Background: Head and neck squamous cell carcinoma (HNSCC) represents a common cancer worldwide. Past therapeutic advances have not significantly improved HNSCC prognosis. Therefore, it is necessary to further stratify HNSCC, especially with recent advances in tumor immunology.Methods: Tissue microarrays were assembled from tumor tissue samples and were complemented with comprehensive clinicopathological data of n = 419 patients. H&E whole slides from resection specimen (n = 289) were categorized according to their immune cell infiltrate as “hot,” “cold,” or “excluded.”Results: Investigating tumor immune cell patterns, we found significant differences in survival rates. Immunologic “hot” and “excluded” HNSCCs are associated with better overall survival than “cold” HNSCC patients (p < 0.05). Interestingly, the percentage of all three patterns is nearly identical in p16 positive and negative HNSCCs.Conclusions: Using a plain histological H&E approach to categorize HNSCC as being immunologic “hot,” “cold,” or “excluded” can offer a forecast of patients' prognosis and may thus aid as a potential prognostic tool in routine pathology reports. This “hot-cold-excluded” scheme needs to be applied to more HNSCC cohorts and possibly to other cancer types to determine prognostic meaning, e.g., regarding OS or DFS. Furthermore, our cohort reflects epidemiological data in the national, European, and international context. It may, therefore, be of use for future HNSCC characterization. HNSCCs are the 6th most common cancer in humans , 2. TodaGiven these therapy-induced impairments, the prognosis is still rather poor. With an increasing tumor stage, there is a decreasing survival. For UICC stage III and IV, the 2-year survival is around 30%. Thirty to fifty percentage develop a recurrent disease (RD) which is mirrored in poor disease-free survival (DFS) , 4–6. ChEspecially with growing knowledge about tumor immune microenvironment (TIME), as described later in this introduction, another promising therapy option was the treatment with immune checkpoint inhibitors (ICI), with PD-L1 and PD-1 as the most prominent ICIs. PD-L1 expression on tumor cells is increased by irradiation of the tumor . AdminisClinical trials rely on biomarkers to select the most suitable patients to receive costly therapy and prevent applying potentially harmful drugs to patients that will not benefit. Thus, there is a need for research tools that can be employed for preclinical investigations. These would need to reflect typical patient features and offer a representative cancer cohort in order to test if a newly targeted antigen is actually present on tumor cells or tumor immune cells. Ideally, they could then be used to shape opinion as to whether a new drug should be considered to be passed on into the clinical trial setting.Intratumoral immune cells have recently advanced into the focus of research groups regarding many solid tumors. Studies have been investigating the TIME in regards to their structure and contents, revealing a labyrinthian interdepending system of cells and cytokines. In several studies, researchers tried to adapt the TIME for better treatment response. Especially irradiation of tumors could induce apoptosis in cancer cells, leading to an antigen download on antigen-presenting cells by an increased MHC expression. This might be important for an increased treatment response by immune checkpoint inhibitors. On the other hand, strong irradiation can lead to lymphodepletion, so a lot of research is still needed . StudiesIt has been widely accepted that TIME can be categorized as being immunologic “hot” (immune cell infiltrates within the tumor), “cold” (no immune cell infiltration), or “excluded” (immune cells at tumor boundaries) , 18. EvaHowever, there are still questions to be answered such as: Do these categories exist in all solid tumor types? And can they assist in predicting patient outcome? Hypothesizing that there is a difference in the OS of HNSCC patients with different TIME patterns in primary tumors (PT), these questions are pursued in the study at hand to provide another piece in the highly complex puzzle of tumor immunology.n = 4 patients was not evaluable for p16.We established a cohort of 419 HNSCC patients  with 27.7% being p16 positive. Tissue of n = 43 were available to TIME evaluation.The majority of HNSCC PT were located in the oropharynx, larynx and oral cavity, followed by hypopharynx . ThirteeThe cohort can be subdivided into two arms: patients with a local RD (25.1%) vs. patients that did not experience a cancer relapse (74.9%). Five-year survival rates ranged from 51.8 to 54.8% for oral cavity and hypopharynx cancer, respectively, to 65.0 and 67.2% for oropharynx and larynx cancer, respectively. Five-year survival rate for the whole cohort was 61.9%, for recurrent disease patients it was 55.3%. 87.6% of patients had reported nicotine abuse, whereas 43.1% had acknowledged alcohol abuse.th edition of TNM classification. 50.2% of patients were classified T1/T2 and 45.4% were classified T3/4. The remaining 4.3% were TX or T0 (as in CUP). This levels with 41.1% UICC stages I/II and 58.8% UICC stages III/IV , irradiation (59.5%) and chemotherapy (30.9%). 52% of patients suffered from nodal positive HNSCC, which was treated by surgery and irradiation in 87% and by chemotherapy in 45.2%. 13.4% developed distant metastasis (DM). 5.8% of patients reappeared with a second cancer type.n = 289) and RD (n = 42) were categorized according to their immune cell infiltrate as being “hot,” “cold,” or “excluded.” In PT, the majority showed an “excluded” phenotype (52.6%) whereas the rest was almost evenly divided as “cold” (24.2%) and “hot” (23.2%). In RDs, the vast majority was either “cold” (47.6%) or “excluded” (42.9%) with only a small portion being “hot” (9.5%). Interestingly, immunologic “hot,” “cold,” and “excluded” tumors were found in equal proportions in p16 negative and p16 positive HNSCCs  = 0.547; p = 0.005), p16 expression , T stage , and UICC stage  were significant prognostic factors for the OS. The multivariate analysis determined the immune cell infiltration pattern  and the p16 expression  as independent prognostic factors for the OS of HNSCC patients.We performed univariate and multivariate Cox regression analyses to state whether the presence or absence of immune cell infiltration of tumors (“hot” or “excluded” vs. “cold” tumors) is a significant prognostic factor for the OS of HNSCC patients and if it is independent of other prognostic factors . We evalp > 0.05)  (No significant difference was found for OS when assessing TIME in RD ( > 0.05)  and in Tctively) . Neitherctively) .p = 0.048, Furthermore, no significant difference was found when observing TIME in different PT locations. In the hypopharynx, larynx, and oral cavity, roughly 55% were “excluded,” about 20% were “hot” and circa 25% were “cold” . When anWe compared OS for resected PT in combination with different therapy regimes. We found that “cold” tumor show a worse OS than inflamed  HNSCCs if the primary tumor was only treated by surgery  or by a Introducing ICI into cancer treatment dramatically changed the fate of many cancer patients. Especially in melanoma patients, there have been large improvements in DFS and OS , 25–27. We created a cohort of 419 HNSCC patients which is well-representative of tumor epidemiology and tumor properties. This cohort reflects the typical HNSCC patients' characteristics with a male to female ratio of ~3:1 , 30 and TIME in cancers can be categorized as being either “hot,” “cold,” or “excluded” by observing the distribution of immune cells in the tumor and its close neighborhood. An immunologic “hot” or “inflamed” tumor offers immune cells distributed diffusely throughout the tumor. A “cold” or “immune desert” tumor lacks immune cells whereas an “excluded” tumor shows immune cells in the desmoplastic septa of the tumor borders  Figure . It is wEspecially for “cold” tumors, our data showed significantly worse survival courses with worse OS and 5-year survival . This isCharacterizing TIME as being either “hot,” “cold,” or “excluded” can easily be done by any pathologist. It is also more cost-effective and time-saving than immunohistochemical analyses as it only requires standard H&E stained slides. These categories, therefore, propose to be a cheap and intriguing prognostic marker that may be considered to be included in routine pathology reports for HNSCC. In daily routine diagnostics, The best and universally applied prognostic system so far was and still is grading and tumor staging by TNM/UICC. The H&E categorization in “hot,” “cold,” and “excluded” could, however, serve as a valuable amendment to it. Especially in an irresectable setting, where no pTNM staging can be established, it might be of use as an addendum to cTNM when assessed on an excisional biopsy specimen. Supposedly, it could also aid in therapeutic decision making when a cancer is right in between two stages. However, before incorporating immune cell phenotypes as predictive markers in clinical practice, further investigation of TIME with special regard to representative classification systems has yet to be conducted. We shall be eager to await validation testing being performed on other cohorts and see the “hot-cold-excluded” scheme being applied to verify prognostic meaning, e.g., regarding OS or DFS. Within early and late UICC stages, there were no statistically significant differences in OS of TIME categories .Knowing distribution patterns of immune cell types in HNSCC could now be used to elucidate if the immune type influences the outcome of an ICI treatment. Our results reveal a minority of RD to be “hot” (9.5%). So far, the only approved ICIs in Germany for the treatment of HNSCC are nivolumab and pembrolizumab. They are, however, solely approved for the treatment of RDs and not for PTs. Before that, approval studies of these drugs were mainly conducted on RD patients . These oOur data show that “cold” HNSCCs show a worse OS when treated with resection only or with resection plus adjuvant chemoradiotherapy. There was no difference if the treatment was resection plus adjuvant irradiation . The finIn our cohort, we could show that immunologic “hot,” “cold,” and “excluded” tumors are equally distributed in p16 positive and negative HNSCCs. This might be an explanation of why there was no difference in the subgroup analysis of HPV positive vs. negative tumors in the KEYNOTE study series leading to the FDA approval of pembrolizumab in the treatment of HNSCCs , 13. HowIn the subgroup analysis of PT site of origin, the tumors of the oral cavity showed a significant difference in OS . Only inHPV positive HNSCCs have been reported to be more inflamed than HPV negative ones . Our datThe results presented here are merely a stopover, demonstrating the usefulness of tissue cohorts. We want to use our cohort and the knowledge of the different TIME types to investigate factors leading to either a “cold,” “excluded,” or “hot” tumor to get a better understanding of cancer immunology in HNSCC.In conclusion, we have constructed a large and well-characterized tumor tissue cohort with comprehensive clinicopathological data. The cohort is well-representative of HNSCC patients and provides us with a subtle device to further investigate ICI-naïve HNSCC. Furthermore, we assessed TIME by reading the tumoral immune infiltrate pattern. We showed that the categorization of HNSCC as being either immunologic “hot,” “cold,” or “excluded” results in statistically significant differences in OS. This cheap and