horizontal transfer of LZD resistance mediated by cfr gene among different CoNS species. However, limitation of this study is that due to lack of resources and involvement of diverse species of CONS, we could not confirm the transmission pathway using suitable typing method for species other than LR S. haemolyticus.The majority (8/15) of LRCoNS was isolated from the ICU and belonged to three diverse species. Probable transmission routes in the ICU are explained diagrammatically in Fig. In earlier reports, previous administration of LZD has been reported to be an independent predictor of LZD resistance in CoNS , interescfr ribosomal methyltransferase. In all reported cases, strains including S. haemolyticus, S. cohnii, S. epidermidis, S. hominis, S. capitis, S. sciuri, S. lugdunensis, S. simulans and S. kloosii were isolated from an aseptic sample, which included blood, pus, CSF and catheter tips. In our study, S. haemolyticus was the predominant species and novel mutations were reported. There have been limited reports of S. arlettae. Dinakaran et al., reported a case of S. arlettae isolated from the blood of a cardiovascular disease patient [S. arlettae could be an emerging pathogen.LRCoNS were reported worldwide, including North America, South America, European and Asia. The mechanisms for LZD resistance were L3/L4 mutation, mutations in the 23S rRNA and the presence of a transmissible  patient . Our stuS. haemolyticus has emerged as a nosocomial pathogen on account of its ability to attain high-level resistance to many antibiotics including glycopeptides. We observed 77% of S. haemolyticus resistant to teicoplanin in absence of prior exposure to this antibiotic. This linezolid-teicoplanin-resistant S. haemolyticus (LTR-SH) is a threat. There is thus an urgent need to identify CoNS to species level along with antibiotic profile before initiating therapy, as these are rarely determined in hospital settings.Interestingly, all the isolates reported were susceptible to vancomycin (MIC range 1-4 μg/ml) and 66% isolates were resistant to teicoplanin. Staphylococcus will not only help in proper antibiotic usage but will also avoid emergence of multidrug resistant bacteria [cfr gene is a more worrying threat because of its rapid spread and horizontal transmission between species [Staphylococcus is still sporadic now, the prolonged hospital stays; frequent interventions and misuse of antibiotics may accelerate the dissemination of LZD resistance Staphylococcus [Surveillance of LZD resistance in bacteria . Mutatio species . This ge species . Though lococcus . So, we lococcus .


What is the level of investment by the National Institutes of Health (NIH) to fund clinical research focused on Asian American, Native Hawaiian, and Pacific Islander populations?This cross-sectional study found 529 clinical research projects focused on Asian American, Native Hawaiian, and Pacific Islander participants funded by the NIH between 1992 and 2018, composing 0.17% of the total NIH budget. This proportion of the total NIH budget has only increased from 0.12% before 2000 to 0.18% after 2000.These findings suggest that without overt direction from federal entities, dedicated funds for health disparities research, and parallel efforts to increase diversity in the biomedical workforce, investments may continue to languish for Asian American, Native Hawaiian, and Pacific Islander populations. This cross-sectional study investigates spending by the US National Institutes of Health (NIH) for studies focusing on Asian American, Native Hawaiian, and Pacific Islander populations. Advancing the health equity agenda for Asian American, Native Hawaiian, and Pacific Islander (AA/NHPI) individuals has become an intersecting priority for federal agencies. However, the impact of federal investments and legislation to ensure systematic processes and resources to eliminate health disparities in AA/NHPI populations is unclear.To perform a portfolio review of clinical research funded by the National Institutes of Health (NIH) for AA/NHPI populations and determine the level of NIH investment in serving these populations.Cross-sectional study in which the NIH Research Portfolio Online Reporting Tools Expenditures and Results system was queried for extramural AA/NHPI-focused clinical research projects conducted in the United States from January 1, 1992, to December 31, 2018. Clinical research funded under research project grants, centers, cooperative awards, research career awards, training grants, and fellowships was included, with an advanced text search for AA/NHPI countries and cultures of origin. Project titles and terms were screened for inclusion and project abstracts were reviewed to verify eligibility. Descriptive analyses were completed.Outcomes included NIH funding trends and characteristics of funded projects and organizations. The proportions of AA/NHPI-related funding trends were calculated using 2 denominators, total NIH expenditures and clinical research expenditures.There were 5460 records identified, of which 891 studies were reviewed for eligibility. Of these, 529 clinical research studies focused on AA/NHPI participants composed 0.17% of the total NIH budget over 26 years. Projects studying AA/NHPI individuals in addition to other populations were funded across 17 NIH institutes and centers. The top 5 funders collectively contributed almost 60% of the total funding dollars for AA/NHPI projects and were the National Cancer Institute ($231 584 664), National Institute on Aging ($108 365 124), National Heart, Lung, and Blood Institute ($67 232 910), National Institute on Minority Health and Health Disparities ($62 982 901), and National Institute on Mental Health ($60 072 779). Funding of these projects ($775 536 121) made up 0.17% of the overall NIH expenditures ($451 284 075 000) between 1992 and 2018, and 0.18% ($677 479 468) of the NIH research budget after 2000. Funding for AA/NHPI projects significantly increased over time, but the proportion of the total NIH budget has only increased from 0.12% before 2000 to 0.18% after 2000. Of total funding, 60.8% was awarded to research project grants compared with only 5.1% allocated to research career awards, training grants, and fellowships.Increases in research dollars for AA/NHPI clinical research were not associated with increases in the overall NIH research budget, and underrepresentation of AA/NHPI subgroups still remains. Without overt direction from federal entities and dedicated funds for health disparities research, as well as parallel efforts to increase diversity in the biomedical workforce, investments may continue to languish for AA/NHPI populations. The prevailing stereotype that AA/NHPI groups are model minority populations2 has resulted in data equity efforts negatively affecting AA/NHPI subgroups .3 Data equity is the need for high-quality, disaggregated racial/ethnic data to capture disparities and underlying social factors associated with health needed to develop evidence-based solutions that inform public policies.The landmark 1985 Report of the Secretary’s Task Force on Black and Minority Health (known as the Heckler Report)5 Data for AA/NHPI populations are typically grouped together, which can conflate or inflate the magnitude of health and health outcomes. For example, in aggregate, AA/NHPI adult rates of liver cancer incidence and mortality are double those of non-Hispanic white adults.6 However, when data are disaggregated, liver cancer incidence is 7 times higher and 9 times higher for Laotian men and women, respectively, compared with non-Hispanic white adults.6 Understanding the complexities of health