Brucella causes persistent infections in various mammalian species. To survive and replicate within macrophages, these bacteria must be able to withstand oxidative stresses and express the type IV secretion system (T4SS) to evade host immune responses. The extracytoplasmic function (ECF) sigma factor system is a major signal transduction mechanism in bacteria that senses environmental cues and responds by regulating gene expression. In this study, we defined an ECF σ bcrS and its cognate anti-σ factor abcS in Brucella melitensis M28 by conserved domain analysis and a protein interaction assay. BcrS directly activates an adjacent operon, bcrXQP, that encodes a methionine-rich peptide and a putative methionine sulfoxide reductase system, whereas AbcS is a negative regulator of bcrS and bcrXQP. The bcrS-abcS and bcrXQP operons can be induced by hypochlorous acid and contribute to hypochlorous acid resistance in vitro. Next, RNA sequencing analysis and genome-wide recognition sequence search identified the regulons of BcrS and AbcS. Interestingly, we found that BcrS positively influences T4SS expression in an AbcS-dependent manner and that AbcS also affects T4SS expression independently of BcrS. Last, we demonstrate that abcS is required for the maintenance of persistent infection, while bcrS is dispensable in a mouse infection model. Collectively, we conclude that BcrS and AbcS influence expression of multiple genes responsible for Brucella virulence traits.The intracellular bacterial pathogen IMPORTANCEBrucella is a notorious intracellular pathogen that induces chronic infections in animals and humans. To survive and replicate within macrophages, these bacteria require a capacity to withstand oxidative stresses and to express the type IV secretion system (T4SS) to combat host immune responses. In this study, we characterized an extracytoplasmic function sigma/anti-sigma factor system that regulates resistance to reactive chlorine species and T4SS expression, thereby establishing a potential link between two crucial virulence traits of Brucella. Furthermore, the anti-sigma factor AbcS contributes to Brucella persistent infection of mice. Thus, this work provides novel insights into Brucella virulence regulation as well as a potential drug target for fighting Brucella infections. Brucella, a genus that contains at least 10 recognized species   . T4SSs am (T4SS) –14. In Bl models , 16, ande models –19. BrucB operon , 72, 73.B operon , quorum-B operon , and hisB operon . More T4Escherichia coli: the primary σ70 family, which directs mainly the transcription of housekeeping genes, and the structurally unrelated σ54 family, which is responsible for gene transcription in response to environmental cues , and based on the presence or absence of these conserved domains and regions, they are classified into four distinct groups, i.e., groups 1 to 4. Group 4 alternative σ factors, also known as extracytoplasmic function (ECF) σ factors, lack both σ1 and σ3 and contain only σ2 and σ4 domains. During the process of DNA double-strand separation, σ2 performs base-specific interactions with the −10 promoter element, while σ4 employs a helix-turn-helix motif to bind to the −35 element  requires a sigma factor (σ), which is responsible for the direct recognition of promoter elements and stimulation of initial steps in RNA synthesis . Sigma ftal cues . σ70 fam element , 26.E in Brucella , B0022 was designated bcrS (Brucella RCS resistance sigma factor), B0023 was designated abcS (anti-Brucella RCS resistance sigma factor), and B0021-B0019 was designated bcrXQP.The ECF16 subgroup, which is particularly present in alphaproteobacteria, is related to SigF from  regions . Here, wgions , and its downstream DUF1109 family protein B0023, with 211 aa , which is usually engaged in binding to the −10 motif and core RNAP, and region 4 , which is usually used for binding to the −35 motif. In addition, a predicted three-dimensional (3-D) structure displayed two distinct domains  reductase system MsrQP, which reduces MetSO to methionine in proteins under HOCl stress  and its mutants in tryptic soy broth (TSB) medium. The results demonstrated that the ΔB0022 and ΔB0021-B0019 mutants displayed similar growth kinetics compared to the wild type; and ΔB0023 mutant grew slightly slower than the wild type during the exponential phase (P < 0.05), and yet it caught up with the wild type as these entered the stationary phase (P < 0.01), whereas the survival of ΔB0022 and ΔB0023 was statistically indistinguishable from that of the wild type. At 4.5 h after stress, the survival of the ΔB0022 mutant was lower than that of the other strains (P < 0.01). On the other hand, the survival of all strains was comparable upon treatment with H2O2 (data not shown). Therefore, these results show that B0022 and B0021-B0019 contribute to the resistance of B. melitensis to HOCl. Thus, we renamed B0022 bcrS (Brucella RCS resistance sigma factor), B0023 abcS (anti-Brucella RCS resistance sigma factor), and B0021-B0019 bcrXQP.B0021, encoding a methionine-rich MXKDX repeat protein, is immediately upstream of B0022 and homologous to MsrX in f itself . B0020-Bl stress . To testry phase . Figure bcrXQP in the soil bacterium A. suillum, namely, yedYZ-mrpX, are functionally and transcriptionally linked  of bcrXQP, 5′ rapid amplification of cDNA ends (5′-RACE) was performed . The TSS and the deduced −10 and −35 motifs for the bcrXQP operon are shown in GCGAAA) and −35 (TGTAAC) motifs are highly concordant with the ECF16 σ consensus recognition sequence [(T/C)GTAAC-N17-CGAA]  in bcrXQP transcription levels relative to the wild type, whereas in the presence of HOCl, the reduction was even greater (at least 2-fold reduction from that in the absence of HOCl). Lacking abcS dramatically augmented the bcrXQP transcription levels . The −10 (GCGAAT) and −35 (TGTAAC) motifs were deduced accordingly, and they are highly similar to those in the PbcrXQP promoter region . As shown in bcrS-lacZ expression was significantly downregulated in the ΔbcrS mutant, whereas PbcrS-lacZ expression was upregulated by ∼5-fold in the ΔabcS mutant compared to the wild type. As expected, introduction of the bcrS locus into the ΔbcrS mutant substantially increased PbcrS-lacZ expression. Similar trends were obtained when HOCl was present. Moreover, the expression of PKmR-lacZ was not responsive to the addition of HOCl or deletion of bcrS or abcS. Together, these data demonstrate that the bcrS operon can be induced by HOCl and is autoregulated and further corroborate that abcS is a negative regulator of bcrS. These results were consistent with our transcriptome sequencing (RNA-seq) analysis (see below).A number of ECF σ factors autoregulate their own expression, which likely occurs to augment their responses to extracytoplasmic stimuli , 35. SinbcrS-abcS system, we wanted to identify all the genes whose transcription might be regulated by bcrS and abcS. Total RNA from the wild type (WT) and ΔbcrS and ΔabcS mutants grown with or without HOCl was isolated, and their transcriptomes were obtained by RNA sequencing. A 2-fold change was selected as the cutoff. Comparative transcriptomic analysis indicated that 36, 63, 13, 45, and 240 genes were differentially expressed in the ΔbcrS versus WT in TSB (group A), ΔabcS versus WT in TSB (group B), ΔbcrS versus WT in HOCl (group C), ΔabcS versus WT in HOCl (group D), and WT HOCl versus WT (group E) binary comparisons, respectively (abcS affected more genes than deletion of bcrS (group B versus group A and group D versus group C) and 75% of DEGs in group A were also upregulated in group B. More genes were affected by bcrS deletion in the absence of the inducer HOCl than in the presence of it , bhuA (encoding a heme transporter), RS13430 (the BMEII0584 ortholog encoding the periplasmic component of an fbpA ABC-type Fe3+ transport system), and dhbCE  are related to iron/heme/siderophore metabolism. nosR and its downstream nosZ are part of a cluster responsible for nitrous oxide reductase production. The ubiDX operon (involved in ubiquinone biosynthesis), cycA (encoding a cytochrome c family protein), cybB3 (encoding a cytochrome b family protein), azu1 (encoding pseudoazurin), and RS11975 (encoding a cytochrome P450-containing protein) are related to electron transporters. Compared to the wild type, genes encoding T4SS VirB1 to VirB6 components and vceA encoding a T4SS effector were uniformly downregulated by at least 2-fold (log2fold change < −1) in the ΔbcrS mutant, while 10 T4SS genes (virB1 to virB10) plus vceA were uniformly downregulated by 2- to 4-fold in the ΔabcS mutant.14–21-CGAA consensus was searched in the M28 genome using the Pattern Locator program  and splenomegaly (P < 0.001) at 3 weeks postinfection, whereas the spleen weights and bacterial colonization in mice infected with ΔbcrS and ΔbcrXQP mutants were comparable to those infected with the wild type. Thus, these data demonstrate that B. melitensisabcS is required for the maintenance of chronic infection but not for the establishment of an infection in mice.To characterize the roles of Brucella species, namely, B. melitensis, B. abortus, and B. suis, revealed that each genome contains two ECF σ factors: σE encoded by rpoE1, which is conserved in alphaproteobacteria , and an investigation of six σ factors in B. melitensis 16M showed that loss of BMEII0072 does not alter Brucella infection of murine macrophages but impairs chronic infection of mice . Similarly, the anti-σ VreR of P. aeruginosa affects virulence gene expression independent of its cognate σ VreI  than those of several other ECF16 σ members. Strikingly, genes encoding Msr are shared by at least three other ECF16 σ regulons , although these organisms lead distinct lifestyles. These commonalities imply that ECF16 σ-controlled Msr could be a very efficient mechanism in overcoming oxidative stresses. BcrS upregulates bhuA, which encodes a heme transporter, dhbCE, which is involved in 2,3-DHBA biosynthesis, and electron transporters  produced positive results. This may be attributable to (i) AbcS being a transmembrane protein with several hydrophobic segments, so fusing a protein to it should properly expose the interaction domain (otherwise an interaction will not occur), or (ii) escentus , which bsporters  and 6. Trophores  and enerrophores .bcrXQP is considerably induced by HOCl and involved in HOCl resistance  or in TSB (Difco) at 37°C in a 5% CO2 atmosphere. All work with live Brucella was performed in a biosafety level 3 (BSL3) facility. E. coli strains  were grown in Luria-Bertani (LB) medium at 37°C. When needed, antibiotics were added at the following concentrations: kanamycin, 50 μg/ml; ampicillin, 100 μg/ml; and chloramphenicol, 25 μg/ml  using a MultiS one-step cloning kit . The resulting recombinant plasmid was transformed into M28 electrocompetent cells, and the transformants were selected on TSA supplemented with kanamycin. Candidate deletion mutants, which were resistant to kanamycin but sensitive to ampicillin, were further subjected to PCR amplification and Sanger sequencing for validation. The bcrS complemented strain was generated by transforming into the ΔbcrS mutant a pBBR1MCS4 variant carrying the bcrS locus driven by its native promoter  or PKmR promoter (in BcrS autoregulation study), while the abcS complemented strain was generated by transforming into the ΔabcS mutant a pBBR1MCS4 variant carrying the abcS locus driven by the PKmR  constitutively expressed promoter.In-frame deletion mutants of escribed , 