easy classification may, therefore, be an intriguing prognostic tool that may be considered to be applied in routine pathology reports of HNSCC, possibly even as an amendment to staging and grading. However, further evaluation is warranted and validation testing on other cohorts is needed. This “hot-cold-excluded” scheme needs to be applied to more HNSCC cohorts and possibly to other cancer types to determine prognostic meaning, e.g., regarding OS or DFS.This retrospective study was conducted following the Declaration of Helsinki. It was approved by the local Ethics Committee . The REMARK (Reporting Recommendations for Tumor Marker Prognostic Studies) checklist  was consGerman state law requires hospital staff to report all first-time diagnosis of cancer to regional Cancer Registries (“Krebsregister”). This is done by using the international code of disease (ICD). For our study, we researched the hospital database for ciphers for squamous cell carcinoma (ICD-O-3) and head and neck regions . This pn = 419 patients. Patient data were anonymized. We assembled tumor tissue from PT, lymph node metastasis (LM), RD, and DM. Tissue samples were re-evaluated to classify each case according to the latest TNM classification (8th edition) and UICC stages accordingly. p16 status was determined by immunohistochemical staining of p16 . Regions of interest (ROIs) were annotated on H&E slides and paraffin blocks were matched. Three 0.1 cm cores (triplets) were punched out of every tumor to reflect heterogeneity and were then arranged in acceptor blocks as tissue microarrays (TMAs). Each TMA contained tissue triplets of 55 cancers and 5 normal mucosa samples.Then, H&E slides and paraffin blocks were drawn from the archives. After checking paraffin blocks for appropriate tumor amount the cohort was finalized with n = 289) or RD (n = 42). We distinguished three categories to determine TIME by reading H&E slides by the following criteria:For assessment of immune cell distribution status, we analyzed H&E whole slides from those patients from our cohort that underwent resection of PT . A third pathologist  was consulted to reach a consensus in the discrepant cases. If both “hot” and “excluded” patterns were present the one reflecting the majority was assigned. Two board-certified pathologists  assessed the slides using Olympus BX50 microscope with fluorite objectives with plano-correction . They achieved matching results in 96% . Sixty-months OS and DFS were calculated by the Kaplan-Meier method and log-rank test for statistical significance. Individuals lost to follow-up were censored. Univariate and multivariate Cox regression analyses were performed to evaluate the association among the three immune profiles  and UICC stages, T stages, p16 status, sex, grading, and patient age. Unless multiple hypothesis testing was applied. https://inkscape.org/). Krita . GIMP . Some data visualization was aided by Daniel's XL Toolbox add-in for Excel .We used the following software to create artwork, edit photomicrographs, and compile data visualization. Inkspace (version 0.92.4, The Inkscape Project c/o Software Freedom Conservancy, Brooklyn, NY, USA, The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Ethics Committee of the University of Luebeck. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.CI and SP: conceptualization. JR-I, RK, and CI: methodology. AO and PK: validation. LK: formal analysis. CI: investigation. SP and BW: resources. LK and CW: data curation. JR-I: writing—original draft preparation and visualization. RK and JR-I: writing—review and editing. BW and CI: supervision. SP and K-LB: project administration. LK, CI, and F-OP: funding acquisition. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Rev. Lett.45 494–7) and promise future applications in quantum computing . The quantum Hall effect derives from transverse conductance, quantized to unprecedented precision in accordance with the system’s topology (Laughlin 1981 Phys. Rev. B 23 5632–33). At magnetic fields beyond the reach of current condensed matter experiment, around 104 T, this conductance remains precisely quantized with values based on the topological order . Hitherto, quantized conductance has only been measured in extended 2D systems. Here, we experimentally studied narrow 2D ribbons, just 3 or 5 sites wide along one direction, using ultracold neutral atoms where such large magnetic fields can be engineered . We microscopically imaged the transverse spatial motion underlying the quantized Hall effect. Our measurements identify the topological Chern numbers with typical uncertainty of 5%, and show that although band topology is only properly defined in infinite systems, its signatures are striking even in nearly vanishingly thin systems.Physical systems with non-trivial topological order find direct applications in metrology (Klitzing  Even at laboratory fields of tens of Tesla, crystalline materials have a small magnetic flux Φ = AB0 per individual lattice plaquette (with area A) compared to the flux quantum Φ0 = h/e. Superlattice [0.The importance of topology in physical systems is famously evidenced by the quantum Hall effect’s role as an ultra-precise realization of the von Klitzing constant sistance . Althougrlattice  and ultrrlattice –9 systemσH = C/RK, and in cold-atom experiments direct probes of the underlying band structure at every value of crystal momentum give access to the Chern number through either static [0 = P/Q (for relatively prime integers P and Q) the integer solutions s and C to the Diophantine equation3 determine the Chern number C of the lowest band. Following theoretical work [Experimental signatures of Chern numbers generally leverage one of two physical effects: in condensed matter systems the edge-bulk correspondence allows the Chern number to be inferred from the quantized Hall conductivity r static ,11 or dyr static –15 signar static , in the cal work –20, we l2.j and m label lattice sites along ex and es, with tunneling strengths tx and ts respectively. As shown in ex was accompanied by a phase shift eϕmi. Hopping around a single plaquette of this lattice imprints a phase ϕ, analogous to the Aharanov–Bohm phase, emulating a magnetic flux ex, with atomic spin states forming lattice sites along a narrow, synthetic [es. The exact Hamiltonian of the underlying atomic system differs from the Harper–Hofstadter Hamiltonian above in that ts is non-uniform due to Clebsch–Gordan coefficients and there is a small m2 dependent potential term due to the quadratic Zeeman shift  in either the F = 1 or 2 ground state hyperfine manifold, creating 3 or 5 site-wide ribbons from the 2F + 1 states available in either manifold. We first loaded BECs into a 1D optical lattice along ex formed by a retro-reflected λL = 1064 nm laser beam. This created a lattice with period a = λL/2 and depth 4.4(1) EL, giving tunneling strength tx = 0.154(4)EL. Here, mRb is the atomic mass. We induced tunneling along es with either a spatially uniform rf magnetic field or two-photon Raman transitions. The tunneling strength was ts = 1.97(8) tx for Raman coupling in the F = 2 manifold, ts = 0.97(8) tx for Raman coupling in the F = 1 manifold, and ts = 7.4(5) tx for rf coupling in both manifolds. The rf-induced tunneling imparted at most only a spatially uniform tunneling phase, giving ϕ/2π = 0. In contrast the Raman coupling, formed by a pair of counter propagating laser beams with wavelength λR = 790 nm, imparted a phase factor exp(−2ikRx). Here, ex, effectively applying a linear potential. Using time-of-flight techniques [n, a function of momentum along ex and position along es, as seen in Our quantum Hall ribbons were created with optically trapped chniques , we meas3.qx(t = 0) = 0 in the lowest band with transverse modal position 4. es direction, with 41 sites. The energy is plotted as a function of crystal momentum along ex, with color indicating the expectation value of position along es, calculated by diagonalizing the full Hamiltonian with zero quadratic shift and uniform Clebsch–Gordan coefficients  EL/λL for varying times Δt, directly changing the longitudinal crystal momentum from 0 to a final qx and giving a transverse Hall displacement from 0 to a final n for a specific final qx, labeled by the overall horizontal axis. For each column, the change in crystal momentum is marked by the horizontal displacement of the diffraction orders relative to their location in the central qx = 0 column. The transverse displacement is visible in the overall shift in density along m as a function of qx, i.e. between columns.We applied a force qx for Φ/Φ0 = 0, −4/3, and 4/3. The data is represented by gray dots, with uncertainty bars reflecting the propagated standard uncertainty from averaging six identical runs. For zero flux Φ/Φ0 = 0 (qx; in contrast, for non-zero flux qx with non-zero slope. These linear dependencies evoke our earlier discussion of the Hall conductance H = 0, ±1 are indicated by black dashed lines in Φ/Φ0 = 0 , m¯ was /Φ0≈±4/3 , m¯ depeqx as calculated from exact diagonalization of the full Hamiltonian (see qx sharply returning to The red curves in nian see , always nian see , i.e. Bllose see , making One might suspect that limiting the domain of the linear fit such that band edge effects are excluded would still provide a good measure of the Chern number. However, as shown in 4.m-translations that are integer multiples of Q, a so-called ‘magnetic-displacement’ by Δm = 1 along es accompanied by a displacement in crystal momentum along ex by Δqx/2kR = P/Q leaves Q-fold reduction of the Brillouin zone along es, and add a Q-fold degeneracy, as illustrated in 0 = 0, 1/3, and 2/5. Recalling that the Brillouin zone is 2ex, it follows that a displacement by 2kL/Q to the nearest symmetry related state involves an integer C magnetic displacements, shown in 0 = 1/3 and 2/5, given by solutions to 2kLs − 2kRC = 2kL/Q, where s counts the number oftimes the Brillouin zone was ‘wrapped around’ during the C vertical displacements. Because this is exactly the TKNN C as the Chern number. Both C and s directly relate to physical processes. First, each time the Brillouin zone is wrapped around implying a net change of momentum by 2m by 1 must be accompanied by a 2To better identify Chern numbers, we relate the TKNN equation  to the pm site as a function of crystal momentum qx in the lowest band. The black circles locate the peak of the fractional population in each spin state. We identify the locations of those peaks as the crystal momenta at which the atoms were displaced by a single lattice site along es starting at qx = 0, similar to the suggestions in [5, in agreement with the exact theory as calculated from the full Hamiltonian (see tions in ,20. We anian see , which pts > tx allowing hybridization of states in the vicinity of the edge (see es and the tunneling ts is studied in es, with tunneling tx = 0.5EL, synthetic direction coupling The deviation from unity results from 5.es could elucidate the appearance of edge modes as the coupling between two of the three states is smoothly tuned to zero. In addition, due to the non-trivial topology as well assign the low heating afforded by synthetic dimensional systems, a quantum Fermi gas dressed similarly to our system would be a good candidate for realizing fractional Chern insulators [Our direct microscopic observations of topologically driven transverse transport demonstrate the power of combining momentum and site-resolved position measurements. With the addition of interactions, these systems have been shown to display chiral currents , and witsulators .