by subgroup could be the difference between eliminating or worsening health disparities.8Asian American, Native Hawaiian, and Pacific Islander populations represent more than 50 countries or cultures of origin and 100 different languages and are the fastest-growing racial/ethnic group in the United States.9 Asian American, Native Hawaiian, and Pacific Islander individuals were more likely to speak a language other than English compared with non-Hispanic white individuals.9 Despite the recognized heterogeneity across AA/NHPI subgroups, these populations remain understudied,11 and data collection, reporting, and dissemination issues14 challenge the ability to understand health.15 Most federal databases are limited to simple distributions owing to small sample sizes for AA/NHPI groups.16 Furthermore, inconsistent implementation of racial/ethnic classifications and dearth of culturally appropriate instruments decrease our ability to understand exposures and health outcomes across subgroups.4The health of AA/NHPI groups is further complicated when data are stratified by sociodemographic characteristics. In 2017, approximately half of AA/NHPI individuals were foreign born and recent immigrants entering the United States in 2000 or later.11 found that AA/NHPI participants were represented in 0.2% of all health-related grants from 7 federal agencies. Similarly, an average investment of 0.4% in AA/NHPI communities was found in the top 20 major US foundations.17 Taken together, there have been minimal financial investments in AA/NHPI populations by federal agencies and philanthropy, even though AA/NHPI individuals represent more than 5.0% of the total US population.9Between 1986 and 2000, Ghosh14 The National Institutes of Health (NIH) Revitalization Act of 199319 and Minority Health and Health Disparities Research and Education Act of 200020 established the National Institute on Minority Health and Health Disparities. Likewise, the NIH Health Disparities Strategic Plan and Budget21 prioritized eliminating health disparities in racial/ethnic minority populations. The 1997 Office of Management and Budget Directive 1522 recognized AA/NHPI as 2 separate racial categories, and the Patient Protection and Affordable Care Act section 4302 expanded these data collection standards to include 7 AA and 4 NHPI subgroups.24 In 2009, Executive Order 1351525 reestablished the Office of the White House Initiative on Asian Americans and Pacific Islanders that highlighted more AA/NHPI-focused investigations.26During the past 2 decades, notable efforts by the federal government have emphasized reducing health disparities for AA/NHPI individuals.8 However, the impact of federal investments and legislation to ensure systematic processes and resources to eliminate health disparities in AA/NHPI groups is unclear. The purpose of this portfolio review is to examine the level of investment by the NIH that focused on clinical research in AA/NHPI populations.Advancing an inclusive national agenda for AA/NHPI populations has become an intersecting priority for federal agencies.27 is an electronic database of federally funded projects dating from 1985 to the present and includes project information and abstracts. We focused on NIH grant programs because the agency is the largest funder of health research globally.28 Approval of this study was waived by the Oregon State University institutional review board because it did not involve human participants.The NIH Research Portfolio Online Reporting Tools Expenditures and Results (RePORTER) system29 We defined unique projects as having the same grant number, which may include multiple years of funding. We excluded active projects because they have not completed the budget period or are projects with no-cost extensions. We included research project grants, centers, cooperative agreements, research career awards, training grants, and fellowships and performed an advanced text search for key words representing AA/NHPI countries or cultures of origin ;AA/NHPI plus non-AA/NHPI, which includes AA/NHPI participants and additional racial/ethnic groups .Total AA/NHPI is the sum of the 2 categories. We excluded projects that did not fit the clinical research definition, did not explicitly include AA/NHPI participants, took place internationally, or had no project abstracts.30 which uses text data mining and NIH-defined terms to catalog projects into research areas. We coded whether projects were trans-NIH efforts  or funded under multiple funding opportunity announcements (FOAs). The first author (L.N.Đ.) identified the clinical research studies based on search criteria that were discussed with the last author (V.L.I.). We followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) protocol31 for the search strategy, study selection, and data extraction from project abstracts. The last author (V.L.I.) was consulted for study eligibility and categorization when necessary. The first author (L.N.Đ.) coded whether the FOAs specifically referenced AA/NHPI participants or health disparities populations. Geographic regions were based on NIH RePORTER32 regional grouping and US Census Bureau definitions.We cataloged studies under specific criteria, including the primary target populations, AA/NHPI subgroups, health domain, and geographic region. Project health domains were coded using the NIH Research, Condition, and Disease Categorization (RCDC) system,11 We defined statistical significance as P < .05. Data were managed in Microsoft Excel (Microsoft Corp) and we conducted statistical analysis using R statistical software version 3.5.0 .Descriptive analyses included NIH dollar investments over time, characteristics of AA/NHPI clinical research, and features of the funded organizations. We examined the proportions of AA/NHPI-related research using 2 denominators, total NIH expenditures and clinical research expenditures. We calculated the annual changes in funding amounts and new projects awarded between 1992 and 2018 using simple linear regression. We also compared funding amounts and projects awarded before 2000 and after 2000, to compare with the study period used by Ghosh.34 Total NIH expenditures were defined as total research grants  plus total research training. Clinical research expenditures for fiscal years 2008 to 2018 were obtained from the Estimates for Funding for Various RCDC.30We obtained the total NIH expenditures from the NIH Office of Budget Mechanism Detail for Total NIH for fiscal years 1992 to 2018.We identified 5460 records on NIH RePORTER based on our search criteria . After sTotal AA/NHPI projects were funded across 17 NIH ICs . The topTotal AA/NHPI projects were funded under 262 unique FOAs, in which 49 projects (9.3%) were trans-NIH efforts and 17 projects (3.2%) were funded under more than 1 FOA. Approximately 22.5% of the FOAs explicitly referenced AA/NHPI populations under the funding opportunity description and 29.4% of the FOAs referenced health disparities population terms  but had a greater mean funding per project ($1 354 449) compared with AA/NHPI plus non-AA/NHPI projects. Looking at R01 grants, the AA/NHPI-only R01 budget ($214 141 949) was less than the AA/NHPI plus non-AA/NHPI R01 budget ($217 973 811), but the mean per project was more for AA/NHPI-only R01s ($2 185 122). Mean project funding was greater for AA/NHPI only for research project grants ($1 574 372), centers ($3 399 835), and fellowships ($115 365) compared with AA/NHPI plus non-AA/NHPI research project grants ($1 267 844), centers ($1 995 453), and fellowships ($91 359).There were 161 unique grantee organizations funded. More than 76% of total AA/NHPI