59. BrilacZ vector plasmid and its derivatives used for transcriptional fusion were constructed according to a previously reported protocol . Translational fusions to T25 at its N and C termini were constructed by ligating CDS regions into pKNT25 and pKT25 plasmids, respectively, using XbaI and EcoRI sites, and translational fusions to T18 at its N and C termini were constructed by ligating CDS regions into pUT18 and pUT18C plasmids, respectively, using XbaI and EcoRI sites. All plasmid constructs were verified by DNA sequencing. Different combinations of plasmids, as illustrated in d-galactopyranoside), 0.5 mM IPTG  and appropriate antibiotics to assay the β-galactosidase activities. A combination of plasmids pKT-Zip and pUT-Zip was used as a positive control, and empty vectors were used as negative controls. Blue colonies indicate substantial β-galactosidase activities, suggesting significant protein interactions, whereas white colonies indicate very low β-galactosidase activities, suggesting no detectable protein interactions  of ∼0.5, and then equal portions of a bacterial culture were collected and treated with the inducers HOCl (1 mM) and H2O2 (1 mM) or phosphate-buffered saline (PBS) as a control. After incubation for 10 min with shaking, the cultures were immediately mixed with RNAprotect reagent (Qiagen) and subjected to RNA extraction by TRIzol reagent according to the manufacturer’s instructions (Invitrogen). RNA samples were then treated to remove genomic DNA and reverse transcribed to cDNA using a PrimeScript RT reagent kit with gDNA Eraser (Clontech).RNA extraction and reverse transcription-PCR (RT-PCR) and quantitative reverse transcription-PCR (qPCR) analyses were performed as previously described , 59. B. bcrP and bcrQ, bcrQ and bcrX, bcrP to bcrX, and bcrS and abcS, yielding amplicons of ∼850, ∼700, ∼1,900, and ∼620 bp, respectively. RNA was extracted as described above from M28 grown in TSB. RNA that was not reverse transcribed served as a negative control, while genomic DNA served as a positive control  and an ABI Quant 5 thermocycler (Applied Biosystems). Melting curve analyses were performed after each reaction to ensure amplification specificity. Fold changes in transcript levels were calculated using the 2−ΔΔCT method  according to the manufacturer’s manual  was used for each NextGen sequencing library, which was constructed according to the manufacturer’s protocol . rRNA was depleted from total RNA using the Ribo-Zero rRNA removal kit for bacteria . Sequencing of the libraries was performed using a 2 × 150 paired-end configuration on an Illumina HiSeq platform according to the manufacturer’s instructions (Illumina), and image analysis and base calling were conducted using HiSeq Control Software (HCS) + OLB + GAPipeline-1.6 (Illumina) on a HiSeq system. The sequences were processed and analyzed, and raw reads were assessed by fastQC and further treated by Cutadapt (version 1.9.1). Clean reads were then aligned to the B. melitensis M28 genome (GenBank accession numbers NC_017244.1 and NC_017245.1) using bowtie2 . Reads were counted using Htseq (v0.6.1). Differential gene expression analysis was then performed using DESeq2 (v1.6.3) with R version 3.3.2 following a standard workflow. All genes with a |log2(fold change)| > 1 and a Benjamini-Hochberg adjusted P value  of <0.05 were considered differentially expressed. Tables S1 to S5 list differentially expressed genes in the five groups, and raw data are available at the National Microbiology Data Center .RNA-seq analysis was performed using a standard protocol with minor modifications . BrieflyBrucella strains were grown on TSA for 72 h, and cells were then harvested and resuspended in sterile PBS at an OD600 of ∼0.02. Bacterial suspensions were diluted 1:100 into 10 ml of TSB and allowed to grow until reaching an OD600 of ca. 0.4 to 0.5. The OD600 values were normalized, and two equal portions were taken, with one portion treated with HOCl at a final concentration of 2 mM and the other with PBS as a control. These cultures were then incubated at 37°C with shaking at 210 rpm, collected at various time points, and spotted onto TSA to determine the CFU counts. The CFU counts at time point 0 were set as 100%. Survival rates were determined as (CFU at a time point after stress/CFU at time point 0) × 100%.HOCl survival assays were performed as previously described . All BrulacZ transcriptional fusions were grown overnight in TSB at 37°C, and then the cultures were diluted 1:100 into 10 ml of fresh TSB and allowed to grow until reaching an OD600 of ca. 0.4 to 0.5. Multiple equal portions of bacterial cultures were collected and treated with HOCl at the indicated concentrations or with PBS as a control. These cultures were then incubated at 37°C with shaking for 60 min before being subjected to β-galactosidase activity measurements as previously reported , followed by Dunnett’s multiple-comparison test, was used to analyze the differences between various mutants and the wild-type strain , and all other binary comparisons were analyzed by a Student Brucella were approved by the Chinese Ministry of Agriculture.Handling and care of mice were performed according to the Beijing Administration Guidelines for the Use of Laboratory Animals. The entire protocol with respect to animal experiments was approved by the Review Board of Harbin Veterinary Research Institute and by the Animal Care and Use Committee of Heilongjiang Province [SYXK(H)2006-032]. Procedures regarding the infection of BALB/c mice with The data that support the findings of this study are openly available at the National Microbiology Data Center .


Recent studies have shown that some inflammatory markers are associated with the prognosis of solid tumors. This study aims to evaluate the prognosis of glioma patients with or without adjuvant treatment using the systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), and platelet-lymphocyte ratio (PLR).All patients who were diagnosed with gliomas at the first and second affiliated hospital of Guangxi Medical University between 2011 and 2020 were included in this study. The optimal cutoff value of SII, NLR, and PLR was determined by X-tile software program. We stratified patients into several groups and evaluated the progression-free survival (PFS) and overall survival (OS) of SII, NLR, and PLR during the period of pre-surgical, con-chemoradiotherapy, and post-treatments. Multivariate Cox regression analyses were performed to detect the relationships between OS, PFS, and prognostic variables.9/L, 2.9 × 109/L, and 123.2 × 109/L, respectively. Patients who are pre-SII < 781.5 × 109/L had better PFS (P = .027), but no difference in OS. In addition, patients who had low pre-NLR (<2.9 × 109/L) meant better OS and PFS. PLR after adjuvant treatments (post-PLR) was significantly higher than pre-PLR (P = .035). Multivariate analyses revealed that pre-SII, pre-NLR were independent prognostic factors for OS , while pre-PLR was an independent factor for PFS .A total of 67 gliomas patients were enrolled in the study. The cutoff values of SII, NLR, and PLR were 781.5 × 10High pre-SII or high pre-NLR could be prognostic markers to identify glioma patients who had a poor prognosis. In Yang et al study, patients with grade III glioma or glioblastoma had a poor prognosis, the median overall survival (OS) for patients with anaplastic glioma was 37.6 months, whereas only 14.4 months for glioblastoma patient.Glioma is one of the most common primary intracranial tumors. According to the 2016 revision of the World Health Organization (WHO) classification of central nervous system tumors, gliomas can be classified into 4 grades. Grades I and II belong to low-grade gliomas, while grades IIII and IV are high-grade gliomas.,4 Bambury et al found that neutrophil-lymphocyte ratio (NLR) is a marker of systemic inflammatory response and a poor prognostic factor in many malignancies such as colon, bladder cancer, and prostate cancer. The authors pointed out NLR > 4 was an independent factor for a poor prognosis of glioblastoma. Wang et al quantified the prognostic value of platelet-lymphocyte ratio (PLR), NLR, and lymphocyte-to-monocyte ratio (LMR) based on the IDH mutation status and pointed out low NLR was a better prognosis in IDH-wt glioblastoma group, while PLR was predictive of survival in patients with glioblastoma, pGBM, and IDH-wt GBM groups. Besides, the preoperative systemic immune-inflammation index (SII) is also being confirmed as a biomarker for predicting the survival and quality of life in patients with esophageal squamous cell carcinoma, small cell lung cancer, and gastric cancer.–10 Li et al also compared the inflammatory and nutritional markers between preoperative neoadjuvant chemotherapy (NACT) and after NACT. The author found that NACT could decrease some inflammatory markers, whereas initial NLR, anemia, and LMR were poor prognosis in locally advanced gastric cancer. As we know, the prognosis of different grades of gliomas are varying. At present, the diagnosis of glioma is based on molecular types, which are of certain significance for the prognosis of glioma patients. However, it is almost impossible to re-biopsies to obtain molecular pathology during the treatment. If some tumor biomarkers which are related to prognosis can be acquired in peripheral blood, it will play an important role in selecting adjuvant therapy and evaluating the prognosis of glioma patients. SII, NLR, and PLR are based on the ratio of neutrophil, lymphocyte, and platelet counts in peripheral blood, and it is easy to get. So if these biomarkers have strong prognostic power for glioma patients, it could bring great benefits for patients with glioma. In previous studies, inflammatory markers such as SII, NLR, and PLR have been proven to be related to the prognosis of gliomas, but the relevant data are few, and more studies are needed to confirm the value of SII, NLR, and PLR as prognostic factors in glioma. Therefore, we aimed to explore the prognostic value of SII, NLR, and PLR at baseline and during the treatments. We defined patients into several groups to investigate the prognosis in gliomas.Several studies have shown that chronic inflammation may be a major characteristic of the tumor microenvironment and may accelerate tumor progression or metastasis.22.1We retrospectively collected all the patients who were diagnosed with gliomas between 2011 and 2020 at the first and second affiliated hospital of Guangxi Medical University. The inclusion criteria for patients were as follows: 1) all the glioma patients were confirmed by pathological examination, including oligodendroglioma, astrocytoma, anaplastic astrocytomas, olidendromas, and glioblastma; 2) patients ranged in age from 16 to 75 years old; 3) all the patients were initially treated without previous chemotherapy or radiotherapy; and 4) Karnofsky performance status (KPS) ≥70 scores. The exclusion criteria were as follows: 1) patients who had recently pyrexia (axillary ≥73.2°C); 2) any other form of active infection that may affect SII; 3) patients with chronic inflammatory disease; 4) patients who died from treatment-related complications;5) a history of tumor radiotherapy or chemotherapy;6) with second primary malignant disease; and 7) incomplete follow-up data. The clinical characteristics of all enrolled patients were shown in Table 2.2Clinical data were collected from medical records, including name, age, gender, preoperative-neutrophil, preoperative-lymphocyte, con-lymphocyte, con-platelet, post-neutrophil, post-lymphocyte, post-platelet, tumor