There is limited data on cerebrospinal fluid (CSF) biomarkers in sporadic amyloid-β (Aβ) cerebral amyloid angiopathy (CAA).To determine the profile of biomarkers relevant to neurodegenerative disease in the CSF of patients with CAA.We performed a detailed comparison of CSF markers, comparing patients with CAA, Alzheimer’s disease (AD), and control (CS) participants, recruited from the Biomarkers and Outcomes in CAA (BOCAA) study, and a Specialist Cognitive Disorders Service.p < 0.01) median concentrations of Aβ38, Aβ40, Aβ42, sAβPPα, and sAβPPβ. CAA patients had higher levels of neurofilament light (NFL) than the CS group (p < 0.01), but there were no significant differences in CSF total tau, phospho-tau, soluble TREM2 (sTREM2), or neurogranin concentrations. AD patients had higher total tau, phospho-tau and neurogranin than CS and CAA groups. In age-adjusted analyses, differences for the CAA group remained for Aβ38, Aβ40, Aβ42, and sAβPPβ. Comparing CAA patients with amyloid-PET positive (n = 5) and negative (n = 5) scans, PET positive individuals had lower (p < 0.05) concentrations of CSF Aβ42, and higher total tau, phospho-tau, NFL, and neurogranin concentrations, consistent with an “AD-like” profile.We included 10 CAA, 20 AD, and 10 CS participants . In unadjusted analyses, CAA patients had a distinctive CSF biomarker profile, with significantly lower (CAA has a characteristic biomarker profile, suggestive of a global, rather than selective, accumulation of amyloid species; we also provide evidence of different phenotypes according to amyloid-PET positivity. Further replication and validation of these preliminary findings in larger cohorts is needed. Sporadic amyloid-β (Aβ) cerebral amyloid angiopathy (CAA) can be reliably diagnosed during life using the clinico-radiological Boston criteria , but nea40, Aβ42) and tau  measures. However, there is limited information on other amyloid species in CAA, including smaller proteins such as Aβ38, soluble amyloid-β protein precursor (sAβPP) α and β. Additionally, a number of newer fluid biomarkers are of potential interest in CAA. Neurofilament light (NFL), soluble TREM2 (sTREM2) and neurogranin are promising new biomarkers for Alzheimer’s disease (AD), but it is not clear whether they are specific for parenchymal amyloid.To date, most data on CSF biomarkers in CAA have focused on Aβ  and other markers studied in neurodegenerative disease  in the CSF of patients with AD, CAA, and control (CS) participants in an exploratory hypothesis-generating study. To explore the heterogeneity of CAA CSF profile and relationship with parenchymal amyloid deposition, we then performed post-hoc analyses comparing the CSF profiles of CAA patients with amyloid-PET positive and negative scans.Our aim was to perform a detailed comparison of amyloid markers  study . Ethical approval for the BOCAA study was granted in October 2015 by the NHS Health Research Authority London (REC reference 15/LO/1443). Secondly, we included samples collected by the Specialist Cognitive Disorders Service at the NHNN, University College London Hospitals (UCLH) NHS Trust, London, UK . This study was approved by the Regional Ethics Committee at UCL.In all cases, informed written consent was obtained for each participant, and inclusion criteria were standardized to be consistent with the BOCAA study (further details below).All patients with CAA were recruited from the BOCAA study . ConsecuCS participants were included from two sources. In all cases, CS participants were required to have no prior history of significant neurological disease. Further inclusion criteria were: age ≥55 years, MMSE score ≥23, and modified Rankin scale (mRS) ≤3.Firstly, we included CS participants recruited as part of the BOCAA study, where patient partners were invited to participate as healthy volunteers. CS participants were also identified from a prospectively collected database of patients attending the ambulatory transient ischemic attack (TIA) service, provided by the NHNN; patients whose final diagnosis was not stroke, TIA, or any other significant neurological condition (and met the inclusion and exclusion criteria) were invited to participate. Those with contraindications to PET or MRI scanning or lumbar puncture were excluded.Secondly, we included samples collected by the Specialist Cognitive Disorders Service at the National Hospital of Neurology and Neurosurgery (NHNN). Samples for age-matched CS participants were included if their final diagnosis, made on the basis of clinical assessment, imaging, and CSF, was not one of dementia or any other neurodegenerative condition . MR imag42 ratio >0.88 together with Aβ42 < 630 pg/ml [Patients with AD presented with “typical”  amnestic30 pg/ml .All CSF analyses were performed by the Biomarker Laboratory of the UK Dementia Research Institute at UCL (Group Lead: Professor Henrik Zetterberg). CSF was collected, processed, and stored according to standardized procedures, and was identical for all diagnostic groups . Samples38, Aβ40, and Aβ42 were measured by electrochemiluminescence (ECL) using a Meso Scale Discovery V-PLEX Aβ peptide panel 1(6E10) kit, according to the manufacturer’s instructions . Briefly, samples were diluted 1 : 2 with diluent 35 and added in duplicate to microplate wells coated with mouse monoclonal peptide specific capture antibodies for human Aβx-38/x-40/x-42. Samples were incubated with anti-Aβ (amino acids 1–16 epitope) antibody (6E10 clone) as the detection antibody conjugated with an electrically excitable SULFO-TAG. Concentrations were calculated from ECL signal using a four-parameter logistic curve fitting method with the MSD Workbench software package. Intra-assay CVs (coefficients of variation) were less than 10%. All samples were measured on the same day by a single operator using the same reagents.Aβα and sAβPPβ were measured by ECL using a Meso Scale Discovery sAβPPα/sAβPPβ Kit, according to the manufacturer’s instructions . Briefly, samples were diluted 1 : 4 with 1% Blocker A and added in duplicate to microplate wells coated with mouse (sAβPPα) and rabbit (sAβPPβ) monoclonal peptide specific capture antibodies. Samples were incubated with anti-sAβPPα and anti-sAβPPβ detection antibodies conjugated with an electrically excitable SULFO-TAG. Concentrations were calculated from ECL signal using a four-parameter logistic curve fitting method with the MSD Workbench software package. Intra-assay CVs were less than 20%. All samples were measured on the same day by a single operator using the same reagents.sAβPP (181P)  were determined using a sandwich ELISA (INNOtest® hTAU-Ag p-Tau (181P) ; Fujirebio Europe N.V., Gent, Belgium) constructed to measure both normal tau and phosphorylated tau. Briefly, for the hTAU Ag assay, tau protein is captured from CSF samples by a monoclonal anti-tau antibody (AT120) bound to a microtiter plate. Captured tau is detected with two biotinylated tau-specific monoclonal antibodies (HT7 and BT2). Similarly, for the t-tau assay, p-tau (181P)  is captured from CSF samples by anti-tau antibody HT7 bound onto a microtiter plate. Captured p-tau (181P)  is detected with a biotinylated monoclonal anti-p-tau antibody (AT270). In both assays, peroxidase-labelled streptavidin and tetramethylbenzidine (TMB) substrate are also added. Peroxidase-catalyzed hydrolysis produces a colorimetric signal. Sample concentrations are extrapolated from a standard curve, fitted using a 4-parameter logistic algorithm. Intra-assay CVs were less than 20%.The levels of CSF t-tau and p-tauNFL was measured using the commercially available NF-Light ELISA, according to the manufacturer’s instructions . Briefly, samples were diluted 1 : 2 with sample diluent and added in duplicate to microplate wells coated with a monoclonal capture antibody specific for NFL. Samples were incubated with a biotinylated NFL-specific monoclonal detection antibody. The detection complex was completed with the addition of horseradish peroxidase-labelled streptavidin and TMB substrate. Peroxidase-catalyzed hydrolysis produces a colorimetric signal. Sample concentrations were extrapolated from a standard curve, fitted using a 4-parameter logistic algorithm. Intra-assay CVs were less than 10%. Samples were run on two different days by different operators; the inter-assay CV was below 16%.μg/ml; BAF1828, R&D Systems, Minneapolis, MN, USA) diluted in block buffer, shaking for 1 h at room temperature. They were subsequently washed five times with wash buffer (0.05% Tween 20 in PBS) and incubated for 2 h shaking at room temperature with 50μL per well of either the standard curve constructed from recombinant human TREM2 protein  diluted in assay buffer  to produce concentrations ranging between 4000 pg/ml and 62.5pg/ml, or CSF samples diluted 1 in 4 in assay buffer. Standards and CSF samples were assayed in duplicate. Plates were again washed five times with wash buffer before incubation for 1 h shaking at room temperature with the detection antibody, monoclonal mouse anti-human TREM2 antibody : sc373828, Santa Cruz Biotechnology, TX, USA), diluted in block buffer. After five additional washing steps, plates were incubated with the secondary antibody  and incubated shaking for 1 h in the dark. Lastly, plates were washed three times with wash buffer then twice in PBS alone. The electrochemical signal was developed by adding MSD Read buffer T 4×  diluted 1 in 2, and the light emission measured using the MSD Sector Imager 6000. The concentration of sTREM2 was calculated using a five-parameter logistic curve fitting method with the MSD Workbench software package. Intra-assay CVs were less than 10%, and all samples were measured on the same day by a single operator using the same reagents.Samples were analyzed using an immunoassay protocol adapted from a previously published protocol . StreptaNeurogranin was measured with the EUROIMMUN Elisa (EQ6551-9601-L) according to manufacturer’s instructions . Briefly, samples were incubated with biotinylated monoclonal anti-neurogranin antibody, followed by addition to microplate wells coated with monoclonal antibodies specific for human neurogranin truncated at P75. Finally, streptavidin peroxidase conjugate was added to initiate the color-changing reaction. Intra-assay CVs were less than 10%, and all samples were measured on the same day by a single operator using the same reagents. Sample concentrations are extrapolated from a standard curve, fitted using a 5-parameter logistic algorithm.18F-Florbetapir, Amyvid) and MR scanning, acquired using the same hybrid Siemens Biograph PET/MR scanner; the protocol for acquisition and initial processing has been previously described [All participants in the BOCAA study underwent PET  was applied.Statistical analysis was performed using Stata (Version 15.1). Group characteristics were compared using one-way ANOVA (age), chi squared (sex), or Kruskal-Wallis (MMSE) tests. Median and interquartile range values were calculated for each biomarker, and, given the