funding was concentrated in 5 organization types, including domestic higher education ($187 192 280), schools of medicine ($182 857 757), research institutes ($110 419 596), organized research units ($57 352 298), and schools of arts and sciences ($51 397 258). The greatest number of projects were awarded to schools of medicine (n = 128), followed by research institutes (n = 73), schools of arts and sciences (n = 64), domestic higher education (n = 59), and schools of nursing (n = 48).By geographic region, 63.5% of the total AA/NHPI funding amount ($492 415 443) and more than half of the total AA/NHPI projects (n = 267) were awarded to organizations located in the Western region, followed by 16.6% of the total AA/NHPI funding ($128 958 357) awarded to 107 projects in the Eastern region. More than 62.6% of the total AA/NHPI projects were conducted in 5 states, California (n = 170), Hawaii (n = 47), New York (n = 44), Washington (n = 35), and Illinois (n = 35), with more than 50% allocated to California ($316 509 458).Almost 60% of the total AA/NHPI projects (n = 303) mentioned an AA/NHPI subgroup . More than 75.0% of AA/NHPI-only projects (n = 200) specified an AA/NHPI subgroup compared with 38.0% of AA/NHPI plus non-AA/NHPI projects (n = 103). Among AA/NHPI-only projects, the subgroups most represented were Chinese (n = 71), Korean (n = 42), Vietnamese (n = 37), Filipino (n = 21), and Japanese (n = 19) for AA participants, and Native Hawaiian (n = 18), Samoan (n = 7), Marshallese (n = 4), Chamorro (n = 3), and Tongan (n = 3) for NHPI participants. For AA/NHPI plus non-AA/NHPI projects, Chinese (n = 62), Filipino (n = 20), Japanese (n = 18), Native Hawaiian (n = 15), and Vietnamese (n = 11) populations were referenced most often.P < .001), but the proportion of the total NIH budget has only increased from 0.12% before 2000 to 0.18% after 2000, with no difference in funding before vs after 2000 (eTable 3 in the P = .02), while funding amounts for AA/NHPI plus non-AA/NHPI significantly decreased after 2000   (eTable 5 in the P = .02) and AA/NHPI plus non-AA/NHPI  groups.11 that found AA/NHPI individuals represented 0.2% of the total health-related federal expenditures. Since the Ghosh study,11 the number and amount of AA/NHPI-only grants increased, but they still accounted for only one-fifth of 1% of NIH’s clinical research budget. The funding allocated to total AA/NHPI research remained less than one half of a percent for both the overall NIH budget and clinical research budget. Furthermore, there was a lack of diversity in the investment in terms of ICs and geographical regions. More than half of the research funding was concentrated in 3 ICs, consistent with the leading causes of death for AA/NHPI individuals in the United States.35 Funding was concentrated in California and Hawaii, corresponding to the top states of residence for AA/NHPI individuals.36 Similar to prior reports,37 negligible investment was seen toward states with the fastest-growing AA/NHPI populations. The current investment patterns, or lack thereof, could result in worsening health disparities in AA/NHPI populations because of prolonged disparate funding of health research.Despite federal legislation and initiatives prioritizing data disaggregation and advancing health disparities research, we found that 0.17% of the overall NIH budget was allocated to 529 AA/NHPI-related clinical research projects over 2 decades. Our findings match the study by Ghosh38 Enrollment for AA participants in extramural NIH-defined phase 3 clinical trials increased from 2.5% (n = 7451) in 2011 to 12.1% (n = 19 172) in 2016, while NHPI participants decreased from 0.3% (n = 1011) to 0.2% (n = 271) during the same period.38 This increase in clinical trial participation by AA participants did not translate to increased data disaggregation. We found that AA/NHPI populations continue to be classified as a homogeneous group and there was unequal representation of AA subgroups. Dishearteningly, the decline in NHPI participants enrolled in NIH clinical trials matched our findings. Native Hawaiian and Pacific Islander participants were almost absent from the grants found in our search.Minimal increases in inclusion of racial/ethnic minority participants across all NIH research have been reported.39 Immigration- and acculturation-related factors influence English language proficiency, health and digital literacy, and preferences of clinical care and treatment.39 The magnitude of these factors varies across and within AA/NHPI subgroups, and differentially affects the generalizability of clinical research as well as the significance of downstream clinical applications. Disaggregated analyses have the potential to inform clinical and public health programs if significant differences are found between groups. Understanding cultural difference within AA/NHPI populations could emend the cycle of poor patient-clinician communication and patient health outcomes.39Treating AA/NHPI populations as homogeneous assumes cultural beliefs and experiences are the same, which could potentially influence and prolong misleading clinician stereotypes and unconscious biases about patients.Findings from our study have implications for future administrative and programmatic efforts, namely (1) intentional use of FOAs, (2) workforce diversity, (3) data disaggregation with data harmonization, and (4) access to data.One reason for poor investment by NIH in AA/NHPI research could be that investigators are not submitting grant applications focusing on these populations. Our research could not asses the number of applications submitted that included AA/NHPI populations. However, we reviewed FOAs and we could differentiate funded grants that were in response to a specific FOA. Approximately half of grants in this analysis (393 of 529) were linked to a FOA. However, less than a quarter of FOAs specifically referenced AA/NHPI populations and only one-third of the FOAs referenced health disparities populations. Our finding suggests that FOAs need to be more intentional in referencing health disparate populations.41 For example, Ginther et al42 reported that Asian investigators were less likely to receive an R01 award on the first or second submission compared with their white counterparts. Enhancing investments in the research pipeline at the early career stages could improve the biomedical workforce diversity and could improve the number and success of funded research studies. In addition, improved workforce diversity could increase participation of AA/NHPI individuals in health research.43 Minority populations are more likely to understand and participate in research if they are able to identify or feel a connection with researchers.48The low rate of funded research among AA/NHPI populations could be because of the low prevalence of AA/NHPI researchers. Our findings could not evaluate the race/ethnicity of principal investigators. However, only 5% of the total AA/NHPI funding was awarded to research career awards, training grants, and fellowships, but more than three-fifths of that amount was allocated to research project grants. Systematic challenges for minority investigators include mentoring differences, securing funding, and achieving academic tenure.49 was modeled after the Multi-Ethnic Study of Atherosclerosis (MESA).50 Because MASALA and MESA had matching data collection protocols and collected disaggregated racial/ethnic data, the prevalence of diabetes is now comparable between specific subgroups of Asian individuals, Latino individuals, and white individuals.50In our study, more AA/NHPI-only projects specified AA/NHPI subgroups