size, tumor site, histopathological tumor grade, dose of gross tumor volume of tumor bed, dose of Clinical Tumor volume 1, dose of Clinical Tumor volume 2, WHO grade, adjuvant treatment methods , and cycles of adjuvant chemotherapy. The endpoints included the OS, progression-free survival (PFS). The calculated methods were as follows: SII = platelets × neutrophils/lymphocytes, NLR = neutrophils/lymphocytes, and PLR = platelets/lymphocytes.2.3t test when they were normally distributed, while non-normally distributed we used Wilcoxon rank-sum to test. We used X-tile software program  as described previously to decide the cutoff value of pre-SII, NLR, and PLR. Differences of SII, NLR, PLR between pre-treatments, post-treatments, and recurrent-treatments (rec-treatments) were analyzed by paired T tests. Survival curves were drawn by graphpad prism 5. According to the cutoff value, patients were divided into the following groups: pre-SII ≥ 781.5 group, pre-SII < 781.5 group; pre-NLR ≥ 2.9 group and pre-NLR < 2.9 group; and pre-PLR≥123.2 group and pre-PLR < 123.2 group. The characteristic of each group was shown in Table P value <.05 was considered a statistically significant difference.All the data were analyzed by SPSS version 22.0 software (IBM), X-tile software program , and graphpad prism 5. Continuous variables were compared by Student 33.19/L, 2.9 × 109/L, and 123.2 × 109/L, respectively.A total of 67 glioma patients  were enrolled in the study. The median followed-up time was 14.7 months (1.0–96.6 months). The median age of the patients was 42 years (16–17years). Within this study, 28 patients (41.8%) had low-grade gliomas and 39 patients (58.2%) had high-grade gliomas. In the research, there were 9 patients (13.4%) who received postoperative radiotherapy, 23 patients (34.3%) received chemoradiotherapy, only 3 cases (4.5%) of chemotherapy, and 32 cases (47.8%) of surgery without adjuvant therapy. By the time of the last follow-up, 32 patients were still alive  and 35 patients had died . As determined by X-tile software program, the cutoff values of pre-SII, NLR, and PLR were 781.5 × 103.29/L and 46 patients <781.5 × 109/L; 20 patients’ pre-NLR ≥ 2.9 × 109/L and 47 patients <2.9 × 109/L. For pre-PLR, there were 38 patients’ pre-PLR ≥ 123.2 × 109/L, while 29 patients <123.2 × 109/L. Most of the pre-SII < 781.5 × 109/L were male, while the female had a similar distribution in the 2 groups . In the pre-PLR groups, the majority of females’ PLR before treatments were ≥123.2 × 109/L, whereas males were <123.2 × 109/L .According to the cutoff values, we defined SII, NLR, and PLR into several groups. There were 21 patients’ pre-SII ≥ 781.5 × 103.3P = .035), while SII and NLR were not significantly decreased  for those with pre-SII < 781.5 × 109/L. And the PFS of patients with pre-SII < 781.5 × 109/L was significantly better than patients with pre-SII ≥ 781.5 × 109/L  whereas the pre-PLR was a significant independent factor of PFS  had higher NLR than patients with grade I to III gliomas. Another study from Poland came to similar conclusions. As for PLR, the prognostic value of PLR still controversial in previous studies. Some studies have reported that increasing PLR may decrease OS in solid tumors such as lung cancer and gastric cancer.–27 In our research, we found the preoperative PLR level was an independent factor for PFS. Therefore, we can detect high-risk patients from all gliomas patients by test PLR before treatment to receive adequate adjuvant chemotherapy. Wang et al and Han et al also got similar results like us, while Sun et al found the value of PLR was less effective than NLR when acting as an independent prognostic factor in prostate cancer. Our results demonstrated that low preoperative NLR suggested a poor OS and PFS, which was consistent with some studies.–32 Furthermore, our study reconfirmed that SII, NLR, and PLR were associated with prognosis in patients with glioma.With the rapid development of molecular biology, the diagnosis and treatments of gliomas mainly depend on molecular biomarkers. However, due to the difficulty in obtaining tumor specimens during treatment, so we cannot monitor the molecular markers all the time. Most inflammatory markers are readily available, and it is convenient to detect the related biomarkers during the treatment period. Studies have shown that there is a certain relationship between inflammation and cancer, and immune cells play a predominant role in the of process inflammation, promoting tumor growth, angiogenesis, and metastasis.Our study also has a number of limitations. Firstly, we included a limited number of patients, only 67 patients, including WHO I–IV gliomas. Secondly, adjuvant treatment methods covered chemoradiotherapy, chemotherapy, and radiotherapy, which may affect the outcomes. Third, the follow-up time was short and long-term survival cannot be assessed.5In conclusion, our research demonstrated that high pre-SII or high pre-NLR could be prognostic markers to identify glioma patients who had a poor prognosis. More studies should be carried out to verify the conclusions.This work was finished by the team at the Department of Radiation Oncology and Department of Neurosurgery, The Second Affiliated Hospital of Guangxi Medical University, and Thank you to all my colleagues.Data curation: Yuanfu Luo, Renzhi Deng, Qiulu Zhong, Xiangde Li, Xueyuan Chen, Sha Tao, Zhoubin Feng, Jiayi Liu, Yiyun Huang.Formal analysis: Yuanfu Luo, Qiulu Zhong, Danjing Luo.Project administration: Wenqi Liu, Jian Li.Writing – original draft: Yuanfu Luo and Renzhi Deng.Data curation: Yuanfu Luo, Renzhi Deng, Qiulu Zhong, Danjing Luo, Xiangde Li, Xueyuan Chen, Sha Tao, Zhoubin Feng, Liu Jiayi, Yiyun Huang, Jian Li.Formal analysis: Qiulu Zhong, Danjing Luo.Project administration: Wenqi Liu.Supervision: Wenqi Liu.Writing – original draft: Yuanfu Luo, Renzhi Deng.


The relationship between moderate alcohol drinking or other alcohol drinking patterns such as frequency, beverage type, and situation of drinking and cognitive function is not sufficiently clear in older people. The purpose of this study was to investigate the association between alcohol drinking patterns and cognitive function in community-dwelling Japanese people aged 75 and over.This study was a cross-sectional design based on a prospective cohort study called the SONIC study. Subjects were older people aged 75-77 or 85-87 who voluntarily participated in 2016-2017. Drinking information was collected for daily drinking frequency, daily drinking intake, beverage type, and non-daily drinking opportunity. Cognitive function was measured using the Japanese version of the Montreal Cognitive Assessment (MoCA-J). Other potential confounding factors evaluated were age, sex, medical factors, and psychosocial factors. An analysis of covariance was performed to evaluate the MoCA-J score relative to drinking frequency or alcohol intake. Multiple regression analysis was performed to investigate the association between beverage type or non-daily drinking opportunity and the MoCA-J score.The final number of participants analyzed was 1,226. The MoCA-J score for participants who reported drinking alcohol 1–6 days/week was significantly higher than that for those who reported drinking none or every day. No significant difference in the MoCA-J score was observed relative to daily alcohol intake. In terms of beverage type, wine was associated positively with the MoCA-J score. Non-daily drinking opportunity was also associated positively with the MoCA-J score.Moderate-frequency drinking, wine consumption, and non-daily drinking opportunities were associated with higher cognitive function in community-dwelling Japanese aged 75 and over. Further longitudinal studies are needed to clarify the causal relationships.The online version contains supplementary material available at 10.1186/s12877-022-02852-8. Dementia, which is especially common among older people, affects about 50 million people worldwide and is a major public health problem , 2. BecaExcessive drinking is commonly cited as a risk factor for dementia and cognitive decline , 5. HoweTherefore, the purpose of this study was to investigate the association between drinking patterns and cognitive function in community-dwelling Japanese people aged 75 and over to provide evidence to contribute to the optimal drinking recommendations in this super-aged society.This study was a cross-sectional design based on a prospective cohort study called the SONIC  study, an ongoing study since 2010 with follow-ups every 3 years [3)”. The alcohol concentration of each beverage was set at 5% for beer, 25% for Japanese spirits, 15% for sake, 12% for wine, and 42% for whiskey. Daily alcohol intake was classified into the following categories: “None (0 g)”, “Moderate (>0 g and <40 g)”, “Moderate to Excessive (≥40 g and <60 g)”, and “Excessive (≥60 g)” for men. For women, the threshold values used were half as high as those used for men [The drinking patterns, such as daily drinking frequency, daily alcohol intake, beverage type, and the situation of drinking: non-daily drinking opportunity, were collected by doctors, nurses, public health nurses, and dietitians. The daily drinking frequency was collected as follows: “None”, “<1 day in 2–3 months”, “1 day in 2–3 months”, “1 day/month”, “2–3 days/month”, “1 day/week”, “2 days/week”, “3 days/week”, “4 days/week”, “5 days/week”, “6 days/week”, and “Every day”. The data were then classified into four categories: “None”, “<1 day/week”, “1–6 days/week”, and “Every day”. Daily alcohol intake was calculated using a measuring cup after considering the beverage type. The formula for calculating alcohol intake was: “alcohol concentration (%) × amount (mL) × density , which was administered by trained gerontologists and psychologists. The MoCA-J, which is based on the MoCA, is a tool for sensitively detecting mild cognitive impairment in older Japanese people, and the score was evaluated from 0 to 30 and adjusted for years of education (1 point added for 12 years or less) , 23. A hMedical factors were collected from interviews and blood samples, which were obtained by doctors, nurses, public health nurses, and dietitians. Smoking was evaluated as currently smoking or not. Stroke was assessed as positive if there was previous medical documentation. Hypertension was defined as positive if an individual had a systolic blood pressure ≥140 mmHg, had a diastolic blood pressure ≥90 mmHg, or was receiving antihypertensive treatment . DiabetePsychological and social factors were collected by gerontologists and psychologists. Mental health was measured using the Japanese version of the WHO Five Well-Being Index (WHO-5-J), and a score of 13 or higher was evaluated as good .Social factors were evaluated, including living alone (or living with someone else), frequency of going out , education , and economic status .t-test were performed for continuous variables. To evaluate the average MoCA-J score relative to daily drinking frequency or daily alcohol intake, analysis of covariance (ANCOVA) adjusted for potential confounders was performed. Multiple comparison tests for the categorical variables treated as dummy variables were used with the Tukey-Kramer tests for post hoc analysis. Univariate regression analysis was performed to examine the relationship between beverage type or non-daily drinking opportunity and the MoCA-J scores. Multiple regression analysis adjusted for potential confounders was performed using