non-normal distribution of the data, comparisons between groups were made using the Kruskal-Wallis test. If a significant difference was identified (defined as p < 0.05), Dunn’s test was used for post-hoc pairwise comparisons of the age-adjusted medians; statistical significance was defined as Bonferroni-corrected p < 0.05.In order to perform age-adjusted analyses, we used quantile regression (comparing group medians) and calculated predicted medians. We then performed post-hoc analyses comparing biomarkers between PET positive and negative CAA patients using Wilcoxon-Mann-Whitney tests.Finally, we performed exploratory We included 20 patients with AD, 10 patients with CAA, and 10 CS participants in this analysis; baseline characteristics are shown in Of the patients with CAA, 5 (50%) presented with intracerebral hemorrhage, and the remainder presented with transient focal neurological episodes associated with convexity subarachnoid hemorrhage. All CAA patients had been clinically asymptomatic in the 6 months prior to their study visits. Amongst the CAA group, 7 patients (70%) showed evidence of cortical superficial siderosis  and 9 (90%) had lobar microbleeds . All CAA patients showed evidence of both deep and periventricular white matter hyperintensities on MRI .38, Aβ40, Aβ42, sAβPPα, sAβPPβ, t-tau, p-tau, NFL, and neurogranin. There was no significant difference in sTREM2 levels between the three groups.Univariable comparisons are shown in post-hoc comparisons , there were no statistically significant differences between the CAA and CS groups; patients with AD had significantly higher levels than both CAA and CS group. Patients with both CAA and AD had significantly higher CSF NFL than the CS group; there was no statistically significant difference between the CAA and AD groups. There was no difference in neurogranin levels between CAA and either the AD or the CS groups; patients with AD had significantly higher levels of CSF neurogranin than the CS group.In parisons , Fig. 1,38, Aβ40, Aβ42, and sAβPPβ, but not sAβPPα. Pairwise comparisons of the age-adjusted medians found significant differences between CAA and AD groups for Aβ38 , Aβ40  and sAβPPβ ; for Aβ42, the difference between the CAA and AD groups did not reach statistical significance . There were significant differences between the CAA and CS groups for Aβ38 , Aβ40 (median difference 4060 pg/ml), Aβ42 , and sAβPPβ . For Aβ42, there was a significant difference between the AD and CS groups .Results from the age-adjusted quantile regression are shown in There were significant differences between the three groups for both t-tau and p-tau. Pairwise comparisons of the age-adjusted medians found significant differences for both t-tau  and p-tau  between the CAA and AD groups. There was no significant difference between the CAA and CS groups for either t-tau (median difference 53.1 pg/ml) or p-tau (median difference 2.2 pg/ml). There were significant differences between the AD and CS groups for t-tau  and p-tau .post-hoc comparisons of the adjusted medians. There were no significant differences between the three groups for NFL or sTREM2.There was a significant difference in neurogranin between the three groups, but no significant differences were identified in pairwise Results for comparison of Aβ ratios are provided in the post-hoc analyses comparing the CSF profiles of CAA patients with amyloid-PET positive and negative scans. Half of the patients in the CAA group  were PET positive by visual read. There were no differences in age, sex or MMSE score between the PET positive and negative groups. When comparing CSF biomarkers , and higher CSF t-tau , p-tau , NFL , and neurogranin .We then performed omarkers , PET pos38, Aβ40, and Aβ42 than both AD and CS groups, with no statistically significant differences between the AD and CS groups for these markers. There were no significant differences in sAβPPα and sAβPPβ between the three groups; the results for t-tau, p-tau, NFL, and sTREM2 were similar to those identified in the original analyses. In contrast to the original analysis, neurogranin in amyloid-PET negative CAA was significantly lower than the AD group.Given these results, we performed a further comparison, in which amyloid-PET negative CAA patients were compared with AD and CS groups; these results are provided in the 38, Aβ40, Aβ42, sAβPPα, and sAβPPβ) and higher NFL, but did not show differences in CSF t-tau, p-tau, sTREM2, or neurogranin profile. Patients with AD had higher t-tau, p-tau and neurogranin than both control participants and patients with CAA. In age-adjusted analyses, differences for the CAA group remained for Aβ38, Aβ40, Aβ42, and sAβPPβ. Finally, we performed exploratory post-hoc comparisons within the CAA group, comparing those with amyloid-PET positive and negative scans, and found that those who were PET positive showed differences in Aβ42, t-tau, p-tau, NFL, and neurogranin, in an AD-like profile.In this exploratory, hypothesis-generating study, we found that patients with CAA had a distinctive CSF profile compared with CS participants and patients with AD. In unadjusted analyses, patients with CAA showed lower levels of all amyloid components measured  in the parenchyma [38, have also been shown to be deposited in the leptomeningeal vasculature [38 and sAβPPβ  are novel, and support the protein elimination failure hypothesis for CAA [Our results for Aβted data ; howeverescribed . Howevere common . The redrenchyma . Aβ peptculature . Our fin for CAA , which p for CAA . Our resOur results also provide new information on non-amyloid biomarkers in CAA. We found significant elevations in t-tau, p-tau, and neurogranin in patients with AD, but not in patients with CAA compared with healthy controls. This is in contrast with other studies which have found that t-tau and p-tau levels in CAA are higher than controls but lower than patients with AD , and maypost-hoc analyses, patients with CAA who were amyloid-PET positive had particularly high levels, raising the possibility that this is a useful marker for co-existing AD and CAA pathology. As seems to be the case for many other conditions, including cerebral small vessel disease [We also provide new data on other markers that have not been tested in CAA. NFL has shown great promise as a biomarker in a large number of neurological conditions , includi disease , NFL in  disease . Again, post-hoc analyses comparing amyloid-PET positive and negative patients with CAA raise new questions about the interactions between AD and CAA pathologies. Although the group sizes are small (with only five participants per group), these findings confirm that not all patients with neuroimaging evidence of CAA are PET positive  [42, higher CSF t-tau, phospho-tau, NFL, and neurogranin—who have evidence of additional fibrillary amyloid deposition. As described above, the marked increase in NFL in PET positive CAA patients might suggest that NFL is particularly sensitive for dual AD and CAA pathology. Neurogranin was able to discriminate between PET negative CAA, AD, and CS groups, with elevations only evident in the AD group, providing further evidence that this might be a very specific marker for fibrillary AD pathology. Together, these data support the hypothesis that amyloid-PET positivity in CAA is a measure of co-existent parenchymal Aβ rather than a measure of vascular amyloid and raises the possibility that the degree or extent of co-existent AD pathology in CAA can be measured using these CSF markers. An alternative explanation is that these markers, together with amyloid-PET positivity, are indicative of a higher vascular amyloid burden that results in neurodegeneration via non-AD mechanisms. Further work in larger cohorts is needed, both to replicate these findings and explore these hypotheses further.Our exploratory  to 91%) , and sug40, Aβ42, t-tau, and p-tau), we provide new data in CAA for a number of other markers, which we hope will inform future research with regard to effect sizes and sample size calculations. We acknowledge that there can be discrepancies between clinical and pathological diagnoses of CAA and AD, as well as significant overlap in these pathologies. While we attempted to avoid this on the basis of clinical and radiological findings, we did not have autopsy data, which would be the gold standard. However, we would argue that most clinicians who review patients with AD or CAA make their diagnosis on the basis of clinical and radiological (rather than autopsy) findings, and therefore our results remain of relevance. We specifically selected non-demented CAA patients with “early” or mild disease; these patients may not have such marked biomarker perturbations as those with more severe disease. The AD patients selected from the Specialist Cognitive Disorders Service may not be fully representative of AD patients more generally; this is a highly specialist tertiary service, which often sees younger patients or those with atypical presentations. We used CSF criteria (t-tau and Aβ42) in order to identify patients with AD and half of the control group, and so these markers are not fully independent in our analyses. Many of our control participants presented to neurology services , and although we only included individuals without clinical, radiological, or CSF evidence of significant neurological disease, we acknowledge that they cannot be described as true “healthy” controls; this is a further limitation of our work. We selected control participants with minimal radiological evidence of brain pathology , and this may not be truly representative of unselected age-matched individuals without AD or CAA. We determined PET positivity on qualitative  rather than quantitative grounds, and the group sizes are particularly small in this post-hoc analysis, which precludes from age-adjustment. However, despite these limitations, we provide important new data on these body fluid markers in CAA, and in particular provide data on effect sizes that will be critical for determining sample sizes for larger future studies.Our work has a number of strengths. We were able to evaluate a large number of markers and to reduce false-positive error we have used two separate statistical methods, one rank-based  and the other an age-adjusted analysis based upon comparison of medians. However, there are some limitations. As mentioned earlier, this was a small exploratory study which may not have been powered to detect differences for all the biomarkers considered. Given our small sample size, we acknowledge the possibility of false-positive results due to multiple comparisons as a limitation of our work. While larger studies have considered some of the CSF markers we describe  compared with AD patients. Replication of these findings in larger cohorts with longitudinal cohort measurements is required to confirm these markers as effective biomarkers for CAA, and their value for monitoring disease progression in clinical trials.Click here for additional data file.