compared with AA/NHPI plus non-AA/NHPI projects. This finding suggests that AA/NHPI-only projects may be prioritizing culturally appropriate inclusion and recruitment strategies as well as disaggregated data, while AA/NHPI plus non-AA/NHPI projects may focus more on studying a disease in general. More focused AA/NHPI research could improve the status of disaggregated data and accuracy of AA/NHPI health profiles, particularly if there is better harmonization of questionnaires and data collection protocols. For example, the Mediators of Atherosclerosis in South Asians Living in America (MASALA) studyFederal agencies must provide public-use data files and reports by disaggregated race/ethnicity to improve transparency and evaluation of research priorities. Eliminating health disparities requires that there is sufficient AA/NHPI data to understand what is happening and to intervene in a meaningful way. Future studies should evaluate research rigor of funded projects, such as the proposed enrollment of racial/ethnic minority participants as comparison with enrollment reported in publications. Funding trends should be assessed to document whether they have kept pace with population growth and whether they are addressing and anticipating health disparities. Other federal agencies and philanthropic organizations should document their funding for AA/NHPI groups.This study has limitations. First, this study summarized extramural funding awarded through the NIH and does not include other federal agencies or philanthropy. Search queries focused on research project grants, training, and fellowships as funding mechanisms because they focus on investigator-initiated research. The applicability of our review is limited to the project information available on NIH RePORTER, so relevant projects may have been excluded during the screening and eligibility protocol.Healthy People 2020 health disparities areas that report AA/NHPI data. However, it is ambiguous how much investment and how many projects are needed to reach the Healthy People 2020 goals, or when an arbitrary level of improved health has been achieved.We do not know what proportion of NIH funding should be spent on racial/ethnic minority populations or what the research priorities should be. We assume that research funding in racial/ethnic minorities will lead to improved health outcomes because more disaggregated health data will improve our understanding of whether health disparities exist and result in evidence-based interventions for at-risk and high-risk AA/NHPI subgroups. Total AA/NHPI projects covered 10 of 11 In addition, information on the inclusion of racial/ethnic subgroups in clinical research are not publicly available. Minority health and health disparities funding data are available on the RCDC summary table, but do not follow the standardized RCDC process for annual estimates. The clinical research budget line is only available starting in 2008. The proportions may be underestimated for the total NIH expenditures  and overestimated for clinical research expenditures . We also reported comparisons between total funding amounts and project means for AA/NHPI only and AA/NHPI plus non-AA/NHPI; however, the more meaningful comparisons may be comparisons with NIH total funding amounts by grant mechanism and mean funding per NIH project, respectively.We found disproportionate long-term investments from the NIH to eliminating health disparities in AA/NHPI populations. Inclusion for these underrepresented populations in the federal agenda and disaggregated data allows for more useful data to reveal the health status of AA/NHPI subgroups and helps to dispel the model minority stereotype. Overt direction from federal entities, dedicated federal funds for health disparities research, and parallel efforts to increase diversity in the biomedical workforce will be critical to advance health equity. This portfolio review can be used to address underlying systematic barriers and inform future health disparity research opportunities.


Proteomic biomarker search requires the greatest analytical reproducibility and detailed information on altered proteoforms. Our protein pre-fractionation applies orthogonal native chromatography and conserves important features of protein variants such as native molecular weight, charge and major glycans. Moreover, we maximized reproducibility of sample pre-fractionation and preparation before mass spectrometry by parallelization and automation. In blood plasma and cerebrospinal fluid (CSF), most proteins, including candidate biomarkers, distribute into a multitude of chromatographic clusters. Plasma albumin, for example, divides into 15-17 clusters. As an example of our technique, we analyzed these albumin clusters from healthy volunteers and from dogs and identified cluster-typical modification patterns. Renal disease further modifies these patterns. In human CSF, we found only a subset of proteoforms with fewer modifications than in plasma. We infer from this example that our method can be used to identify and characterize distinct proteoforms and, optionally, enrich them, thereby yielding the characteristics of proteoform-selective biomarkers. It is also difficult to obtain longitudinal samples and perform multidisciplinary studies1. However, another significant cause is the biological variability of protein expression and modification8. Protein modifications are variable among healthy individuals and diseases further alter the diversity of multiple proteoforms8 producing dysfunctional or even toxic entities10. Therefore, it is illogical to expect that it is possible to produce a reliably usable biomarker from an entire protein family deduced from a few tryptic peptides12. This role is more reasonably fulfilled by one or multiple distinct proteoforms.A major challenge in the search for proteomic biomarkers is the low reproducibility among studies. There are several likely reasons for this low reproducibility most of which originate from experimental design or methodology13. More suitable for identifying disease specific modifications are comprehensive, unbiased, and precise sample pre-fractionation methods on the protein level12, combined with sophisticated data analyses.To identify and quantify specific proteoform biomarkers among the many and varied modifications possible, new experimental ideas and settings are required because the common shotgun and bottom-up approaches are insufficient for this purpose14. Moreover, the workflow inherently produces highly characteristic sub-fractions. This thereby preserves information on the native molecular weight , and fragmentation), charge (- PTM and conformation), and lectin affinity (- major types of glycans). The immune reactivity and biological activities of the proteoforms can also be analyzed in our native sub-fractions when required15. Such information is beneficial for identifying specific proteoform biomarkers as well as for their validation.We therefore developed an unbiased protein pre-fractionation workflow using non-denaturing multidimensional chromatography. We adapted the workflow for the proteomic analysis of body fluids without any depletion. This workflow therefore allows the characterization of the entire proteome. The combination of parallel liquid handling, microplate format, and automation ensures maximum velocity, precision and reproducibility17. It is necessary to determine the probable reasons for this heterogeneity in order to identify biomarkers that are highly specific for particular diseases. A principal reason is that different proteoforms are characterized by different splice variants and PTM. In