the drinking pattern variables. In each multivariable analysis, the interaction with sex was examined concerning the drinking pattern variable and the MoCA-J score. p-values <0.05 were considered statistically significant. All analyses were performed using SPSS Statistics version 27 .Descriptive data are shown as a percentage (%) or mean ± standard deviation (SD). For comparisons between characteristics, chi-square test or Fisher’s exact test were used for categorical variables, and one-way analysis of variance (ANOVA) or The number of participants analyzed in this study was 1,226. Table Figure Figure p<0.01) and non-daily drinking opportunity  significantly positively correlated with the MoCA-J scores  in the associations between all drinking patterns and cognitive function  For drinking frequency, the cognitive function of moderate-frequency drinkers, such as those who drank 1–6 days/week, was higher than those for individuals who reported their consumption as none or every day; (2) For beverage type, the cognitive function of wine drinkers was higher than that of non-drinkers; and (3) For drinking opportunity, the cognitive function of those who engaged in non-daily drinking opportunities was higher than that of those who did not. Daily alcohol intake was not associated with increased cognitive function.Few studies have reported an association between cognitive function and drinking frequency among the alcohol drinking patterns of the older population . This isExcessive drinking has a negative effect on dementia and cognitive function , 5. HoweFor the type of alcohol consumed, we found that only wine was positively associated with cognitive function Table . This reIn addition, non-daily drinking opportunity was associated with higher cognitive function. Similar to the relationship between drinking frequency and cognitive function, social factors such as communication with others may have positively influenced cognitive function , 31. TodThis study has several limitations. First, a survival bias effect may play a role in the association between alcohol drinking patterns and cognitive function. The subjects in this study were older people who voluntarily participated, and they tended to be healthier than the average older Japanese population because they had enough physical function to participate in a site survey. Second, drinking information was evaluated by interviews. Self-reporting methods of typical weekly alcohol intake are useful for epidemiological studies, but not perfect , 40. ForIn conclusion, we observed that moderate-frequency drinking, wine consumption, and non-daily drinking opportunities were associated with higher cognitive function in community-dwelling Japanese aged 75 and over. Although the association between drinking frequency, situation of drinking, beverage type and cognitive function is new findings, the reverse association between drinking and cognitive function is often concerned. Further longitudinal studies are needed to clarify the causal relationships.Additional file 1:  Figure S1. Details for daily drinking frequency.Additional file 2: Table S1. Comparison of characteristics relative to daily alcohol intake.Additional file 3: Table S2. Comparison of characteristics relative to wine consumption.Additional file 4: Table S3. Comparison of characteristics relative to non-daily drinking opportunity.Additional file 5: Table S4. Comparison of characteristics relative to sex.


An increase in blood brain barrier permeability commonly precedes neuro-inflammation and cognitive impairment in models of dementia. Common methods to estimate capillary permeability have potential confounders, or require laborious and subjective semi-manual analysis.Here we used snap frozen mouse and rat brain sections that were double-immunofluorescent labeled for immunoglobulin G  and laminin-α4 (capillary basement membrane). A Machine Learning Image Analysis program (Zeiss ZEN Intellisis) was trained to recognize and segment laminin-α4 to equivocally identify blood vessels in large sets of images. An IgG subclass based on a threshold intensity was segmented and quantitated only in extravascular regions. The residual parenchymal IgG fluorescence is indicative of blood-to-brain extravasation of IgG and was accurately quantitated.Automated machine-learning and threshold based segmentation of only parenchymal IgG extravasation accentuates otherwise indistinct capillary permeability, particularly frequent in minor BBB leakage. Comparison with Existing Methods: Large datasets can be processed and analyzed quickly and robustly to provide an overview of vascular permeability throughout the brain. All human bias or ambiguity involved in classifying and measuring leakage is removed.Here we describe a fast and precise method of visualizing and quantitating BBB permeability in mouse and rat brain tissue, while avoiding the confounding influence of unphysiological conditions such as perfusion and eliminating any human related bias from analysis. Alterations in cerebral capillary blood-brain barrier (BBB) are increasingly recognized as central in maintaining optimal brain and cognitive functioning . A numbeex vivo measurement of cerebral extravasation of exogenous dyes such as fluorescein and Evans Blue that are injected intravenously. Potential confounders include dosing; differential binding of dyes between treatment groups; metabolic effects, including on the vascular endothelium. Another major confounder of exogenous dye tracing is the requirement to in vivo perfusion wash. Acknowledging the importance to remove blood from the vasculature to measure the cerebral extravasation of dye, perfusion of animals introduces another layer of unphysiological conditions, which may confound the detection of subtle BBB alterations. These include the use of non-physiological perfusing solution , artificial intravascular flow and pressure, and prolonged application of anesthesia  and albumin may serve as preferential surrogate markers of BBB-permeability, however require either pre-perfusion to remove intravascular fluorescence, or tedious identification and demarcation of intravascular vs. parenchymal fluorescence of protein. To overcome these limitations, here we describe a novel automated method with machine-learning to achieve highly sensitive detection of cerebral extravasation of endogenous IgG, not requiring perfusion of animals prior to tissue collection.Snap frozen brain hemispheres were sectioned using a cryotome and fixed with paraformaldehyde , followe® CoreTM i5-4670 4- Core 3.0 GHz, 8 gb RAM, 250 gb free hard disk space.Materials required for immunofluorescent slide staining and image capturing are indicated in their respective sections. Quantitation was performed on ZEN Blue 3.1 software (Carl Zeiss Microscopy GmbH) requiring modules: ThirdPartyImport, Intellesis, and ImageAnalysis. Minimum system requirements: Intelad libitum access to the standard rodent chow and water. We have used previously established models of mild-severe BBB disruption in our laboratory for the validation of the new method developed . The animals had eveloped . The aniAnimals were anesthetized with 4% gaseous isofluorane and exsanguinated via cardiac puncture followed by cervical dislocation. The left hemisphere of brain tissue was immediately removed and frozen in liquid nitrogen, then stored at –80°C. Brain tissue was sliced into 20 μm coronal sections using a Leica CM1520 cryostat onto poly-L-lysine coated slides (Trajan P7511) and either immediately frozen or immediately fixed in 4% paraformaldehyde (Sigma P6148) for 10 min on ice.Samples were washed 3× in 0.1 M phosphate buffered saline (PBS) pH 7.4. Fixed brain sections were incubated overnight at 4°C in 1:200 goat anti-laminin-α4  in Antibody Signal Enhancement Buffer  as per the protocol described by Slides were then washed 3× in PBS and incubated with donkey anti-goat Alexa555 (Abcam ab150130) 1:500 for 2 h at room temperature and washed 3× in PBS, followed by a further 1 h incubation with anti-mouse Alexa488 (Thermo Fisher A-11029) 1:100 or anti-rat Alexa 488 (Thermo Fisher A-11006) in PBS. Slides were washed 1× in PBS, then nuclear counterstained with DAPI. Slides were washed 3× in PBS, then mounted in an anti-fade mounting medium.Stained sections were imaged using an UltraVIEW Vox Spinning Disk Confocal Microscope (PerkinElmer) with a Hamamatsu C9100-50 EMCCD coupled with Volocity image acquisition software. A minimum of five 3-dimensional images consisting of 21 x 2D z-stacks from each mouse or rat  were captured at random positions throughout the hippocampus and cerebral cortex. To address the Intellisis module’s incapability to perform segmentation on 3D images, Maximum Intensity Projections were produced using ImageJ’s “Z-project” processing function. This also significantly reduced data storage and processing burden. XY points for this purpose were designated the “time” parameter, enabling quantitation of multiple images within one TIFF file. Images stored in the Zeiss native.czi format do not require conversions described here.Pre-requisites:•The following modules within ZEN 3.1 or above are licensed: ImageAnalysis, Intellesis, Third Party Import.•Images are in a.czi file format. For images acquired on other platforms, import into FIJI/ImageJ using the BioFormats Import plugin and save as TIFFs. TIFFs can be imported into ZEN using the option File > Import/Export > BioFormats Import.•Multiple Z-stack images can be flattened using the “Z Project” tool in FIJI/ImageJ or the “Make Maximum Intensity Projection” tool in the orthogonal view in ZEN.•A “training set” of about 10 images has been derived from the experimental dataset.1. Train an Intellesis Model on the laminin- α4 single channel imagea.Create new Intellesis Trainable Segmentation model and click “Start Training.”b.Select Segmentation: “Basic Features 33” and Postprocessing: “Conditional Random Field.”c.Click “Import Images” to open the single channel laminin a4 training set images. Choose “Training Mode” as “Single Channel,” and select the Laminin-a4 channel.d.Note: Imported sample images must have pixel and channel match to the experimental images.e.Under the “Labeling Options” tab, use the paint brush, eraser and brush width tools to label some input pixels as “Object” for Laminin- α4 or “Background.”f.Note: It is not required to label the whole object. Carefully label boundaries between vessels and background, focusing especially on weaker signal.g.Click “Train and Segment” to create a “Segmentation” channel. Check where the software has made mistakes and then re-label misclassified regions. Check the “Confidence” channel—label the regions of low confidence indicated by dark grey pixels.h.Repeat steps 1d. to 1e. on the training set until the Intellesis model classifies accurately. Click “Finish.”2. Determine an IgG signal threshold intensity using the “Profile” tab on the left side of the image viewer.For example, if background values never exceed 2,000 and IgG signal is around 5,000, choose a threshold of 3,500.3.Create a new Image Analysis Setting to automatically quantitate IgG.a.In the “Image Analysis” tab click “Setup Image Analysis.”b.Step 1: Add and name the following classes:i.Class 1: Lamininii.Class 2: Extravascular segmentiii.Class 2 Subclass: Extravascular IgGNote: Ensure the correct target Channels for each class are selected.c.Step 2: Leave “Frame” as “Interactive.”d.Step 3: Automatic Segmentation:i.Click the Laminin class and change “Segment by Global Thresholding” to “Intellesis