Exotic species’ introductions together with extinction of native species represent the main mechanisms driving biotic homogenization of freshwater fish assemblages around the world. While generally ichtyofaunistic realms transit towards biotic homogenization, for conservation purposes it is essential to understand what specific mechanisms are promoting it on particular areas or regions. Here, we report the occurrence of biotic homogenization in 29 Chilean watersheds, analyzing its β-diversity (including turnover and nestedness) and predicting future trends.o–56o S).Continental Chile (18sim), nestedness (βnes), and β-diversity (βsor); then, we obtained Δβsim, Δβnes, and Δβsor, as the arithmetical difference between basin pairs over time. In addition, we search for explanatory variables correlating Δβsim, Δβnes, and Δβsor with geographical and land use variables. Finally, simulating events of species introduction  and extinction, we generated 15 hypothetical assemblages, looking to establish future trends towards biotic change in Chilean basins.We determined fish composition per basin for historical and current assemblages; extant native, exotic, and extinct species were recorded as 1 (presence) or 0 (absence) in two matrices basins × species. For each matrix, we calculated the turnover , while the species nestedness did not show significant changes (Δβnes = 0.08). Biotic changes have been driven mainly by the introduction of 28 exotic species, with a minor role of extinctions (one species) and translocations (0 species) of native species. Changes in β-diversity were negatively correlated with area, elevation, and geographical distance between basins but not with land-use nor human population. Finally, the analysis of 15 future assemblages predicts a significant decrease of β-diversity and turnover, and an increase for species nestedness, this time promoted by an increase in the extinction of native species.Species turnover and β-diversity significantly decreased from historical to current assemblages (ΔβChilean basins show a significant decrease of the distributional β-diversity and species turnover of the freshwater fish fauna, evidencing a trend towards biotic homogenization. This trend is shared with other Neotropical basins; however, specific mechanisms driving it show different magnitude. Changes in the β-diversity components do not show correlation with variables associated to land use, thus suggesting that casual introductions of freshwater fishes in Chile follow an opportunistic mode related to commercial use. According to future scenarios simulated, biotic homogenization should increase further, mainly as consequence of increased native extinctions. Freshwater fish assemblages show a particular vulnerability to human activity and agents of global change . These aAlthough homogenization studies in freshwater fish assemblages are increasing, most part of them come from temperate latitudes of the Northern Hemisphere e.g., –20, evidFreshwater fish assemblages show that biotic homogenization is driven mainly by introduction of exotic species, and to a lesser extent by extinction of native species , 24, 25.Due to the presence of natural geographical barriers that coincide with its political limits, continental Chile is considered a biogeographic island inside South America, and therefore, an attractive study system . On its In the present article, we aim to establish whether Chilean basins exhibiting a poor but highly endemic freshwater fish fauna, are undergoing biotic homogenization. Unlike previous studies carried out in Chile, we propose an analytic framework based on β-diversity and its components, in order to examine homogenization and its future trends in Chile. Thus, we consider three situations: a) historical , b) current species (post-European) composition on basin assemblages, and c) a set of hypothetical assemblages which were generated simulating occurrence of new invasion and extinction events. We hypothesized that: (i) if biotic homogenization is occurring among Chilean basins, then the current β-diversity components (one of them or both) should be significantly lesser than those historically established; and (ii) if future spread/extinction events intensifies homogenization, then future β-diversity components (one of them or both) should be significantly lesser than those currently established.2. Within this territory, there are 101 major hydrographic basins , presence in assemblage ‘Future 2’, presence in ‘Future 3’ assemblage]. For exotic species potentially successful colonizers to the Chilean basins, the information was ordered as: . By using this information other nine assemblages were conformed.(XLSX)Click here for additional data file.S3 TableBasins were latitudinally ordered see . Chungar(XLSX)Click here for additional data file.


Xylella fastidiosa is a highly virulent pathogen that causes Olive Quick Decline Syndrome (OQDS), which is currently devastating olive plantations in the Salento region . We explored the microbiome associated with X. fastidiosa-infected (Xf-infected) and -uninfected (Xf-uninfected) olive trees in Salento, to assess the level of dysbiosis and to get first insights into the potential role of microbial endophytes in protecting the host from the disease. The resistant cultivar “Leccino” was compared to the susceptible cultivar “Cellina di Nardò”, in order to identify microbial taxa and parameters potentially involved in resistance mechanisms. Metabarcoding of 16S rRNA genes and fungal ITS2 was used to characterize both total and endophytic microbiota in olive branches and leaves. “Cellina di Nardò” showed a drastic dysbiosis after X. fastidiosa infection, while “Leccino” (both infected and uninfected) maintained a similar microbiota. The genus Pseudomonas dominated all “Leccino” and Xf-uninfected “Cellina di Nardò” trees, whereas Ammoniphilus prevailed in Xf-infected “Cellina di Nardò”. Diversity of microbiota in Xf-uninfected “Leccino” was higher than in Xf-uninfected “Cellina di Nardò”. Several bacterial taxa specifically associated with “Leccino” showed potential interactions with X. fastidiosa. The maintenance of a healthy microbiota with higher diversity and the presence of cultivar-specific microbes might support the resistance of “Leccino” to X. fastidiosa. Such beneficial bacteria might be isolated in the future for biological treatment of the OQDS. Olea europaea L.) has also an associated microbiome, which was shown to be cultivar-specific [Pseudomonas savastanoi pv. savastanoi and showed the presence of a highly diverse bacterial microbiota. However, they did not analyze P. savastanoi-uninfected plants as control, which makes it impossible to derive conclusions about the response of the native olive microbiome to the disease.The central role of the plant-associated microbiome in maintaining host’s fitness is being recognized more and more, and plants are now regarded as “holobionts”, which include the plant itself and the entire community of associated microbes, seen as a single unit of evolution ,2. The pspecific ,10,11. Bspecific . Indeed,specific . In contspecific  analyzedXylella fastidiosa Wells et al., subsp. pauca, a xylem-limited bacterial pathogen transmitted in olive trees by sap-feeding insect vectors, i.e., the meadow spittlebug (Philaenus spumarius L.) [X. fastidiosa can infect more than 550 plant species, including trees of major importance in forest ecosystems and urban greening plantations, such as oak, elm, sycamore, and maple, representing one of the major threats to agro-forest-ecosystems all over the world [X. fastidiosa, while a notable resistance was observed in the less common cultivar “Leccino” [One of the most dramatic diseases for olive trees is the Olive Quick Decline Syndrome (OQDS), which resulted in devastation of thousands of hectares of plants in the Salento region, in Southern Italy), including very old trees ,16, and rius L.) . X. fasthe world ,20,21,22he world ,24,25,26Leccino” ,28,29.X. fastidiosa, it was shown that the nature of the endophytic bacterial community is able to downregulate the pathogen growth or plant symptoms, either because they compete with the pathogen or because they secrete substances able to modulate its virulence [The mechanisms of resistance of the “Leccino” trees are still unclear. It was previously shown that “Leccino” resistance is probably influenced by the lignin amount in the xylem vessels, which can limit the bacteria movement and the host invasion by slowing down the disease progression , or by tirulence ,32. The irulence .X. fastidiosa, for example, by inhibiting the pathogen via microbe–microbe interactions or by triggering the reaction of the plant immune system. This interaction could synergistically enforce the natural resistance of “Leccino” trees to this pathogen.Microbial diversity is associated with plant health and productivity ,35, simiX. fastidiosa-infected (Xf-infected) and -uninfected (Xf-uninfected) “Leccino” trees (X. fastidiosa-resistant), using the X. fastidiosa-susceptible “Cellina di Nardò” trees as a control. Our aims were as follows: (i) to assess differences in terms of assemblage, diversity, and structure between the two cultivars; (ii) to understand the response of the native microbiota to the X. fastidiosa infection; and (iii) to identify taxa specifically associated to the cultivar “Leccino”. Moreover, we explored the microbial co-occurrence/co-exclusion network within the endophytes of Xf-infected “Leccino”, to gain insights into the multispecific interactions involving X. fastidiosa. We hypothesized the following: (i) the cultivar “Leccino” harbors specific microbiota, with a different assemblage and a higher diversity compared to “Cellina di Nardò”; (ii) the response of the olive microbiota to X. fastidiosa infection is different between the two cultivars; (iii) microbial taxa exist that are maintained, or that appear only, during X. fastidiosa infection in “Leccino” but not in “Cellina di Nardò”; and (iv) these “Leccino”-specific taxa show potential multispecific interactions with X. fastidiosa.In this study, we explored and compared the autochthonous fungal and bacterial microbiota associated with both A total of 4,548,524 raw sequences were obtained from the 16S rRNA gene amplicon libraries after joining the paired ends, from 78 of the 80 samples (two samples did not give a sequencing output). After length and quality filtering, a total of 4,534,886 sequences remained, of which 4,337,856 (95.7%) were identified as non-chimeric. They were grouped into 21,358 OTUs at 97% sequence