addition, however, it is necessary to consider the role of fragmentation and of complex formation with proteins and metabolites. Since albumin is the dominant plasma protein and is not known to be glycosylated or otherwise functionally modified but might be altered in the body by various processes18, we used it as an example for characterizing the capabilities of our workflow. To provide proof of principle, we searched albumin heterogeneity for various PTM.As expected, we showed that the majority of plasma proteins appear in multiple chromatographic clusters16. For all fractionation processes, hit picking and analytics were controlled by adapted software packages. We developed several tools to maximize compatibility, throughput, recovery and precision of the preparation steps. We based protein recoveries from the corresponding load into liquid sub-fractions on UV measurements.Our method orthogonally combined size exclusion chromatography , followed by anion exchange  and, optionally, lectin affinity  chromatography Fig.  enabled Our first step, 1D-SEC, was characterized by the greatest reproducibility for reference peaks of UV-absorbing material . For this method, only liquid handling was required, so guaranteeing high through-put, automation, and high precision. With the tool in Fig. 21 . To perform medium exchange, we selected parallelized microdialysis and developed various tools . Elution solvents produced a stepwise gradient with increasing ionic strength14 . For example, from pooled human CSF, we were able to identify 5185 non-redundant proteins supported by ≥1 peptides and 1691 by ≥2 peptides. We also applied our method to the search for biomarkers of severe inflammation or sepsis, renal failure, Alport syndrome28, and psoriasis16. In these searches, we obtained several candidate markers with significantly altered protein concentrations inferred from MS data. We validated several of these using immunoassays.Applications of our methods showed versatility and high protein yield of plasma proteins from humans, cattle, goats, dogs and mice, and from human CSF , to uncover several interesting PTM for a variety of clusters of plasma proteins (patent sensitive data not shown). Below, we discuss our results for albumin, using it as an example of a typical eukaryotic protein.It must be noted that we consistently identified not only one but several chromatographic clusters for the majority of altered proteins, some of which demonstrated opposite directions of alteration. Sometimes we were unable to verify the main alteration of such biomarker candidates by commercial ELISA27, just as many other plasma proteins are. After 1D-SEC, immune reactive albumin reproducibly separated into three distinct peaks with apparent molecular weights of ~90–95 kDa, ~69–73 kDa, and ~50 kDa , N-glycation , palmitoylation  and palmitoleylation , myristoylation (K), octanoylation , decanoylation , ethylation , acetylation , methylation , dimethylation , propionylation , succinylations (K), carboxylation , carbamoylation (K), carboxymethylation (K), carboxyethylation (K), and phosphorylations  and guanidylations (K).To compare the results of different sub-fractions, the number of modified sites was normalized by the total number of identified peptide spectra matched for albumin (PSM) of the same search run. This allowed for a semi-quantitative comparison based on the degree of modification. As expected, the chromatographic clusters and the pools they represented were not uniformly modified. In healthy controls, each cluster possessed its own characteristic pattern of modification .The precision of sample pre-fractionation and proteome coverage of our workflow allows reliable sample comparison for biomarker search. We were therefore able to validate several biomarker candidates by ELISA30. Nevertheless, information on the extent and distribution of other modifications is rare. There are, though, two major and seven minor fractions identified and partially characterized by IEX/MS starting from Cohn fraction IV31. Our native 2D-fractionation method applied to whole blood plasma shows, however, that the complexity is, in fact, much higher. Serum albumin subdivides into at least 17 different parts all of which are modified in multiple ways. Most of the modifications, with the exception of deamidation, glycation and methylation, affect less than one percent of all the peptides identified (PSM). Nevertheless, their cumulative effect on charge and conformation might produce the characteristic sub-fractions observed. Carbamoylation (K), deamidation , and glycation (K) lead to the loss of positive charges32, produce higher negative net charges, and correlate well with the ionic strength necessary for elution from our AEC matrix. In contrast, we found an inverse relationship for carboxymethylation and carboxyethylation (K), although these modifications introduce additional negative charges  probably due to the instability of this bond under DDT reduction before MS37. Albumin acylation still requires verification by other means.The serum albumin of healthy individuals and patients contains several major modifications attributed to micro heterogeneity ges Fig. . Carboxy41 as well as its increase  under renal failure. This modification competes with glycation and is raised under diabetes, chronic kidney diseases, and inflammation42. Carbamoylation might occur artificially if urea is present in high concentrations (e.g. for denaturation)43. This is not true for our method and therefore such an artefact is highly improbable.In addition, we re-identified a low degree of carbamoylation in healthy individuals in accordance with a previous studyin vivo and in vitro. Non-enzymatic deamidation of Q and N is considered to be both pathological and physiological, increasing protein negative charge, altering conformation, stability, longevity, and function44. Although Q is more resistant to deamidation44 than N , we identified Q and N deamidations in similar proportions in our albumin fractions . Deamidation (N + Q) shows a strong correlation with AEC elution  is a common and wide spread reaction46 particularly as part of the antioxidant activity of albumin48 and under oxidative stress in renal diseases49, we omitted these data, because we are unable to exclude the possibility of some oxidation during sample preparation. In contrast, glycation is unlikely to result from ex vivo reactions50, since this non-enzymatic reaction exhibits low binding rate constants and requires high glucose concentration and temperature51. This reaction is unlikely in our study as all our samples were frozen quickly in liquid nitrogen. Moreover, after 1D-separation (SEC) the proteins were freed from non-bound sugars. Thus the degree of glycation appears to reflect natural modification.Deamidation is a common modification of several proteins occurring spontaneously, both ion Fig. . We cannd N Fig. . Deamida53. Moreover, the total amount of albumin determined by ELISA in patients was shifted slightly from little modified to greatly modified entities compared to amounts in healthy people . Moreover, the distribution into identical sub-fractions from patients and from healthy individuals occurs with different degrees of modification . Hence, the assignment of proteoforms might depend only in part on net charge but also on different conformations, probably due to a certain modification threshold, pattern or, even, an as yet unrecognized modification.The three apparently different molecular weights of plasma albumin could not be explained by PTM, since MS data does not provide strict stoichiometry. Nevertheless, besides PTM