Trainable Class Segmenter” and select Model as created in Step 1.ii.Select “Model Class” as “Object.” Select “Fill holes.”iii.Click the Extravascular Class and repeat Step 3di.iv.Select “Model Class” as “Background.” Do not select “Fill Holes.”v.Select “Binary,” choose “Erode” and set “Count” as 1 to make a 1 pixel buffer around blood vessels.vi.Click the IgG class and set the “Threshold” to the value determined in Step 2 with 0% tolerance.vii.Select “Smoothing,” choose “Lowpass” and set “Size” as 3 to reduce IgG signal noise.e.Step 4: Region Filter:i.2 to exclude unspecific signal.It is not necessary to filter segmented objects, but it may be required to set a minimum area of approximately 5 μmf.Step 5: Leave “Interactive Segmentation” as “Interactive.”g.Step 6: Features:i.To record vascular density, click the Sum Laminin class (Σ) and select “Edit.”ii.Add features “Image Name,” “Area,” “Area Percentage,” and “Area Frame.”iii.Click the Sum IgG class (Σ) and select “Edit.”iv.Add features “Intensity Sum of Channel X” and “Intensity Pixel Count of Channel X.”v.For all features, select “Copy to Sum Laminin class (Σ)” to consolidate all data into one table.h.Step 7: Results Preview.i.Check segmentation and results table, unchecking “Show Objects” in the Analysis tab to compare with the raw image.ii.Click Finish4.Open experimental datasets and click “Analyze” in the “Analysis” tab to run the Image Analysis Program.5.To extract the data table, select “Sum Laminin class (Σ)” at the bottom “Analysis” Tab and click “Create Table.” This table can be copied into Microsoft Excel for collation and analysis.6.Check through images to confirm accurate segmentation and exclude unwanted data. Alternatively, click “Analyze Interactively” to adjust the analysis frame (step 2) or manually correct segmentation (step 5).7.Repeat steps 2–6 with the remaining experimental data set.Note: Batch Analysis can be done on multiple.czi files through the Processing tab > Batch > Batch Method > Image Analysis Program. Macros are also available to automatically construct data tables from batch analyses.All analysis was performed on a PC workstation (Intel Core (TM) i7-4770 CPU, 24 GB RAM, Windows 7 × 64).The assessment of subtle changes in cerebrovascular BBB permeability is increasing in importance in neuroscience research. The detection of such delicate breaches of the BBB remains elusive and may be confounded by various factors including the cellular effects of exogenous dye and extended use of anesthesia. Moreover, historical approaches to determine BBB permeability are laborious due to manual selection of extravasation and analysis is often subjected to human bias. Here, we present a new methodology of highly sensitive automated detection of BBB disruption in mouse and rat brain that may mitigate the aforementioned major confounding issues.We utilized laminin-α4 staining to precisely identify the microvascular network. The laminin-α4 subunit comprises laminin isoform 8 (new nomenclature laminin-411) and is a major subendothelial basal membrane component of animal CNS vasculature, including capillaries . This isThereafter, the ZEN Image Analysis protocol was applied to example images without IgG extravasation, mild leakage and severe leakage, to test whether the method can quantitatively analyze parenchymal IgG extravasation of varying degrees. As indicated in In biological specimen samples derived from preclinical models of neurodegenerative disorders, mild BBB leakage events are much more numerous than severe BBB leakage events, however, are visually less obvious. The marked boundary between the intraluminal and parenchymal environments of small capillaries defined by laminin-α4 masking, removes all ambiguity of IgG staining location which previously concerned investigators, and significantly improves the detection of subtle leakage patterns .We compared the new IgG quantitation method established in this paper to our conventional “manual” IgG quantitation protocol. Single-channel anti-mouse 488 images , 3A are Careful manual quantitation of large datasets can occupy a trained investigator for several weeks. The high-throughput method described in this paper allows researchers to identify modest IgG extravasation en masse with minimal user input . UtiliziThe reproducibility of the data is presented in The applicability of the automated selection of peri-vascular IgG extravasation in order to assess the BBB integrity was tested on several images from thalamus as presented in In conclusion, this machine-learning method produces a high-signal to noise ratio to measure BBB impairment and indeed, advantageous to perfusion of exogenous dyes such as Evans Blue, without altering physiological conditions which may affect BBB permeability. Laminin-α4 co-staining with IgG drastically accentuates the determination of subtle changes in BBB permeability which are otherwise undetectable, while facilitating expeditious and intelligent automated quantitative analysis. This method is significantly superior to other existing methods because it eliminates any potential bias by the investigator(s), avoids labourious image analyses/processing by the investigator(s), and is highly reliable and reproducible.The original contributions presented in the study are included in the article/The animal study was reviewed and approved by the Curtin Animal Ethics Committee.MN was involved in the conceptualization, data curation, formal analysis, and investigation and writing manuscript. JM contributed to the funding acquisition and resources and manuscript writing. MM validated the methodology. VL contributed to the funding acquisition, animal experiments and sample collection, and method development. RT developed the study concept, obtained the funding, supervised the project, and wrote the manuscript. 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.


The assays were optimized in terms of amplification efficiency and hybridization specificity, using mixtures of two genomic DNAs with different genotypes and percentages to simulate fetal and maternal circulating cell-free DNA (ccfDNA) at various gestational weeks. The two ddPCR assays were then applied to determine the fetal genotype from 52 maternal plasma samples at different gestational ages. The diagnostic outcomes were confirmed for all the samples by DNA sequencing. In the case of mutations inherited from the mother or from both parents, a precise dosage of normal and mutated alleles was required to determine the fetal genotype. In particular, we identified two diagnostic ranges for allelic ratio values statistically distinct and not overlapping, allowing correct fetal genotype determinations for almost all the analyzed samples. In conclusion, we have developed a simple and sensitive diagnostic tool, based on ddPCR, for the NIPT of β+IVSI-110 and β039 mutations paternally and, for the first time, maternally inherited, a tool, which may be applied to other single point mutations causing monogenic diseases.Non-invasive prenatal testing (NIPT) is based on the detection and characterization of circulating cell-free fetal DNA (ccffDNA) in maternal plasma and aims to identify genetic abnormalities. At present, commercial NIPT kits can detect only aneuploidies, small deletions and insertions and some paternally inherited single-gene point mutations causing genetic diseases, but not maternally inherited ones. In this work, we have developed two NIPT assays, based on the innovative and sensitive droplet digital PCR (ddPCR) technology, to identify the two most common β thalassemia mutations in the Mediterranean area (β Non-invasive prenatal testing (NIPT) is based on the detection and characterization of circulating cell-free fetal DNA (ccffDNA) in maternal plasma , using accffDNA comprises only a small portion 10–20%) of total circulating cell-free DNA (ccfDNA) in the last gestational weeks 0% of tot, increasIn addition, the amount of ccffDNA depends, besides the gestation period, on several other factors, such as maternal characteristics and body weight ,6,7, aneNIPT has been applied to determine fetal sex ,10, fetaAt present, commercial NIPT assays can detect only aneuploidies, fetal sex and small deletions or insertions, but not maternally inherited single point mutations ,24,25. In this context, innovative and sensitive approaches for NIPT of fetal single-gene point mutations are greatly needed. Recently, we have applied droplet digital PCR (ddPCR) technology to NIPT of Y chromosome at early gestational ages (prior to 7 weeks) for fetal sex determination. In particular, all maternal plasma samples were correctly determined for SRY gene detection using ddPCR even at very early gestational ages (4.5 weeks), achieving an unprecedented level of 100% accuracy . The ddPCR technology is based on the partition of nucleic acid samples in thousands of nanoliter-sized droplets by water–oil emulsion droplet technology, reducing costs, preserving precious samples and detecting rare DNA target copies with high sensitivity ,31.For all these reasons, in this study, we decided to validate the ddPCR technology for NIPT of two single point mutations causing β thalassemia. 0) or reduction (β+) of adult hemoglobin, inducing severe anemia [+IVSI-110 (a splicing site mutation) and β039 (a nonsense codon mutation) [β thalassemia is an autosomal recessive inherited blood disorder associated with the absence (βe anemia . The thee anemia . Patientutation) ,33.In this work, we have optimized and developed two ddPCR-based NIPT assays for the above-mentioned mutations  by fetal genotyping performed on 52 maternal plasma samples at different gestational ages . Blood samples from pregnant women were collected using test tubes containing EDTA anticoagulant after approval by the Ethical Committee (Prot. 67-2012) of University Hospital S. Anna, Ferrara  and the Thalassemia and Hemoglobinopathies Center, Laiko General Hospital, Athens (Greece). In all cases, informed consent was obtained and the experiments were conducted in agreement with the Declaration of Helsinki. A progressive number was assigned to each specimen to ensure the anonymity of the donor.g for 10 min at 4 °C without brake. Plasma was then carefully collected and centrifuged again at 2400× g for 20 min at 4 °C in order to completely remove platelets and precipitates. The resulting supernatant was collected and stored at −80 °C in aliquots, so as not to thaw them more than once.Plasma was prepared within 3 h from blood collection, according to the protocol previously described in the literature . Briefly® DNA Blood Mini Kit  according to the manufacturer’s instructions. Two 60 μL DNA elutions were performed.Genomic DNA was extracted either by 0.5 mL of blood or by two buccal swabs using the QIAampCollected DNA was checked by agarose gel electrophoresis, spectrophotometrically quantified and stored at −20 °C. UV spectrophotometric quantification was performed with a SmartSpec™ Plus spectrophotometer . The enzymatic digestion of genomic DNA was carried out using SspI restriction enzyme  in order to simulate ccfDNA. It was chosen because it is able to recognize and cut two regions of the β globin gene, upstream of the promoter and inside the second intron, respectively, leaving the region around the mutation intact to avoid interference in primers and probe hybridization. Each reaction had a final volume of 20 μL, containing 50 ng of genomic DNA, 1 U of SspI enzyme and 1× buffer G (Thermo Fisher Scientific). The reaction was incubated at 37 °C for 16 h and finally the enzyme was inactivated at 65 °C for 20 min.