similarity threshold. After removal of mitochondrial, plastidic, unassigned OTUs, and OTUs with less than 10 reads, 881 OTUs remained, representing a total of 3,944,950 sequences , after joining the paired ends. After length and quality filtering, a total of 2,868,275 sequences remained, of which 2,753,359 were identified as non-chimeric and 2,753,353 (96.0%) were extracted as ITS2. They were grouped into 6039 OTUs at 97% sequence similarity threshold. After removal of unassigned OTUs, metazoan OTUs and OTUs with less than 10 reads, 1006 OTUs remained, representing a total of 2,620,425 sequences  three to five biological replicates remained (www.ebi.ac.uk/ena) under the project number PRJEB32050.Before proceeding with the sequence analysis, five 16S rRNA gene samples and nine ITS2 samples were eliminated because of the lower number of reads in order to increase the sequencing depth at the rarefaction step (11950 reads for Bacteria and 4850 for Fungi), thus optimizing the per-sample coverage. However, the statistical design was nor unbalanced, since for each type of sample  and >98.7% for Fungi (average = 99.57%) , demonstProteobacteria with an average relative abundance per sample of 76.0%, followed by Firmicutes (16.9%), Bacteroidetes (3.8%), and Actinobacteria (2.8%). The remaining phyla  accounted for a total of 0.49%. The most abundant families were Pseudomonadaceae (56.2%) and Paenibacillaceae (14.8%) , P. stutzeri (98.9%) and, at a less extent, P. taiwanensis/monteilii/knackmussii/juntendi (98.7%), except the Xf-infected “Cellina di Nardò” samples that were dominated by Ammoniphilus  . The ten (14.8%) A. All saniphilus A. X. fas samples , where i samples , and in ommunity , where iommunity A.Ascomycota, with an average relative abundance per sample of 84.4%, followed by Basidiomycota (11.5%) and unidentified (4.1%). The most abundant families were Hypocreales_fam_Incertae_sedis (45.8%) and Aureobasidiaceae (7.3%) ; branches looked similar to the endophytic communities, while, in leaves, Aureobasidium and Capnodiales prevail  compared to “Cellina di Nardò”. However, the Xf-infected “Leccino” looked very similar to the Xf-infected “Cellina di Nardò” , 26 classes, 63 orders, 142 families, and 241 genera . The mose (7.3%) . The tene (7.3%) B. All enocladium B. Total  prevail B. “Leccii Nardò” B.p < 0.001; p < 0.001) (p = 0.044) (p < 0.001) and in Xf-infected samples (p = 0.018), respectively. Both factors did not significantly affect fungal diversity instead . Significant interactions for bacteria were Cultivar*Infection status (p = 0.007) and Cultivar*Habitat (p = 0.003). For fungi, also, the same interactions were significant , as well as Cultivar*Infection status*Habitat (p < 0.001). Interestingly, Xf-uninfected “Leccino” showed a notably higher bacterial diversity compared to the corresponding Xf-uninfected “Cellina di Nardò”, for both total community and endophytes (Alpha-diversity metrics (Shannon diversity and equitability) of the endophytes were lower when compared to the total community for both fungi and bacteria, as expected (< 0.001) A, while,= 0.044) B. Habitadophytes A. In thedophytes B. The EqXf-infected “Cellina di Nardò” plants was the most different from all the other ones, especially the leaf endophytic microbiota   than for Xf-uninfected plants (p = 0.04). Indeed, analyzing separately Xf-uninfected branches and leaves, no difference was found between endophytes of “Cellina di Nardò” and “Leccino”  . Howeveri Nardò” B. The efi Nardò” C. Separa samples D.Xf-infected “Leccino” branches and leaves showed the same structure than “Cellina di Nardò” plants (p = 0.024), which was due only to “Leccino” (p < 0.001), while “Cellina di Nardò” Xf-infected and -uninfected samples did not differ (p = 0.069)  . In the = 0.069) E, no clep-values .Xf-infected and -uninfected “Leccino” trees, while it is significantly different between Xf-infected and -uninfected “Cellina di Nardò” trees : Firmicutes were more abundant in “Cellina di Nardò” Xf-infected samples, while Proteobacteria, Bacteroidetes and Actinobacteria were more abundant in “Leccino” . Seven bacterial families showed a significantly different distribution among the groups ; their clustering showed how “Cellina di Nardò” Xf-infected samples clustered apart . X. fastidiosa was directly interacting with 10 OTUs , showing eight positive and two negative interactions . Interestingly, several OTUs belonging to the bacterial genera that were found to be significantly associated with “Leccino” ; however, g-test of independence indicated that, also, this OTUs was non-randomly distributed between Xf-infected cultivars (FDR-corrected p < 0.001).Out of 43 bacterial and 47 fungal OTUs that passed the minimum abundance thresholds, 28 and 24 showed at least one potential interaction. There were 225 positive and 192 negative interactions that were supported by at least three of the four correlation and distance methods computed . There wLeccino” B–J, wereicrobium , arrow; icrobium , althougX. fastidiosa-resistant olive cultivar “Leccino”, a highly promising cultivar able to survive and grow in Salento despite the severe X. fastidiosa outbreak that affects this region of Italy since several years [X. fastidiosa, perhaps acting synergistically with the plant’s own resistance mechanisms, as suggested for the olive cultivar FS17 [X. fastidiosa-susceptible cultivar (“Cellina di Nardò”) as control for comparison. Our sampling strategy followed a full factorial scheme, including the separation of endo + epiphytic microbiota and endophytic only (by surface-sterilizing a subset of samples), the differentiation between branches and leaves, and the analysis of Xf-infected and -uninfected plants. Both Prokaryotic and Fungal microbiota were analyzed. To the best of our knowledge, no data have been available so far in the literature on the total microbiome associated with Xf-infected and Xf-uninfected olive trees in Salento. In this work, we aimed to characterize the microbiota associated with the al years . So far,al years ,39,40, bal years ,41,42. Ivar FS17 . Plant mXf-infected and -uninfected samples of our study were collected from two areas within the same natural park, at 3 km distance from each other. This was a strategical choice, due to the higher risk to select false-negative samples in orchards in which positive plants were previously detected compared to sampling in orchards in which the pathogen was not yet ascertained. This event is not uncommon for an erratically distributed pathogen with a long period of latency [The  latency . HoweverX. fastidiosa, especially in the branches, confirming previous reports [Pseudomonas, Sphingomonas, and Methylobacterium among bacteria, and Acremonium, Aureobasidium, and Sarocladium, among fungi. Interestingly, Archaea were not detected, although the primers used in this work are able to amplify archaeal 16S rRNA genes [O. europaea cultivars from the Mediterranean basin, including “Leccino”, and found abundant Archaea in all of them. However, these plants were all grown at a single agricultural site in Spain, and therefore this abundance might have been determined by the local soil and environmental conditions.Our samples showed an erratic distribution of  reports ,26,41. T reports ,44. DomiNA genes  analyzedXf-infected “Cellina di Nardò” samples appeared severely dysbiotic, especially for the leaf endophytic bacteria. Here the endophytic bacteria were dominated by Ammoniphilus, an obligate oxalotrophic bacterium that requires high concentration of ammonium to grow [Xf-infected “Cellina di Nardò” is linked to the advanced status of the disease in our samples, where high amount of ammonium might have accumulated due to tissue decay. The nitrogen level in the soil where Xf-infected plants grew is good (1.92 g kg−1); however, it is unlikely that a high concentration of ammonium in plant tissues could have derived directly from soil nitrogen, otherwise we would have observed a prevalence of Ammoniphilus also in the Xf-infected “Leccino” samples. Xf-infected “Leccino”, instead, showed a bacterial assemblage and structure similar to that of the Xf-uninfected samples, and this was particularly evident for the leaf endosphere. Considering that this is the primary site of X. fastidiosa infection, the stability of the endophytic microbiota in the leaves of Xf-infected “Leccino” appears very relevant and promising for the biological control of X. fastidiosa. Dysbiosis is the unbalanced microbial status associated to several diseases in humans and animals [X. fastidiosa infection contributes to the maintenance of a good healthy status of the plant. In fact, it is known that the plant microbiome provides several ecological services important for the maintenance of the host’s fitness and health [Ralstonia solanacearum [X. fastidiosa.In our study,  to grow . To the  animals ,47. Howed health , within  good 1.9 g kg−1; d health , as it wnacearum . Moreovenacearum ,51. As sX. fastidiosa infection or modifies its output effect for the plant.The microbiota of “Leccino” also showed a higher diversity and equitability with respect to those of “Cellina di Nardò”. Again, this difference was especially evident for bacteria of both, endo + epiphytic and endophytic microbiota . MicrobiXf-infected samples , in our study the detected Pseudomonas OTUs were taxonomically related mainly to different species, such as P. aeruginosa, P. stutzeri and, at a less extent, other Pseudomonas spp. . We hypothesize that these “Leccino”-specific taxa could interact with X. fastidiosa in “Leccino”, and therefore the next step was to perform a co-occurrence analysis to assess potential interactions in the endophytic microbial network.The total fungal microbiota appeared very diverse, but no clear difference could be seen in the  samples D. The enhizobium , Burkholeriaceae , Sphingongomonas , and Entrobacter . Massilire level ,61; howeal genus , the latX. fastidiosa is potentially interacting with several species of bacteria and fungi in the leaf endosphere of Xf-infected “Leccino”. Although we were particularly interested in negative correlations, which could indicate a direct inhibition of X. fastidiosa, the positive correlations could point to beneficial species that grow together with the pathogen (therefore being positively correlated) but at the same time modify its metabolism and reduce its pathogenicity. To perform this analysis we used only the endophytic samples from “Leccino” (and excluded ones of “Cellina di Nardò”) for two reasons: First, X. fastidiosa was consistently detected only in “Leccino”  . Olive trees showing symptoms of OQDS were grown in neighboring “Leccino” and “Cellina di Nardò” orchards. Asymptomatic plants were grown in neighboring orchards of both cultivars, at a distance of about 3 km from the symptomatic plants’ orchards. The two sampling areas lie on a flat region without dividing or surrounding orographic elements , with an even climate . PhysicoX. fastidiosa was assessed by real-time PCR [X. fastidiosa, checking for symptoms caused by natural infection of Spilocaea oleaginea and Pseudomonas savastanoi pv. savastanoi during the 12 months before sampling. According to [Botryosphaeria dothidea [Colletotrichum spp., C. acutatum and C. gloeosporioides [Diplodia seriata [Phaeomoniella chlamydospore [Phaeoacremonium aleophilum, and P. parasiticum [Phytophthora spp. [Verticillium dahliae [The presence of time PCR  in 2016–rding to , the predothidea , Colletoorioides , Diplodi seriata , Phaeomoydospore , Phaeoacasiticum ,80, Phytora spp. , and Ver dahliae .X. fastidiosa positive and negative plants (5 plants per cultivar/infection status) were selected according to lower severity (= 1) for P. savastanoi pv. savastanoi (plants without symptoms of olive knot were not available in the area) and negative response to diagnostic test for the other pathogens , while each leaf sample consisted of a pool of 25 leaves. Xf-infected or -uninfected); third letter = habitat . A progressive number from 1 to 5 was added to identify the replicate. The prefix “Endo” was added to the samples that were subsequently surface-sterilized, to analyze the endophytic microbiota only. Examples: Endo-LHB-1 = the first replicate of “Leccino” Xf-uninfected branches, endophytes; CIL-2 = the second replicate of “Cellina di Nardò” Xf-leaves, total microbiota (epiphytes + endophytes). In total, 80 samples were analyzed: 2 cultivars*2 infection status (Xf-infected vs. -uninfected)*2 plant habitats (branches or leaves)*2 community type (epiphytic + endophytic vs. endophytic only)*5 replicates, according to a full-factorial scheme.Samples were named according to the following identification code: first letter = cultivar ; second letter = infection status  was transferred into an extraction bag  and 4 mL of extraction buffer  were added. Sample homogenization was performed by using a semi-automatic homogenizer  at 50% maximum speed. DNA extraction was performed according to Edwards et al.  with somX. fastidiosa detection by TaqMan real-time PCR protocol with XF-F/R primers and XF-P probe [−1 dilution of DNA extracted from 1 g of leaf petioles or branches, 12.5 μL of Master Mix (Applied Biosystems), 400 nM of forward and reverse primers, 200 nM of TaqMan probe, and ultrapure DNase/RNase-free water  in a total volume of 25 μL. The cycling conditions were as follows: an initial denaturation step at 95 °C for 10 min, followed by 40 cycles of 95 °C for 15 s and 60 °C for 1 min, with the final dissociation at 95 °C for 15 s, 60 °C for 30 s, and 95 °C for 15 s. All symptomatic and asymptomatic plants were singularly tested each year (2016–2018). Trees were considered Xf-uninfected when leaf samples were negative to every X. fastidiosa assay carried out in 2016-2018. In regard to Xf-infected plants, X. fastidiosa concentration from “Leccino” and “Cellina di Nardò” samples were inferred by the standard calibration curve, using Cqs from qPCR as described by [The isolated DNA was used as template for -P probe . Reactioribed by  (Table SAACTTTYRRCAAYGGATCWCT-3′) and ITS4-Fun (5′-TACGGTAGCAGAGACTTGGTCT-CCTCCGCTTATTGATATGCTTAART-3′) [CCTAYGGGDBGCWSCAG-3′) and 806R (5′-TACGGTAGCAGAGACTTGGTCT-GGACTACNVGGGTHTCTAAT-3′) for prokaryotes [The extracted DNA was also used as the template to perform a two-step amplification protocol in which the core PCR primer and the adaptors CS1 (forward) and CS2 (reverse) were included in a single oligonucleotide at the 5′ end of each primer to allow multiplexing with the Fluidigm Access Array System . The oligonucleotide sequences were (core PCR primer in bold) 5.8S-Fun (5′-ACACTGACGACATGGTTCTACA-AART-3′)  for fungkaryotes . A mixtukaryotes ,87.® GT PCR Master Mix, Takara Bio, Inc., Japan), 1.0 µL FWD primer (0.2 µM), 1.0 µL REV primer (0.2 µM), 1.25 mPNA blocker (0.25 µM), 1.25 pPNA blocker (0.25 µM), and ultrapure DNase/RNase-free water  to final volume of 25 µL. PCR was performed in 2720 Thermal Cycler , using the following program: initial denaturing at 94 °C for 3 min, followed by 30 or 35 cycles (for prokaryota or fungi) of denaturation at 94 °C for 15 s, PNA clamping at 75 °C for 10 s, primer annealing at 58 °C for 10 s (both primer pairs), extension at 72 °C for 60 s, followed by a final extension at 72 °C for 10 min and by cooldown to 4 °C. Briefly, the PCR was carried out in 25 µL of reaction mixture, using 50 ng of DNA, 12.5 µL 2× PCR master mix  under the project number PRJEB32050.The sequence datasets generated during and analyzed during the current study are available in the European Nucleotide Archive (ENA) repository , “Cultivar” , “Habitat” , and their interactions. The “Type of microbiota”  was also tested, however, only for its main effect, since the endophytes are a subpopulation of the total microbiota and their diversity is obviously lower. Beta-diversity (distance between samples’ microbiome structures) was computed separately on total community and endophytes, by using Bray–Curtis distances; the effects of the investigated factors were statistically assessed with the Adonis test [Xf-infected vs. -uninfected) at alpha-level = 0.05. All p-significance values were corrected for multiple testing using the false discovery rate (FDR) adjustment according to Benjamini-Hochberg [Statistical comparison of alpha diversity between samples was performed with SPSS version 20 , using factorial ANOVA and Duncan’s post hoc test at alpha-level = 0.05. Normality of distribution and heteroscedasticity were tested with the Shapiro–Wilk and the Levene’s tests, respectively. The factors considered were as follows: “Infection status”  are sensitive to outliers and robust to compositionality, while correlation measures (Pearson and Spearman) are biased by compositionality and robust to outliers [p-values; this method draws unbiased networks with interconnected nodes close each other and single-linked ones placed apart [To identify significant patterns of correlated bacteria in the re CoNet , which ire CoNet . Non-rarre CoNet . A prelioutliers , only edoutliers , additioed apart . “Hub” ned apart , were ided apart , based oed apart ,107.http://apps.cytoscape.org/apps/legendcreator). Adobe Photoshop CS6  was used to assemble and label the final figures.Legends of the network were created with the Cytoscape-App “Legend creator” , in contrast with the strong dysbiosis observed in the X. fastidiosa-susceptible cultivar “Cellina di Nardò”.In this work, we performed a large-scale observational study of the olive tree microbiome in a X. fastidiosa.The microbiota of “Leccino” trees are also more diverse and include cultivar-specific bacterial taxa that appear to interact, directly or indirectly, with X. fastidiosa infection. Our results represent a first step toward the identification of microbial species or consortia with potential biotechnological applications for the biological treatment of the Olive Quick Decline Syndrome in Salento. Our data could be used to perform a targeted isolation of candidate microorganisms of interest to be tested against X. fastidiosa under experimental conditions.The maintenance of a healthy microbiota and the presence of cultivar-specific microbes might support the resistance of “Leccino” to


P < 0.001, except for RBC, P = 0.446). These differences were also observed in four α-thalassemia subgroups (P < 0.001) and were associated with the number of defected genes. Similarly, in five β-thalassemia genotypes, HbF, HbA, RBC, MCV, MCH and NBRCs were presented differently (P < 0.05). Additionally, the trends in RBC, Hb, and HCT changes in three α-thalassemia subgroups  and β+/β+ fetuses’ MCV, MCH, and RDW levels with gestation age were opposite to those of normal fetuses. We compared the distribution of hematological parameters in fetuses affected by most genotypes of thalassemia, as well as their trends in relation to gestational age for the first time, which is a good reference for future studies and prenatal diagnostic practices. The investigated hematological parameters are also valuable in diagnosing and differentiating thalassemia.The aim of this study was to retrospectively compare hematological parameters among normal, α-, and β-thalassemia fetuses between 17 and 38 weeks of gestation. Pregnant women at risk of having fetuses with thalassemia major and underwent cordocentesis for prenatal diagnosis were recruited. Fetal cord blood samples were collected from 249 fetuses for hematological and DNA analysis. Fetuses were divided into subgroups according to thalassemia DNA genotypes. The average and gestational age of subjects were 27.95 ± 5.78 years and 27.78 ± 3.57 weeks, respectively. The distribution of α-thalassemia, β-thalassemia, and normal cases was 67.87%, 19.68%, and 12.45%, respectively. Significant differences in almost all the hematological parameters  were observed in three groups ( According to the specific defects of the relevant globin genes, thalassemia is classified into two major types, namely α- and β-thalassemia. The severity of the disease is dependent on the degree of imbalance in the quantity of the globin chains. Fetuses with thalassemia major, the most severe form of thalassemia, will either die or be born with serious complications, which heavily threaten the health of both the fetuses and the mothers. Thalassemia mainly occurs in tropical and sub-tropical areas such as Mediterranean countries, the Indian subcontinent, the Middle East, North Africa, and Southeast Asia3. In China, the overall prevalence of α-thalassemia and β-thalassemia was reported as 7.88% and 2.21%, respectively6. Therefore, it is a considerable burden to families of thalassemia major patients and their societies. To relief from such burden, effective measures to prevent and control prevalence of thalassemia are absolutely vital.Thalassemia is a medical condition caused by mutations or deletions of globin genes, which results in abnormal hemoglobin (Hb) formation, causing red blood cell embrittlement and hemolytic anemia disease0-thalassemia  carriers or β-thalassemia minor to prevent the development of thalassemia major in the prevalent regions2. Among these, prenatal diagnosis is essential because the detection of fetal cases