it is also possible that apparently large size proteoforms result from complex formation with members of the “albuminome”59. Applying our workflow, we were only able to identify only 4 clusters with different degrees of PTM. The degree of carbamoylation . Moreover, our precise micro-separation can be used for the native preparation of sufficient amounts of selected minor constituents directly from pre-analyzed sub-fractions. This serves as a prerequisite for their final enrichment and subsequent more reliable characterization by targeted MS or other analytical techniques so as to provide true biomarker candidates and antigens for the development of highly PTM selective immunological assays.In contrast to Multiomics by SIMPLEX, the simultaneous profiling of proteins, lipids and other metabolites in initially denatured samples16. Samples from patients were identically obtained immediately before hemodialysis treatment. Dog samples were collected from a colony of dogs with X-linked hereditary nephropathy. These dogs have a naturally occurring, 10 base pair deletion in the gene encoding the α5 chain of type IV collagen. Samples were collected from 7-week old unaffected male and female, carrier female, and affected male siblings by jugular venipuncture. Blood was placed into a glass EDTA tube which was immediately placed on ice and centrifuged within 30 minutes of collection at 1500 g * 10 min at 4 °C. After centrifugation, the tubes were placed on ice, and plasma was removed, avoiding the buffy coat interface. The plasma was maintained on ice until frozen at −80 °C within 2 hours of initial blood collection. The samples were stored at −80 °C for two to three weeks before being shipped on dry ice and transferred to long-term liquid nitrogen storage. No freeze-thaw cycle was allowed. Human samples were collected in accordance with ethics agreement 4829–06/2016 (JUH Jena). The informed consent of all participants was obtained. Dog samples were collected in accordance with ethics agreements AUP IACUC 2013–0028 and 2016_0017 (TAMU Texas). The study protocol was reviewed and approved by the Texas A&M University Institutional Animal Care and Use Committee.We included 4 randomly selected healthy human volunteers  and 4 patients (54.5 SD 2.3) with end stage renal failure but without diabetes mellitus. Both groups contained 2 males and 2 females. Nine milliliters of blood were collected by venipuncture into EDTA containers  and centrifuged at 1500 g * 10 min at room temperature no longer than 30 min after withdrawal™ Ultra, MWCO 3 kDa, Merck Millipore), followed by 2D fractionation as described below.We used CSF sample from 26 patients with neurological symptoms. CSF samples were collected in accordance to the ethical agreement 3633-11/2012 (JUH Jena). From all participants 12 mL of CSF were collected in polypropylene tubes (Greiner Bio-One 115261) following standard protocol . The samples were kept on ice and immediately centrifuged at the bed side for 10 min. at room temperature (3500 g). Supernatants were aliquoted into cryovials , shock frozen in liquid nitrogen and stored at −150 °C. The CSF samples were characterized using key laboratory parameters. For MS analysis we used only samples without either blood contamination or blood-brain barrier disturbance. We assessed blood contamination by visual inspection for erythrocyte sediment and barrier disturbance by checking for erythrocytes, total protein, cell count and CSF:serum albumin ratio). To ensure adequate amounts of protein for analysis we created 3 mixtures. Each mixture was made up of equal volumes of CSF samples from 7–10 individuals. These pools were concentrated ~70–95-fold by ultrafiltration  and an automated workstation ) were separated by 2D AEC.Pre-fractionation of blood samples was performed according to an existing detailed method for human plasma and serumled Fig. . To obta280 and hemoglobin by the cyanhemiglobin reference method.BSA was quantified by A34) 100 µL sample volume, in microplates .Gemini XPS Microplate Reader, Molecular Device  using an Accela 1250 UHPLC system  using binary gradient elution of the mobile phase. This gradient involved 0.1% formic acid in (A) water and in (B) acetonitrile at a flow rate of 150 µL min-1, throughout . Tandem mass spectrometry (MS/MS) measurements were carried out on an LTQ Orbitrap Discovery  by positive heated electrospray ionization (H-ESI) at a vaporizer temperature of 200 °C. Sheath gas flow (30.0) and auxiliary gas flow (10.0) were used to dry the ionspray. The ionization voltage was set to 4.5 kV and the temperature of the ion transfer tube to 275 °C. The MS/MS system was operated in data-dependent TOP10 mode using 1 microscan. For this purpose, ions were monitored in the LTQ ion trap in full scan centroid mode at m/z 350–1700. The ten most intense ions were run through collision-induced dissociation for further orbitrap high resolution  analysis (profile data type). Wideband activation was used. The automatic gain control target value for the Orbitrap mass analyzer in full scan mode was 1.0 × 106. The LC–MS/MS was operated via the graphical interface of the Xcalibur software 2.1. Fractions from blood plasma and CSF were run in duplicate and triplicate followed by a blank (water) to avoid carryover. For quality control, a digested transferrin (human (Holo), SERVA Electrophoresis GmbH, 36756, 50 µg mL-1) was analysed four times per microplate.We performed tryptic digest and MS as previously described© 1.3 (Thermo) with no more than three dynamic modification types per run. We considered only highly confident database matches. The following dynamic modifications of amino acids were analyzed: oxidation (M), palmitoyl , palmitoleyl , myristoyl (K), octanoyl , decanoyl , deamidation , carboxy , carboxymethyl , carboxyethyl (K), carbamoyl , hexose , acetyl , ethyl , dimethyl , propionyl , methyl , phosphoryl , and succinyl (K). Multi reports of the related replicates were utilized.We considered identical chromatographic 2D-sub-fractions  to contain identical proteoforms of albumin due to their identical physicochemical properties. Because of this, we were able to combine, when equally pre-fractionated, counts of the PTM of proteins of identical 1D- and 2D-location but from different runs. We built up to 17 pools from 2D-subfractions of each plasma sample containing the major parts of visible albumin clusters. Due to slight differences in pooling of 2D-sub-fractions from healthy individuals, we processed the data sets from the 2 females separately from those of the two males. Although the samples were processed apart, we combined the data from all 4 patients under hemodialysis  by taking the mean values. These values we then compared with the identically obtained data from the 2 healthy male individuals. CSF and dog plasma albumin were analyzed in MS data of 2D-fractions of known major albumin content without pooling thereby reanalyzing the MS raw data from human CSF and dogs (still unpublished data) from other studies. The search for PTM ran successively using Proteome Discoverer26. Briefly, 25 µL of the samples and the standards were incubated for 90 min. at room temperature in the wells of 384-well microplates  pre-coated with anti-human albumin  overnight at 4 °C). After washing  the wells were incubated with 25 µL goat anti-human albumin-HRP conjugate  for 2 h. After washing, 25 µL of substrate were added  and the reaction was stopped by 25 µL 0.5 M H2SO4 after ~20 min and visual inspection. Due to the limited sample volumes and protein concentrations, it was only possible to analyze the 8 most highly concentrated 2D-pools  by PTM-selective ELISA according to the instruction of the suppliers. We tested all selected 2D-pools in duplicate with deviations of no more than 10%. Carbamoylation: Human Carbamylated Albumin ELISA (XPEH0930) supplied by XpressBio; 1 to 5 and 1 to 10 diluted 2D-pools. Pentosidine: Human Pentosidine ELISA-Kit (CSB-E09415h) from Cusabio® applying 1 to 5 diluted 2D-pools. Glycation: Glycated human albumin competitive ELISA-Kit (CSB-E09599h) from Cusabio®; slightly modified: we pre-mixed 2D-pools (diluted 1 to 3) and conjugate solutions before spotting to the wells.Albumin was measured using a slight modification of an existing methodSupplementary Figures and Table