® DSP Virus Spin Kit (Qiagen), according to the manufacturer’s instructions. DNA elution was performed in 60 μL of AVE buffer.As previously reported , ccfDNA Synthetic oligonucleotides used as primers in polymerase chain reactions (PCR) and sequencing reactions were purchased from Sigma-Aldrich , while assays for ddPCR, containing differently labeled normal and mutated probes for each mutation under investigation, were purchased from Thermo Fisher Scientific and Bio-Rad. Sequencing reactions were performed using, as templates, β globin PCR products obtained from genomic DNA from healthy donors, healthy carriers, β thalassemia patients, future mothers or fathers or from babies. PCR amplification was carried out with the primer pairs BG1F (5′-GTGCCAGAAGAGCCAAGGACAGG-3′)–BG1R (5′-AGTTCTCAGGATCCACGTGCA-3′), BG2F (5′-GCCTGGCTCACCTGGACA-3′)–BG2R (5′-GTTGCCCAGGAGCTGTG-3′) and BG3F (5′-ACAATCCAGCTACCATTCTGCTTT-3′)–BG3R (5′-CACTGACCTCCCACATTCCCTTTT-3′). Each PCR was prepared in a final volume of 50 μL containing 1× ExTaq Buffer , 12.5 μM dNTPs, 150 ng PCR primers, 1.25 U/reaction ExTaq DNA polymerase (Takara Bio) and 100 ng or 30 ng of human genomic DNA obtained from blood or buccal swabs, respectively. After a first denaturation step at 94 °C for 2 min, 35 amplification cycles were performed, including denaturation at 94 °C for 30 s, annealing at 1–2 degrees lower than the melting temperature of primers for 30 s and elongation at 72 °C for a time proportional to the product length. At the end, reactions were maintained at 72 °C for 10 min.PCR products were analyzed by agarose gel electrophoresis before being purified from unincorporated PCR primers using the MicroClean reagent .® BigDye™ Terminator Cycle Sequencing Ready Reaction Kit (Thermo Fisher Scientific). A total of 45 amplification cycles were performed as follows: denaturation, 96 °C, 10 s; annealing, 65 °C, 5 s; elongation, 65 °C, 3 min. After purification of the reaction products using Sephadex™ G-50 Superfine , sequencing was finally performed by BMR Genomics , while the obtained sequence data were analyzed by Sequence Scanner, version 1.0  software.Sequencing reactions were carried out according to Sanger’s method  with the+IVSI-110  or β039  assays for detection of β+IVSI-110 and β039 mutations, respectively. The reactions were prepared in a final volume of 22 μL containing 1× ddPCRTM Supermix for Probes (no dUTP) (Bio-Rad), 1× β+IVSI-110 or 0.75× β039 assay and 1 ng of genomic DNA, 10 ng of genomic DNA mixtures or 8 μL of ccffDNA template. ddPCR was performed using the QX200™ Droplet Digital™ PCR system (Bio-Rad) and βTM AutoDGTM Droplet Generator (Bio-Rad) was then used to generate the emulsion droplets before amplification in an IQTM5 Multicolor Real-Time PCR Detection System (Bio-Rad) under the following conditions: 10 min at 95 °C, 45 (for β+IVSI-110) or 40 (for β039) cycles of a two-step thermal profile of 30 s at 94 °C and 1 min at the selected annealing and extension temperature, a final hold of 10 min at 98 °C, ramping rate 2 °C/s. The droplet fluorescence analysis was performed with a QX200TM Droplet Reader (Bio-Rad) in combination with QuantaSoft 1.3.2.0 software (Bio-Rad) for data acquisition and calculation of the absolute concentration of target DNA (in copies/μL of reaction) using Poisson distribution analysis [The QX200analysis .M:N) and the z-score were calculated, as previously described by Tan et al. [® Excel® software according to the equations and Poisson distribution models described in Tan et al. [After determining the numbers of positive droplets, negative droplets and double-positive droplets, to determine the disease status of a test sample, the ratio of the total copy number of the mutant allele to the total copy number of the wild-type allele , both the normal and the mutated alleles were amplified, and the M/N ratio was around 1 B,F; whilThe obtained M/N allelic ratios (Obt) are reported in +IVSI-110 or β039 mutation ddPCR assays. For each sample, parental genotypes were determined by sequencing of the β globin gene after genomic DNA purification from blood or buccal swab. Fifty-two plasma samples collected from pregnant women at different gestational ages were analyzed using β+IVSI-110/N , confirming that ddPCR is a robust, sensitive, efficient and reliable technology [In conclusion, for the first time, we have demonstrated that ddPCR technology can be used for NIPT of paternally inherited βchnology .The case of mutations inherited from the mother or both parents is more complex: a precise quantification of normal and mutated alleles in maternal plasma is required to evaluate whether there is a balanced or unbalanced M/N allelic ratio, according to the RMD approach described for the NIPT of monogenic diseases . When th+IVSI-110 (on the left side) and β039 (on the right side) mutations with a partner who is normal homozygous (in the upper part) or a carrier of the same maternal mutation (in the lower part).+IVSI-110, #49 for β039) are highly informative and interesting from a diagnostic point of view because they represent a real case in which the fetus could become a β thalassemia patient, e.g., sample #23.The samples for which both the parents are carriers of the same mutation . The relevant statistical analyses confirmed that the differences among the value populations obtained from different fetal genotypes were statistically significant.The distribution of M/N ratio values obtained from these samples is displayed in Indeed, as expected, the samples from women carrying heterozygous fetuses showed an M/N ratio around 1. On the contrary, the samples from women carrying normal fetuses, where the allelic imbalance is due to an under-representation of the mutant allele compared to the normal one , showed Finally, as regards the mutated homozygous fetal genotype, we found only one sample (#23) which gave an M/N ratio higher than 1 (1.40), as expected, but without the chance to obtain a statistical analysis, as for the two other genotypes.We further investigated the confidence level of the results by calculating the z-scores . An indeThe z-score classification was able to correctly assign 29 cases over 30. There was an incorrect classification for sample #45, showing the highest M/N ratio among the normal fetuses, and which the z-score classified as heterozygous , most prRecently, NIPT based on advanced technologies to investigate ccffDNA has allowed the determination of fetal sex, fetal rhesus D (RhD) genotyping, aneuploidies, micro-deletions and the detection of only paternally inherited monogenic disorders . An exam+IVSI-110 and β039 mutations, both maternally and paternally inherited. These data confirm that the ddPCR is a robust, sensitive, efficient and reliable technology to easily detect single point mutations. The second consideration supporting the novelty of our approach is that we have been able to perform NIPT using seventh week sampling. This remarkable timing has not been described so far for ddPCR-based NIPT. Actually, Sawakwongpra and colleagues described a ddPCR-based approach for the NIPT of β thalassemia, but they have been able to perform NIPT only from the 17th gestational week [In this article, we demonstrated for the first time that ddPCR technology can be used for the NIPT of βnal week . In addiWith respect to the aforementioned points, we would like to underline that the NIPT of maternally inherited mutations has been recently reported employing approaches other than ddPCR ,42. For +IVSI-110 and β039 thalassemia mutations, suitable for application at very early gestational ages, such as the seventh gestation week.In conclusion, in this study we report for the first time a simple, fast and sensitive NIPT approach for the identification of maternally inherited β


SD = 12.12)). A structured interview was developed and used during the pandemic to measure the patients’ perceived (A) fear of COVID-19, and (B) feelings of hopelessness, and (C) physicians’ use of empathetic and (D) clear language during the consultation. A multiple mediation model was tested, and the effects between males and females were also compared. Results: Empathetic and clear doctor–patient communication buffered the adverse effect of the fear of COVID-19 on hopelessness through a full-mediation model. The effects did not differ between males and females in the overall model but its indirect effects. Discussions: Patient-centered communication using empathy and clear language can buffer the adverse effect of the fear of COVID-19 and protect oncological patients from hopelessness during the pandemic. These findings might help to improve clinical oncological practice.Background: COVID-19 represents a threat both for the physical and psychological health of oncological patients experiencing heightened distress levels to which the fear of the virus is also added. Moreover, fear of COVID-19 could lead oncological patients to experience feelings of hopelessness related to their medical care. Patient-centered communication may act as a buffer against the aforementioned variables. This study aimed to test the role of doctor–patient communication in the relationship between fear of COVID-19 and hopelessness. Methods: During the COVID-19 pandemic, a sample of 90 oncological outpatients was recruited  and 50 females (55.6%), mean age = 66.08 ( In March 2020, due to the global spread of the coronavirus disease COVID-19), the World Health Organization declared the situation as a pandemic 9, the Wo.The COVID-19 virus can rapidly spread and it causes a potentially mortal acute respiratory syndrome ,4,5,6,7.Although effective preventive policies were adopted by countries worldwide , tDue to comorbidities with other medical conditions, some people were found to be at increased risk of morbidity and mortality for COVID-19 ,14, inclThe awareness of the increased vulnerability to severe COVID-19 syndrome, together with the lived experience of COVID-19 and its related consequences, have intensified both psychological difficulties and negative feelings already existing in oncological patients ,35,37,38In fact, as a consequence of generic advice given to people who are clinically susceptible to COVID-19, patients with cancer  had to deal with several changes in the management of their condition during the past few months ,40,41 inConsequently, regarding the contextual factors concerning the delay of medical treatments, COVID-19 may represent a hazardous trigger for oncological patients. Research further shows that fear of COVID-19 might be associated with feelings of hopelessness  as well Still, several studies suggest that a good doctor–patient relationship might act as a protecting (buffering) factor against cancer-related negative feelings, thus preventing the patients from the onset of further psychological issues and other medical issues related to non-adherence to treatment recommendations ,57,58,59Particularly, two important aspects seem to play a central role in PCC: empathy and clear language ,66,67,68Indeed, physicians’ ability to communicate with patients by expressing validation, empathy, and support  seems toClinician–patient communication presenting both empathy and clarity of language seems to calm oncological patients’ fears and to reduce both their anxiety symptoms and their feeling of hopelessness ,72,73,74Still, the research focused on the impact of doctor–patient communication on oncological patients’ negative feelings at the time of COVID-19 is scant ,72,75, aIn this scenario, the