. 2024 Apr 26;384(6694):428-437.

doi: 10.1126/science.adh7954. Epub 2024 Apr 25.

Vitamin D regulates microbiome-dependent cancer immunity

Evangelos Giampazolias^{1

2}, Mariana Pereira da Costa^#¹, Khiem C Lam^#³, Kok Haw Jonathan Lim^#^{1

4}, Ana Cardoso^#¹, Cécile Piot^#¹, Probir Chakravarty⁵, Sonja Blasche⁶, Swara Patel², Adi Biram¹, Tomas Castro-Dopico¹, Michael D Buck¹, Richard R Rodrigues^{7

8}, Gry Juul Poulsen⁹, Susana A Palma-Duran¹⁰, Neil C Rogers¹, Maria A Koufaki¹¹, Carlos M Minutti¹, Pengbo Wang², Alexander Vdovin², Bruno Frederico¹, Eleanor Childs¹, Sonia Lee¹, Ben Simpson¹², Andrea Iseppon¹³, Sara Omenetti¹³, Gavin Kelly⁵, Robert Goldstone⁵, Emma Nye¹⁴, Alejandro Suárez-Bonnet^{14

15}, Simon L Priestnall^{14

15}, James I MacRae¹⁰, Santiago Zelenay¹¹, Kiran Raosaheb Patil⁶, Kevin Litchfield¹², James C Lee^{16

17}, Tine Jess⁹, Romina S Goldszmid³, Caetano Reis E Sousa¹

Affiliations

¹ Immunobiology Laboratory, The Francis Crick Institute, London NW1 1AT, UK.
² Cancer Immunosurveillance Group, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK.
³ Inflammatory Cell Dynamics Section, Laboratory of Integrative Cancer Immunology (LICI), Center for Cancer Research (CCR), National Cancer Institute (NCI), Bethesda, MD 20892, USA.
⁴ Department of Immunology and Inflammation, Imperial College London, London SW7 2AZ, UK.
⁵ Bioinformatics and Biostatistics STP, The Francis Crick Institute, London NW1 1AT, UK.
⁶ MRC Toxicology Unit, University of Cambridge, Cambridge CB2 1QR, UK.
⁷ Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA.
⁸ Microbiome and Genetics Core, LICI, CCR, NCI, Bethesda, MD 20892, USA.
⁹ National Center of Excellence for Molecular Prediction of Inflammatory Bowel Disease, PREDICT, Faculty of Medicine, Aalborg University, Department of Gastroenterology and Hepatology, Aalborg University Hospital, A DK-2450 Copenhagen, Denmark.
¹⁰ Metabolomics STP, The Francis Crick Institute, London NW1 1AT, UK.
¹¹ Cancer Inflammation and Immunity Group, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK.
¹² Tumor Immunogenomics and Immunosurveillance (TIGI) Lab, UCL Cancer Institute, London WC1E 6DD, UK.
¹³ AhRimmunity Laboratory, The Francis Crick Institute, London NW1 1AT, UK.
¹⁴ Experimental Histopathology, The Francis Crick Institute, London NW1 1AT, UK.
¹⁵ Department of Pathobiology and Population Sciences, The Royal Veterinary College, North Mimms, Hatfield, Hertfordshire AL9 7TA, UK.
¹⁶ Genetic Mechanisms of Disease Laboratory, The Francis Crick Institute, London NW1 1AT, UK.
¹⁷ Institute of Liver and Digestive Health, Division of Medicine, Royal Free Hospital, University College London, London NW3 2QG, UK.

^# Contributed equally.

PMID: 38662827
PMCID: PMC7615937
DOI: 10.1126/science.adh7954

Vitamin D regulates microbiome-dependent cancer immunity

Evangelos Giampazolias et al. Science. 2024.

. 2024 Apr 26;384(6694):428-437.

doi: 10.1126/science.adh7954. Epub 2024 Apr 25.

Authors

Affiliations

¹ Immunobiology Laboratory, The Francis Crick Institute, London NW1 1AT, UK.
² Cancer Immunosurveillance Group, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK.
³ Inflammatory Cell Dynamics Section, Laboratory of Integrative Cancer Immunology (LICI), Center for Cancer Research (CCR), National Cancer Institute (NCI), Bethesda, MD 20892, USA.
⁴ Department of Immunology and Inflammation, Imperial College London, London SW7 2AZ, UK.
⁵ Bioinformatics and Biostatistics STP, The Francis Crick Institute, London NW1 1AT, UK.
⁶ MRC Toxicology Unit, University of Cambridge, Cambridge CB2 1QR, UK.
⁷ Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA.
⁸ Microbiome and Genetics Core, LICI, CCR, NCI, Bethesda, MD 20892, USA.
⁹ National Center of Excellence for Molecular Prediction of Inflammatory Bowel Disease, PREDICT, Faculty of Medicine, Aalborg University, Department of Gastroenterology and Hepatology, Aalborg University Hospital, A DK-2450 Copenhagen, Denmark.
¹⁰ Metabolomics STP, The Francis Crick Institute, London NW1 1AT, UK.
¹¹ Cancer Inflammation and Immunity Group, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK.
¹² Tumor Immunogenomics and Immunosurveillance (TIGI) Lab, UCL Cancer Institute, London WC1E 6DD, UK.
¹³ AhRimmunity Laboratory, The Francis Crick Institute, London NW1 1AT, UK.
¹⁴ Experimental Histopathology, The Francis Crick Institute, London NW1 1AT, UK.
¹⁵ Department of Pathobiology and Population Sciences, The Royal Veterinary College, North Mimms, Hatfield, Hertfordshire AL9 7TA, UK.
¹⁶ Genetic Mechanisms of Disease Laboratory, The Francis Crick Institute, London NW1 1AT, UK.
¹⁷ Institute of Liver and Digestive Health, Division of Medicine, Royal Free Hospital, University College London, London NW3 2QG, UK.