The unique demographic and dietary characteristics of modern Arabic population require development of a new predictive equation for the estimation of resting energy expenditure (REE). This study presented new equations characteristic to Saudi population.  A set of predictive equations for REE was derived for 427 healthy male and female subjects (aged 18–57 ± 14 years). REE was measured (REEm) by indirect calorimetry (IC) and predicted (REEp) using nine equations. REEp was compared with REEm to determine the predictive accuracy of these equations. Using IC and anthropometrics for stepwise linear regression analysis, a new set of equations to predict REE of men and women was developed. Accuracy of the new main equations was further tested in an external sample of 48 subjects (men = 50%). R2 = 0.702).  Using a number of parameters , our results suggested that almost all (9/9 in men and 7/9 in women) equations either underpredicted or overpredicted (2/9) REE. None of the already existing equations showed an acceptable REEp/REEm difference as low as 5% and an accurate prediction (∼55%) at the individual level. Based on these findings, a new prediction equation  equation) was developed using this study's data, after a rigorous stepwise regression analysis using the following formula: REE = 3832.955 + AdjWt (kg) × 48.037 − Ht (cm) × 30.642 + gender × 141.268 − age (years) × 4.525 . The regression model accounted for approximately 70% of the variance in REEm ( Previous equations likely over- or underpredicted REE. Therefore, the new predictive AA equations developed in this study are recommended for the estimation of REE in young to middle-aged Saudi men and women with different body mass indexes. Future research is also required for further clinical and cross-validation of these new equations. However, due to high cost of equipment and operation involved, certain equations have been usually in use to estimate energy expenditure [Resting energy expenditure (REE) is the main component of total energy expenditure, taking into consideration the amount of energy the living body requires to maintain its dynamic functions . Indirecenditure .Numerous equations for the prediction of REE have been recommended for general use , includiIn addition, the abovementioned predictive equations are considered unsuitable for predicting REE in certain populations with different body mass indexes , 16. TheIt is worth mentioning that none of the formulae developed to date have taken into account these population differences. Based on studies that report the possible effects of race/ethnicity , 23 and The study subjects were recruited from both male and female sections at the Clinical Nutrition Program, Department of Community Health Science (CHS), Main Plaza of the King Saud University, and the Medical City. Participants (aged 18–57 years) were evaluated between December 2015 and June 2017 in a cross-sectional basis. Subjects with diabetes, thyroid disorders, or any other disease that could possibly affect REE were excluded. An additional inclusion requirement was reportedly good health defined as <1 sick day/month for the past year and no major existing psychological problems or any other illness . RecruitParticipants attended an orientation session before the start of the study. All participants completed consent forms and demographics and health-related questionnaires. The participants were evaluated for REE using IC, body composition using bioelectrical impedance analysis (BIA), and anthropometrics to design the REE prediction equation. REE was also estimated using the Harris–Benedict, Ireton–Jones, Carrasco, Mifflin St. Jeor, Kleiber, and Owen equations. Since we were primarily interested in predicting REE across a wide range of body weight, equations developed from samples of exclusively overweight men and women were excluded. Although additional formulae that predict REE based on body composition  have previously been developed, the current study was restricted to equations consisting of readily available body measures , as these are clinically more useful and easy to perform.Demographics, smoking status, and reproductive health history were assessed via self-report . For theDetails of anthropometric measurements can be found elsewhere . In brie2); FFM index was calculated in the same manner (FFMI = FFM (kg)/Height (m2)) [Fat mass (FM) and fat-free mass (FFM) were obtained using TANITA BC-418 analyzer . As per the manufacturers' specifications, the machine emits an electric current with 50 and 500 kHz in frequency. This multifrequency bioelectric impedance analysis measures components of body impedance, reactance, and resistance that are used to accurately calculate body water, FM, and FFM. Fat mass index (FMI) was calculated by dividing FM (kg) by height (mht (m2)) .To determine REE, IC was performed on all patients using the QUARK RMR . The laboratory space was maintained at 20–25°C, ensuring that each individual was physically comfortable and thus properly positioned for measurements. All participants were asked to fast at least for 12 h and also to abstain from caffeine, nicotine, and physical activity (minimum abstention from vigorous resistance exercise for 24 h). Before the test, a 20 min rest was allocated for device warming and gas, air, and turbine calibration of Quark RMR. Practically speaking, REE is equivalent to RMR.2 and VCO2 of ≤10%) were excluded from the analysis. At any time, it was not accepted to have the % coefficient of variation in gas volumes >10%.Energy expenditure was measured for all participants, which took 16 min for each individual after excluding the first 5 min preparation/stability time. Laboratory visits were scheduled at the same time of day (between 08 : 00 and 11 : 00 am). All the assessments were completed on an outpatient basis, with participants arriving at the laboratory on the morning of testing sessions; silence was observed during session, and subjects comfortably lied in the supine position without moving or sleeping. Sessions that failed to achieve at least 5 min of steady state (variations in the VOn < 50); impractical or suspected body composition as a variable, glucose concentrations, or diabetes as a variable, total energy expenditure, athletes, and duplicate publications.REE was also calculated using equations that included body weight, height, age, sex, FFM, and/or FM. The exclusion criteria for these equations were as follows: equation using age range of <12 yrs or only elderly; those using only one sex, those having normal weight based on Cole et al.   were used for external validation of the main new equations. The validation population was recruited from visitors of therapeutic nutrition clinic at CAMS, KSU. The same inclusion criteria were applied, and the same measures were recorded as the original study population.R2) and residual standard deviation (RD) as goodness-of-fit measures between the predicted and measured regression equations used.All the data were analyzed using SPSS . Continuous variables were expressed as means (SD), and dichotomous variables were expressed as percentages and frequencies. The accuracy of predictive formulae at the individual level was defined as percentage of the subjects whose predicted RMR was within ±10% of the measured RMR , 33. The2. All anthropometric and body composition parameters significantly differed between men and women and between the three BMI groups, except for height .Among 510 participants, the inclusion criteria were eligible for 423 participants (men 49%) .  