present research aimed to examine “if” and “how” two of the main characteristics of doctor–patient communication  might act as mediators (with a buffering effect) in the relation between fear of COVID-19 and hopelessness in a sample of oncological outpatients during the outbreak. Specifically, it was hypothesized that an empathetic and clear doctor–patient communication might buffer the relation between fear related to the COVID-19 pandemic and the consequent feeling of hopelessness in patients with cancer.An observational research design was used to investigate the psychological experiences that the oncological outpatients made of the COVID-19 pandemic at the beginning of the lockdown (from the third week of March 2020 to the second week of April 2020).n:q criterion” was used where n is the number of participants in the study and q is the number of model parameters to be estimated [The sample size was determined a priori by considering the statistical analyses used in this study (see designated section). In particular, the “stimated ,77,78,79An initial sample of 100 oncological outpatients was consecutively recruited at admission at the Department of Medical Oncology, Presidio Ospedaliero di Saronno, ASST Valle Olona, in Saronno (VA), Italy.Inclusion criteria for participating in the study were: (A) being over 18 years old; (B) being a native Italian speaker; (C) having received a diagnosis of cancer within the last 6 months; (D) having had a confrontation with the physician about cancer management during the COVID-19 pandemic; (E) providing signed informed consent. Participants were excluded from the study if they were: (F) unable to attend the clinical interview due to cognitive or speech impairments, and/or to upcoming medical commitments; (G) presenting critical/severe anxiety and/or depressive feelings; or (H) showing unusual physical distress/suffering. Moreover, each participant was an oncological outpatient, he/she should never be hospitalized for problems related to cancer and he/she should follow an intravenous therapy for cancer.According to the abovementioned criteria, 10 outpatients were excluded from the study.mean = 66.08, SD = 12.12, median = 67)). Considering the type of cancer, 32 patients had lung cancer (35.6%), 24 patients had breast cancer , 18 patients had gastrointestinal cancer (20%), 11 patients had urogenital cancer (12.2%), and 5 patients had oncohematological cancer (5.6%). Considering education level, 51 patients had a middle school diploma (56.7%), 32 patients had a high school diploma (35.6), and 7 patients had a bachelor/master’s degree (7.8%). Considering civil status, 71 patients were either in a relationship or married (78.9%), 10 patients were either separated or divorced (11.1%), 7 patients were widowed (7.8%), and 2 patients were single (2.2%). Considering working status, 47 patients were retired (52.2%), 24 patients were dependent workers (26.7%), 13 patients were entrepreneurs (14.4%) and 6 patients declared “other” (6.6%).The final sample comprised 90 participants  and 50 females (55.6%), aged from 30 to 89 years  independently conceived and listed 20 ad hoc items investigating 4 specific domains (4 items per domain) related to COVID-19 and PCC.Second, the two lists of items were merged and screened: item wordings were adjusted to the target population, and redundant items were removed. Then, a final list of 12 items (3 items per domain) was created and approved by the two expert psychologists. The final set of items were rated on a dichotomous scale: 0  and 1 (= true). No reverse items were retained.In more detail, the first and the second set of items aimed to investigate negative psychological feelings related to the medical condition of the patients  within the pandemic framework; namely, (I) the fear of COVID-19 and (II) hopelessness. The third and the fourth set of items aimed to investigate the patient’s perception concerning the communication he/she had with the physician regarding his/her oncological treatment; namely, (III) (perceived) empathic communication and (IV) (perceived) clarity of information received.Fear of COVID-19Considering your oncological disease, are you afraid of COVID-19?”). High values indicated high fear of COVID-19. For the present study, the expected a posteriori (EAP) reliability coefficient was equal to 0.715 and the KR-20 was equal to 0.878.The first set of three items aimed to evaluate the presence (or absence) of fear towards COVID-19 in comorbidity with the oncological problem . High values indicated high hopelessness. For the present study, the EAP reliability coefficient was equal to 0.622 and the KR-20 was equal to 0.878.The second set of three items aimed to evaluate the presence (or absence) of hopelessness towards COVID-19 in comorbidity with the oncological problem  Empathic communicationWas the doctor empathetic in communicating the management of cancer care during this period?”). Moreover, considering a possible difficulty in fully understanding the term “empathic” for some patients, a brief explanation of the term was provided by the psycho-oncologist during the interview. High values indicated the perception of high empathic communication. For the present study, the EAP reliability coefficient was equal to 0.630 and the KR-20 was equal to 0.762.The third set of three items aimed to assess the patient’s perception of empathy shown by the physician in communicating care management during the pandemic  Clarity of informationWas the doctor clear in communicating information relating to the management of cancer care during this period?”). High values indicated the perception of high clarity of information. For the present study, the EAP reliability coefficient was equal to 0.601 and the KR-20 was equal to 0.787.The fourth set of three items aimed to assess the patient’s perception of the clarity of the information received regarding cancer treatment during the pandemic.  during the initial psychological consultation. Patients’ responses were registered into an electronic report form.Statistical analyses were performed with the R statistical software ,86. withConsidering the novelty of the structured interview, the psychometric properties of its items were assessed. In more detail, given the binary response scale  as well as the (assumed) unidimensionality of each scale ,94,95,96Once the psychometric properties of the structured interview were assessed, instead of summing the items, the total score of each scale was computed by extracting their factor scores (FSs), which was used for statistical analyses ,101,102.r) was computed to evaluate the strength of the relationships between variables [In line with previous studies , before ariables ,103. A cariables ,103 and ariables ,63,104, First, a simple predictor-only model was specified: “fear of COVID-19” (X) was regressed on “hopelessness” (Y) : “fear of COVID-19” (X) was regressed on “hopelessness” (Y) through “empathic communication” (M1) : “fear of COVID-19” (X) was regressed on “hopelessness” (Y) through “clarity of information” (M2)  was regressed on “hopelessness” (Y) through “empathic communication” (M1) and “clarity of information” (M2)  , Model 1n = 40) and females (n = 50), a multi-group path analysis was not applicable to evaluate gender differences between (A) the two indirect model effect (fear of COVID-19 → empathic communication → clarity of information → hopelessness) and (B) the total model effects. Thus, in line with previous studies [R2, percentage, etc.) [Finally, considering the small, unbalanced sample between males  .As reported in Correlation analyses between FSs showed moderate-to-large associations between variables included in the mediation model. However, none of the reported values exceeded the recommended threshold of |0.80| . These r“civil status” was found on “fear of COVID-19” , “empathic communication” , “clarity of information” , and “hopelessness” .The multiple multivariate regression analysis showed no statistically significant effects of predictors  on the dependent variables. In more detail, controlling for other predictors, no statistically significant effect of the respondents’ (A) work status”, was found on “empathic communication” , “clarity of information” , and “hopelessness” , while a small effect was found for “fear of COVID-19” .In addition, controlling for other external variables, no statistically significant effect of the respondents’ (B) “ducation” was found on “fear of COVID-19”  and “hopelessness” ; while small effects were found for “empathic communication”  and “clarity of information” .Controlling for other external variables, no statistically significant effect of the respondents’ (C) “ep = 0.274), “empathic communication” , “clarity of information” , and “hopelessness” .Lastly, controlling for other external variables, no statistically significant effect of the respondents’ (D) “type of cancer” was found on “fear of COVID-19” , p < 0.001. Results are reported in Considering the first model , Model 1Model 2apath a1: β = −0.524 (SE = 0.061), p < 0.001. Moreover, “empathic communication” (M1) negatively predicted “hopelessness” (Y), path b1: β = −0.361 (SE = 0.135), p = 0.008 suggesting a buffering effect of “empathic communication”.Considering the second model (path c1: β = 0.374 (SE = 0.100), p < 0.001.At the same time, “fear of COVID-19” (X) was directly positively associated with “hopelessness” (Y), p = 0.013. Lastly, the total model effect was statistically significant: β = 0.563 (SE = 0.058), p < 0.001, thus suggesting a partially mediated path. The results are reported in Furthermore, the total indirect effect (fear of COVID-19 → empathic communication → hopelessness) was statistically significant: β = 0.189 (SE = 0.076), Model 2bpath a2: β = −0.365 (SE = 0.076), p < 0.001. Moreover, “clarity of information” (M2) negatively predicted “hopelessness” (Y), path b2: β = −0.350 (SE = 0.093), p < 0.001, also suggesting a buffering effect of “clarity of information”.Considering the third model (path c1: β = 0.435 (SE = 0.074), p < 0.001.At the same time, “fear of COVID−19” (X) was directly positively associated with “hopelessness” (Y), p = 0.010. Lastly, the total model effect was statistically significant: β = 0.563 (SE = 0.058), p < 0.001, thus suggesting a partially mediated path. The results are reported in Furthermore, the total indirect effect (fear of COVID-19 → clarity of information → hopelessness) was statistically significant: β = 0.128 (SE = 0.050), Full modelFinal model (Model 3)path a1: β = −0.524 (SE = 0.061), p < 0.001. At the same time, “fear of COVID-19” (X) was not statistically associated with “clarity of information” (M2), path a2: β = −0.029 (SE = 0.086), p = 0.739 ns.Considering the fourth model , “fear opath d21: β = 0.642 (SE = 0.110), p < 0.001.However, “empathic communication” (M1) was positively associated with “clarity of information” (M2), path b1: β = −0.186 (SE = 0.148), p = 0.207 ns. At the same time, “clarity of information” (M2) was negatively associated with “hopelessness” (Y), path b2: β = −0.272 (SE = 0.101), p = 0.007.Simultaneously, “empathic communication” (M1) was not statistically associated with “hopelessness” (Y), path c1: β = 0.366 (SE = 0.101), p < 0.001.Finally, “fear of COVID−19” (X) was still positively associated with “hopelessness” (Y), first total indirect effect (fear of COVID-19 → empathic communication → hopelessness) was not statistically significant: β = 0.098 (SE = 0.079), p = 0.219 ns. In line with these results, the second total indirect effect (fear of COVID-19 → clarity of information → hopelessness) was not statistically significant: β = 0.008 (SE = 0.027), p = 0.771 ns. Instead, the third total indirect effect (fear of COVID-19 → empathic communication → clarity of information → hopelessness) was statistically significant: β = 0.091 (SE = 0.038), p = 0.016.Furthermore, an examination of the three indirect paths was performed. The without the path from “fear of COVID-19” → “hopelessness”) was statistically significant: β = 0.197 (SE = 0.078), p = 0.012. The second total model effect (considering each relationship between variables plus the path from “fear of COVID-19” → “hopelessness”) was statistically significant: β = 0.563 (SE = 0.059), p < 0.001, thus suggesting a partially mediated path.Lastly, the two total model effects were also examined. The first indirect total model effect  was equal to β = 0.164 ((SE = 0.070), p = 0.019) for males and it was equal to β = 0.324 ((SE = 0.115), p = 0.005) for females. Consequently, the η-index revealed considerable separation between the estimated densities bootstrapped distribution of the “(indirect) total model effect” for both males and females: η = 0.234 (23.4%) with a consequential separation index (“1-η”) of 0.766 (76.6%) (The first  (76.6%) .plus the path from “fear of COVID-19” → “hopelessness”), it was equal to β = 0.615 ((SE = 0.095), p < 0.001) for males and it was equal to β = 0.522 ((SE = 0.082), p < 0.001) for females. Consequently, the η-index revealed a substantial overlap between the estimated densities’ bootstrapped distribution of the “total model effect” for both males and females: η = 0.845 (84.5%) with a consequential separation index (“1-η”) of 0.155 (15.5%)  .The COVID-19 virus has shown high mortality rates and symptom severity, putting a strain on the endurance of health care resources around the world ,3,12,117Consequently, the present study aimed at testing if empathy and clarity of communication may buffer the adverse relation between fear of COVID-19 and hopelessness feelings experienced by oncological patients during the COVID-19 pandemic.A series of path analysis models were consecutively tested. Model 1 showed the existence of a positive relationship between fear of COVID-19 and hopelessness: higher fear of COVID-19 was associated with higher hopelessness experienced by patients.Model 2a and Model 2b, respectively, tested the mediating (buffering) role of PCC variables, i.e., (perceived) empathic communication and (perceived) clarity of information showing a negative association between them, and both the fear of COVID-19 and hopelessness.Lastly, Model 3 tested the conjoint sequential effect  of empatInterestingly, the overlap analysis showed the separation level of the model effects distributions between males and females. Overall, the results showed that the complete model was similar between males and females (overlap 85%). However, the total indirect effect was different between males and females (23% overlap). In particular, the mediated effects were stronger among females, suggesting that the PCC would have a greater impact among females.These results are in line with the scientific literature ,62,69,70Moreover, a lack of clear information  would increase the patients’ level of anxiety and contribute to worsening the doctor–patient relationship. Furthermore, unclear information would lead the patients to experience uncertainty, which represents a strong predictor of emotional distress in both patients with cancer and their family members ,66,67,68Indeed, in line with findings of previous research, the provision of clear and understandable information would decrease the patients’ level of anxiety, and increase their sleep quality, appetite, hope for the future, and satisfaction with the visit and with the physician ,66,67,68Conversely, a lack of communication skills might even worsen rather than improve the psychological well-being and adherence to treatment recommendations of patients suffering from cancer and other chronic conditions . For exaTherefore, good communication skills among doctors are crucial in building a trustworthy doctor–patient relationship that not only helps in therapeutic success but also increases their job satisfaction. Unfortunately, not many doctors are naturally blessed to have good communication skills and there is a necessity for formal training ,132,133.These results seem to further prove how empathy and clarity of communication in doctor–patient dialogues may buffer patients’ development of psychological symptoms ,134. TheSome limitations of this research should be listed. First, a relatively small sample was used. However, some studies showed that even 60 cases could provide an accurate estimation of path analysis models ,136, sugStill, to our knowledge, this study represents the first that highlights how PCC based on doctors’ empathic and clear communication might have a buffering effect on the relationship between fear of COVID-19 and lack of hope experienced by oncological patients during the pandemic.Moreover, a methodological peculiarity is the use of factor scores that allowed each item to have a realistic weight for the measured construct.Future research should further test the buffering effect of empathy and clarity of information in the relationship between fear and hopelessness oncological patients in different settings and cultures. Additionally, research that aims to evaluate the short and long-term benefits of effective doctor–patient communication on diverse life domains and the overall perceived quality of life of patients with cancer is warranted.Furthermore, other studies should investigate the possible differences between PCC conducted face-to-face compared with that provided via digital tools. In addition, other variables should be included in future research, such as the patients’ levels of distress and/or the presence of depressive symptoms, to further understand the mechanism underlying the benefits of empathy and clarity  on this population of patients.The present research supports the efficacy of a PCC in the oncological setting, with a specific focus on the role played by empathy and clarity of information in increasing patients’ well-being. Specifically, this study showed the buffering effect of the doctors’ communication on the relationship between the patients’ fear of COVID-19 and hopelessness for the future. Overall, the findings of this study contribute to a better understanding of the importance of physicians’ empathic communication with oncological patients, providing evidence of its mediating role in the context of the COVID-19 pandemic.


Leiomyoma of the seminal vesicle is a rare leiomyoma characterized by the formation of benign leiomyomatous tissue within the seminal vesicle. Although histologically benign, excessive size can lead to urinary system disease if left untreated. Herein, we report a case of a seminal vesicle epithelioid leiomyoma.A 36-year-old Chinese man sought medical attention at our hospital for urination pain and hemospermia. CT showed a 5.3 cm × 5.0 cm seminal vesicle mass with a mixed density in the right seminal vesicle. The gross specimen showed light yellow, gray, and white tissues, with softness and hemorrhage in some places. Histologically, it showed classic spindle cell proliferation, with spindle cells arranged in fascicles, and mitosis was rare. Immunohistochemistry showed frequent expression of smooth muscle markers, such as calponin, SMA, and desmin. A diagnosis of epithelioid leiomyoma was proposed according to the immunohistochemical findings and morphology. The patient did not receive adjuvant therapy. There was no evidence of tumor recurrence in the 10 months after surgery.We report the first case of epithelioid leiomyoma in the seminal vesicle. This disease should be included in the differential diagnostic list of seminal vesicle tumors with epithelioid morphology.The online version contains supplementary material available at 10.1186/s12894-022-00963-3. Primary tumors of the seminal vesicle are rare. Seminal vesicle cysts occur more commonly than solid masses. Among solid masses, the most common primary tumor is adenocarcinoma. Among them, the most common is metastatic adenocarcinoma from colorectal or prostate. Benign solid tumors of the seminal vesicle include cystadenoma of the seminal vesicles, fibroadenoma, solitary fibrous tumor and leiomyoma. Among the seminal vesicle masses, leiomyoma is a very rare mass, with only a few cases reported in the literature. We searched PubMed using the keywords “leiomyoma”, “seminal vesicle”, “seminal duct”, “seminal vesicle pathology” and “seminal duct pathology”. As of January 2021, only 12 cases of seminiferous duct/scrotal leiomyoma have been reported in the medical literature, but no epithelioid leiomyoma has been reported. We report a new case of primary epithelioid leiomyoma of the seminal vesicle and present a brief review of the literature.A 36-year-old Chinese man sought medical attention at our hospital for urination pain and hemospermia. The patient developed hematospermia 2 weeks prior. At the same time, there was pain in urination, and the pain was a tingling sensation. His symptoms were relieved after urination. There were mixed lesions behind the bladder by B-ultrasonic examination (no image provided). No special disease history was reported.Computed tomography (CT) showed that the mixed density of mass could be seen in the right seminal vesicle with clear boundary. The size of the mass was 5.3 cm × 5.0 cm. CTU (CT urography) showed that the mass had uneven enhancement, and patchy non enhancement areas could be seen Fig. a–c. PreoDue to the nature of laparoscopic surgery, the specimen was relatively broken. However, it seemed that there was still a fibrous capsule in some places Fig. d. In othMicroscopically, the mass was rich in cells, there were fibrous capsules, and the boundary was relatively clear. The cells were arranged in the shape of sheets, nests or strips. The tumor cells were round and partly polygonal, with an abundant and acidophilic cytoplasm. The nucleus was located in the center of the cell. Mitosis was rare < 2/10 HPF). Some tumor cells lined up around blood vessels to form a perivascular tumor-like structure. Vascular hyperplasia was seen in the stroma , Vimentin(−), calponin(+), SMA(+), Desmin(+), CK5/6(−), P63(−), HMB45(−), MelanA(−), EMA(−), 34βE12(−), CD31(−), CD34(−), ERG(-), D2-40(−), S-100(−), CD117(−), DOG-1(−), CD68(−), PSA(−), and Ki-67 (2–5%) ; therefore, vascular tumors were excluded. HMB45(−), Melan-A(−) and S-100(−) can exclude malignant melanoma. It is not a neurogenic tumor because S-100 was not expressed. GISTs were excluded because CD117, DOG-1 and CD34 were all negative. Ewing’s sarcoma was not considered due to ERG negativity. The immunohistochemistry results for smooth muscle markers, such as calponin, SMA, and desmin, were positive. Therefore, the proposed diagnosis was that the patient had a smooth muscle tumor. EMA and CD68 were used to differentiate fibrous histiocytoma from epithelioid leiomyoma.Leiomyoma often occurs in the uterus . It is tDiseases of the seminal vesicle are extremely rare, and there are only a few reports in the literature. A systematic review of the medical literature shows that only 12 cases of leiomyoma of the seminal vesicle have been reported so far , calponin(+), Ki-67 (2–5%), CK , PSA(−), ERG(−), CK5/6(−), P63(−), 34βE12(−), HMB45(−), MelanA(−), S-100(−), CD68(−), EMA(−), CD117(−), DOG-1(−),CD34(−), D2-40(−) and CD31(−).