provides direct evidence for whether and how the fetuses are affected. Currently, extensive knowledge and experience have been accumulated in the prenatal diagnosis of thalassemia, but little is known about the distribution of red blood cell indices in the thalassemia fetuses. Although previous studies have shed light on the hematological values in normal fetuses between 18 and 30 weeks of gestation, fetuses with α- and β-thalassemia and their subgroups related to severity, were not included in the analysis8. Moreover, in a study where red blood cell indices such as Hb, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), and mean cell hemoglobin concentration (MCHC) were investigated among normal, α-thalassemia-1 trait9 , and Hb Barts fetuses, it revealed that these basic data are valuable in differentiating the affected fetuses among couples at risk10. Therefore, it is worthy to explore the role of hematological indices  in α- and β-thalassemia and their different genotypes.Nowadays, several programs, including carrier screening, molecular diagnostics, and prenatal diagnosis have been applied to screen αThe aim of this study was to cast light on the differences in hematological values of fetuses with α- and β-thalassemia and their subgroups with varying severity between 17 and 38 weeks of gestation. Besides, the effect of gestational weeks on these hematological values was also observed.P = 0.069) and gestational age (P = 0.192) among the three groups (data not shown).A total of 247 pregnant women with 248 fetuses were recruited in this study. The average age and gestational age were 27.95 ± 5.78 years and 27.78 ± 3.57 weeks, respectively (data not shown). The ratio of α-thalassemia, β-thalassemia, and normal cases was 67.87% 169/249), 19.68% (49/249), and 12.45% (31/249), respectively. The detailed distribution of different thalassemia genotypes is shown in Table 69/249, 12, Hb Barts, Hb CS, Hb Portland, Hb Epsilon4, Hb Gower1, HbH, and other HbF variant  among different types of thalassemia and normal fetuses.Fetuses with α-thalassemia presented several Hb fractions including HbF, HbA, HbAnt Table . As showP = 0.003 and P < 0.001, respectively, data not shown). It is well known that the hematological parameters of a fetus change with different gestational ages. To control for this factor, the α-thalassemia major  group was selected randomly to match the gestational age with other groups .Significant differences were observed in hematological parameters including Hb, HCT, MCV, MCH, MCHC, RDW, and percentage of NRBCs among the three groups  in Thailand10. This difference may be due to the different gestational age ranges of the recruited subjects between their study (18–22 weeks) and our study (17–38 weeks). Some Hb Barts disease cases may have been overlooked due to the narrow gestational age range. Moreover, differences in the prevalence of thalassemia in different populations and sample sizes could also contribute to increase in incidence13. In our study, as for the genotype, 14 different α-thalassemia genotypes and 17 different β-thalassemia genotypes were found. Interestingly, a rare β28/βIVS-II-672 genotype was also found. Consistent with Ketong et al.’s study6, the most common α- and β-globin gene mutations were --SEA and CD41/42, respectively.In this study, the distribution of Hb Barts disease was 40.96% (102/249). The incidence of Hb Barts disease was relatively higher than that in the study performed by Kasemsri et alP < 0.001, Table 14 the proportion of HbF decreased with the severity of α-thalassemia, while the level of HbA, Hb Barts, Hb Portland, and Hb Epsilon4 increased. Therefore, since the percentage of HbF, HbA, and Hb Barts were significantly different among α-thalassemia, β-thalassemia, their different genotype subgroups, and normal fetuses in our study, and they also correlated with the number of affected genes, HbF, HbA, and Hb Barts is valuable in diagnosing and differentiating thalassemia.As for the hemoglobin fractions, because β-thalassemia fetuses were rarely researched, our study showed for the first time that significant a difference was observed in the percentage of HbF in five different β-thalassemia genotypes (P < 0.05). Among them, RBC were increased with the type of defected β-thalassemia gene, which indicates the potential role of these parameters in differentiating the severity of β-thalassemia. Similarly, in terms of RBC, Hb, HCT, MCV, MCH, MCHC, and RDW, significant differences were noticed between four different α-thalassemia subgroups (P < 0.001), and they all increased with the number of defected genes, except RDW which declined with the severity of defected genes. Hence, these indices could distinguish different α-thalassemia genotypes. Interestingly, our study showed for the first time that in α-thalassemia, although the same number of genes were affected, the hematological parameters presented differently between groups with mutation and deletion defects. As shown in Table When comparing the hematological parameters between fetuses of different genotypes, first, between fetuses with five different β-thalassemia genotypes, significant differences were observed in the percentage of RBC, MCV, MCH and percentage of NRBCs  were opposite to those of normal fetuses, while other subgroups were consistent to normal groups. In fetuses affected with β-thalassemia, the trends in changes of all the red blood cell indices were the same as those of normal fetuses, except for the MCV, MCH, and RDW levels of the β+/β+ subgroup. Our study supplied comprehensive information about the development of hematological parameters in different types and severities of thalassemia.Hematological parameters in the normal groups were observed to change with gestational age regularly and their changing trends were consistent with those in previous studiesA limitation of this study is that the sample size is limited. As most thalassemia prevention and control programs, the pregnant women with high risk of having thalassemia major fetuses would be advised to undergo chorionic villus sampling or amniocentesis instead of cordocentesis in the first or second trimester. Therefore, the number of women having cordocentesis for prenatal diagnosis of thalassemia is limited. In addition, the subjects of this study were from Guangdong province only, subjects from more districts are needed in future studies.To conclude, there are several strengths in this study. First, as far as we know, this is the first study that investigated the distribution of so many hematological parameters in fetuses affected by four α-thalassemia genotypes, five β-thalassemia genotypes, and normal fetuses, as well as the trends in changes with gestational age, which can be a good reference for future studies and prenatal diagnostic practices. Additionally, this study found that HbF, HbA, and Hb Barts have the value of diagnosing and differentiating thalassemia, as well as the RBC, MCV, MCH and percentage of NRBCs in β-thalassemia and RBC, Hb, HCT, MCV, MCH, MCHC, RDW in α-thalassemia. Lastly, we have shown for the first time that even with the same number of defected α-genes, the manifestation of fetuses with mutation defects was more severe than that of groups with deletion defects in fetuses. The hematologic studies among normal, α-, and β-thalassemia fetuses from this study supplied comprehensive information about the development of hematological parameters in different types and severities of thalassemia and may serve as a reference range for future studies or even clinical prenatal diagnosis applications.All the pregnant women who were at risk of having fetuses with thalassemia major and underwent cordocentesis for prenatal diagnosis at the Guangdong Women and Children Hospital, Guangzhou, China, from 2014 to 2019 were recruited. This amounted to a total of 248 women with 249 fetuses. The experimental process for the subjects recruited in this study is shown in Fig. 17 (2016) and consistent with Implementation Rules for Prenatal Diagnosis Technology Management published by Guangdong Provincial government (http://wsjkw.gd.gov.cn/gkmlpt/content/2/2131/post_2131319.html#2531).All steps were performed according to the Declaration of Helsinki and informed consents were collected from all the participants. This research was approved by the Medical Ethics Committees of Guangdong Women and Children’s Hospital and Prenatal Diagnosis (Technology) Ethics Committee of Guangdong Women and Children’s Hospital. The prenatal diagnosis operations were performed under the ISUOG Practice Guidelines of invasive procedures for prenatal diagnosisCordocentesis was performed by experienced perinatologists under the guidance of real-time ultrasound equipment  with transabdominal 3.5 MHz curvilinear transducers. About 1.5 mL of fetal cord blood was collected from each subject for hematological and DNA analysis.After mixing by inversion of the tubes, blood cell indices were measured from approximately 1 mL fetal cord blood with a Sysmex XN-1000 automated hematological analyzer , including red blood cell count (RBC), Hb levels, Hematocrit (HCT), MCV, MCH, MCHC, red cell distribution width (RDW), and the proportion of nucleated red blood cells (NRBCs).After dilution of 18 μL of fetal cord blood with 90 μL hemolytic agent , the Hb fractions of the mixture was analyzed with a CAPILLARYS 2 automatic analyzer (Sebia).18 and has been commercialized as the Thalassemia (α/β) Gene Diagnostic kit . Mutations that were outside the detection range of the suspension array were detected by gene sequencing. All fetuses were divided into three groups: normal group; α-thalassemia; and β-thalassemia. Moreover, the latter two groups were further divided into four subgroups, namely the (-α/αα), (--/αα), (--/-α), and (--/--) genotype and five subgroups, the (β+/βN), (β0/βN), (β+/β+), (β0/β+), and (β0/β0) genotype, respectively.All the fetal blood samples were subjected to a suspension array assay which was previously developed by our teamP < 0.05 was considered statistically significant.Normally distributed data are expressed as mean and standard deviation (SD), and the rest of the data are presented as median and interquartile range. Comparisons among three or more groups of the normally and informally distributed data were analyzed using the ANOVA test and Kruskal–Wallis test, respectively. Moreover, comparisons of mean and median differences between 2 groups were analyzed using Student's t test and Mann–Whitney test, respectively. Statistical analyses were carried out with SPSS software version 20.0.  and