Table We evaluated differences in the following measures: bias (difference between REEp and REEm) , percent bias (REEm − REEp by equations multiplied 100/REEp by equations), underestimation , overestimation , and accurate prediction .1919 , HB1984 , Schofield  , Schofield (Wt) , WHO (Wt) , and WHO  . The number of underpredictions within ±10% range varied from 90.4% (Bernstein equation) to 20.2% (WHOW equation) in men and from 82.3% (Bernstein equation) to 17.2% (each SchofieldW and WHOW equations) in women. The number of overpredictions within ±10% range varied from 23.6% (WHOW equation) to 0% (Bernstein equation) in men and from 27.4% (WHOW equation) to 0.5% (Bernstein equations) in women.WH, WHOW, SchofieldWH, and SchofieldW equations, respectively, having % accurate prediction of 56.3, 56.3, 56.3, and 55.8, followed by the equations of H-B1919 (55.3%), H-B1984 (54.8%), Mifflin (44.2%), Owen (37.0%), and Bernstein (9.6%). In women, out of 10 equations, two  overestimated REE (mean REEp > REEm) although the difference between the means was not significant (P > 0.05). The remaining eight equations underestimated REE (REEp < REEm) with a statistically significant difference . Contrary to men, equations of H-B1984 for female participants had the highest accurate prediction (62.3%), followed by H-B1919 (61.4%), WHOWH (58.6%), SchofieldWH, (58.6%), SchofieldW (55.8%), Mifflin (52.6%), Owen (37.7%), and Bernstein (17.2%). All the prediction equations used in the current study significantly underestimated the measured REE  in men  were WHO) in men .Figures r = −0.230, P=0.01), height , weight , and gender . P, for all trends <0.01). Of no surprise, the best REE predictor was body weight (r = 0.730). Of the remaining variables, height demonstrated the next strongest relationship to REE , followed by gender  and age . The results of the multiple regression analysis produced the equations in predicting REE , as shown in the multivariate analysis of all study population, and the variables included in the equations significantly and independently contributed to the model (P=0.001). Stepwise entry of anthropometric variables revealed that adjusted weight, height, gender, and age increases R2 of this model from 0.620 to 0.702, as shown in R2 to 0.706, but for clinical ease and slight increase in R2, the much simpler equation 1 was considered. After dividing the sample into normal weight and overweight/obese categories, another set of equations were created but R2 < 0.7 , Owen , and Bernstein equations . The Bland–Altman plot  to 9.6% (Bernstein) in men and from 62.3% (HB1984) to 17.2% (Bernstein) in women. The bias for predictive equations used in this study varied from 33.5% (Bernstein) to 4.3% (H-B1919) in men and from 24.7% (Bernstein) to 0.4% (SchofieldW) in women (W and SchofieldW) had bias of <1%  had relatively high % accurate prediction (>50 and <60%) but with >1% bias and can be used for REE estimation at the population levels in Saudi men. Similarly, in women, our data  had relatively high percentage accurate prediction (>50 < 60%), but only two equations (WHOW and SchofieldW) had bias of <1% and can be used for REE estimation at the population levels for Saudi women. However, none of the 10 equations used for comparison in the current study can be reliably used to assess individual differences, as they are likely to provide inaccurate results in clinical settings.The percentage accurate predictions varied between equations, from 56.3% (for each WHOin women . These rs of <1% . Our fins of <1%  showed tour data  showed tW (Wt) and SchofieldWT) overpredicted REE in women; (2) predictive equations that underpredicted REE in men mostly overpredicted those in women; (3) predictive equations overpredicted REE at the higher REE level  and underpredicted REE mostly at lower REE levels (as in women), an observation in contrast to that by Müller et al. [W and SchofieldW) showed an acceptable REEp/REEm difference of <1% and an accurate prediction (∼55%) at the individual level [2).Based on these data, four main lines of arguments can be drawn: (1) all nine predictive equations used in this study underpredicted REE in men, while most (eight out of nine) predictive equations underestimated REE for women, but two , only WHOW had a percentage bias of <1.0. Therefore, WHO equations (both WHOWT and WHOW) may not be suitable for, at least, Saudi men. As also argued by De Oliveira et al. [W is the most suitable equation for normal weight individuals [2 and aged 16–65 years [The WHO equation was developed on a study of young Europeans, mostly police and military recruits, with a high proportion 45%) being of the Italian descent . In our a et al.  and Owenividuals , and witividuals ; howeverividuals –43, therividuals . Mifflinividuals ; despite65 years . However65 years ; however5% being 65 years .This study has a number of strengths. The sample size  was large enough for subgroup (men and women) analyses. Furthermore, the data were derived from otherwise healthy individuals , and therefore, the study population may be representative for a wider BMI subjects and hence has large generalizability. The prediction equation we developed in this study is the first, to our knowledge, that is specifically adjusted for Arab ethnicity. This study included women representing a wide range of body weight, a fact which may increase the generalizability of the newly developed equation to other samples. Finally, we carefully controlled for the effects of several potentially important confounders, such as menopausal status, menstrual cycle, pregnancy/lactation, other relevant medical conditions, and the possible thermogenic effects of food and nicotine. However, there are several limitations in this study as well. First, the investigation did not include subjects with BMI <18.5 (underweight), which is still relatively a substantial fraction of the total population and their underrepresentation may cause a decrease in the generalizability of the new equation. Second, the sample was limited to men and women aged 18–57 years; thus, the predictive value of the new equation in estimating REE among older men and women may be uncertain. Future investigations should focus on the impact of ethnicity on the accuracy of prediction equations that have been developed for older Saudi men and women, e.g., study by Arciero et al. . For thi2, and HB1918 equation is advised for obese individuals (BMI of >30 kg/m2). Measuring REE with indirect calorimetry is the preferred option and should be used when the facility is available as well as feasible for the optimization of nutritional support in hospital in- and outpatients with different degrees of malnutrition. In case of nonavailability/unfeasibility of the facility, we recommend use of a population-specific predictive equation. Future validation of other equations developed in this study is required using adequate samples.In conclusion, we found in the present study that REE predictive equations are only accurate in approximately half the individuals. The WHO equation is advised for use up to BMI 30 kg/m