^# Contributed equally.

PMID: 38662827
PMCID: PMC7615937
DOI: 10.1126/science.adh7954

Abstract

A role for vitamin D in immune modulation and in cancer has been suggested. In this work, we report that mice with increased availability of vitamin D display greater immune-dependent resistance to transplantable cancers and augmented responses to checkpoint blockade immunotherapies. Similarly, in humans, vitamin D-induced genes correlate with improved responses to immune checkpoint inhibitor treatment as well as with immunity to cancer and increased overall survival. In mice, resistance is attributable to the activity of vitamin D on intestinal epithelial cells, which alters microbiome composition in favor of Bacteroides fragilis, which positively regulates cancer immunity. Our findings indicate a previously unappreciated connection between vitamin D, microbial commensal communities, and immune responses to cancer. Collectively, they highlight vitamin D levels as a potential determinant of cancer immunity and immunotherapy success.

PubMed Disclaimer

Conflict of interest statement

Competing interests: C.R.S. is a founder of Adendra Therapeutics and owns stock options and/or is a paid consultant/advisory board member for Adendra Therapeutics, Bicara Therapeutics, Montis Biosciences and Bicycle Therapeutics, all unrelated to this work. C.R.S. has an additional appointment as a Visiting Professor in the Faculty of Medicine at Imperial College London and holds honorary professorships at University College London and King’s College London.

Figures

**Fig. 1. Loss of Gc increases CD8⁺ T cell dependent tumor control and augments response to immunotherapy.**
(A) Growth profile of 0.2 x 10₆ 5555 Braf^V600E cancer cells implanted in separately housed groups of Gc^-/- mice (n=8) and Gc^+/+ littermate control mice (n=11). (B) Quantification of the indicated intratumoral immune cell populations in separately housed groups of WT C57BL/6J (n=9) or Gc^-/- (n=8) mice at day 15 post-inoculation with 5555 Braf^V600E cancer cells. Data are presented as number of cells per gram of tumor from two independent experiments. (C) As in (A) but mice received anti-CD8 antibody or isotype-matched control (300μg intraperitoneally (i.p.) on days -3, 1, 4, 7, 10, 13, 16, 19, 22). WT C57BL/6J + isotype (n=12), WT C57BL/6J + anti-CD8 (n=12), Gc^-/- + isotype (n=14) and Gc^-/- + anti-CD8 (n=13). (D) Percent of 5555 Braf^V600E tumor rejection from two independent experiments in separately housed WT C57BL/6J or Gc^-/- groups of mice that received anti-PD-1 monoclonal antibody or isotype-matched control (200μg i.p. every 3 days from day 3 to day 18). WT + isotype (n=15), WT + anti-PD-1 (n=16), Gc^-/- + isotype (n=14), Gc^-/- + anti-PD-1 (n=15). (**E-F**) Separately housed WT C57BL/6J or Gc^-/- groups of mice implanted with 0.5 x 10⁶ MCA-205 and given isotype-matched control or anti-CTLA-4 (50μg injected i.p. on days 6, 9 and 12). (E) Growth profile (n=10 mice per group). (F) Survival (Kaplan-Meier) curves from two independent experiments (n=21 mice per group). Data in (A, C and E) are presented as tumor volume (mm³) ± SEM and are representative of two independent experiments. Tumor growth profiles (A, C and E) were compared using Bonferroni-corrected two-way ANOVA. Groups in (B) were compared using two-tailed unpaired t test with Welch’s correction. Incidence of tumor rejection and survival (Kaplan-Meier) curves in (D and F) were compared using Log-rank (Mantel-Cox) test for comparison of each group with WT C57BL/6J + isotype and Log-rank for trend for comparison of all groups. In (F) hazard ratios (HR) with 95% confidence interval are shown in brackets, calculated as a ratio of each group / WT + isotype. *p<0.05, ***p< 0.001, ****p< 0.0001; ns, not significant.

**Fig. 2. Fecal transplants from Gc^-/- mice increase anti-cancer immunity.**
(**A-E**) Growth profile of 0.2 x 10⁶ 5555 Braf^V600E cancer cells implanted into: (A) Separately housed Gc^+/+ (n=12) and co-housed Gc^+/+ (n=7) and Gc^-/- (n=6) groups of mice. (B) Separately housed groups of WT C57BL/6J mice (n=10 per group) that received orally PBS or fecal transplant (FT) from WT or Gc^-/- donors twice (days -14 and - 12) before tumor inoculation (day 0). (C) Separately housed groups of WT C57BL/6J or Gc^-/- mice that received or not vancomycin (0.5 g/L) in the drinking water starting from 2 weeks prior to tumor inoculation. WT (n=11), WT + vancomycin (n=10), Gc^-/- (n=11), Gc^-/- + vancomycin (n=10). (**D-E**) the indicated separately housed groups of mice that received oral FT from WT C57BL/6J or Gc^-/- donors twice (days -14 and -12) prior to tumor inoculation (day 0). (D) WT (n=11 per group), *Rag1*^-/- (n=9 per group), *Ifngr1*^-/- (n=10 per group), *Batf3*^-/- (n=10) and *Ifnar*^-/- (n=10 per group) mice, (E) Irradiated CD45.1 WT mice reconstituted using bone marrow (BM) from CD45.2 WT or *Myd88*^-/- donors. WT (WT BM) + WT FT (n=11), WT (WT BM) + Gc^-/- FT (n=12), WT (*Myd88*^-/- BM) + WT FT (n=10), WT (*Myd88*^-/- BM) Gc^-/- FT (n=10). Data in (A-E) are presented as tumor volume (mm³) ± SEM and are representative of two independent experiments. Tumor growth profiles (A-E) were compared using Bonferroni-corrected two-way ANOVA. *p<0.05, **p<0.01, ***p< 0.001, ****p< 0.0001; ns, not significant.

**Fig. 3. Loss of Gc increases VitD-dependent anti-cancer immunity by altering the gut microbiome.**
(**A-E**) Growth profile of 0.2 x 10⁶ 5555 Braf^V600E cancer cells implanted into: (**A-B**) WT C57BL/6J or Gc^-/- mice that were fed a VitD₃ standard (2 IU/g), deficient (0 IU/g) or high (10 IU/g) diet starting from 3.5 weeks before tumor inoculation. (A) WT + VitD₃^Standard (n=8), WT + VitD₃^Deficient (n=9), Gc^-/- + VitD₃^Standard (n=8), Gc^-/- + VitD₃^Deficient (n=9). (B) WT + VitD₃^Standard (n=12), WT + VitD₃^High (n=13), Gc^-/- + VitD₃^Standard (n=12), Gc^-/- + VitD₃^High (n=13). (C) WT C57BL/6J (n=10 per group) that received (on days -14 and -12 prior to tumor inoculation) FT from WT C57BL/6J or Gc^-/- donors that had been fed with VitD₃ standard or deficient diet. (D) WT C57BL/6J mice that were fed a VitD₃ standard or deficient diet starting 3.5 weeks before FT (on days -14 and -12 prior to tumor inoculation) with fecal matter from WT C57BL/6J or Gc^-/- donors. WT-VitD₃^Standard + WT FT (n=7), WT-VitD₃^Standard + WT Gc^-/- (n=10), WT-VitD₃^Deficient + WT FT (n=10), WT-VitD₃^Deficient + WT Gc^-/- FT (n=10). (E) WT C57BL/6J, *Rag1*^-/-, *Batf3*^-/- or *Myd88*^-/- mice (n=10 per group) that were fed with VitD₃ standard or high diet starting from 3.5 weeks before tumor inoculation. (F) Growth profile of 0.5 x 10⁶ MCA-205 cancer cells implanted into WT C57BL/6J mice (n=10 per group) that received (on days -14 and -12 prior tumor inoculation) FT from WT C57BL/6J donors that were fed with VitD₃ standard or high diet. Mice were treated i.p. with 50μg of isotype-matched control or anti-CTLA-4 antibody on days 6, 9 and 12. (**G-H**) Separately housed groups of WT C57BL/6J mice implanted with 0.5 x 10⁶ MC38 that received (on days -14 and -12 prior to tumor inoculation) FT from WT C57BL/6J donors that were fed with VitD₃ standard or high diet. Mice were treated i.p. with 200μg of isotype-matched control or anti-PD-1 monoclonal antibody every 3 days from day 3 to day 12. (G) Growth profile (n=10 mice per group). (H) Percent tumor rejection from two independent experiments (n=20 mice per group). Data in (A-G) are presented as tumor volume (mm³) ± SEM and are representative of two independent experiments. Tumor growth profiles (A-G) were compared using Bonferroni-corrected two-way ANOVA. Incidence of tumor rejection in (H) were compared using Log-rank (Mantel-Cox) test for comparison of each group with WT C57BL/6J + isotype and Log-rank for trend for comparison of all groups. *p<0.05, **p<0.01, ***p< 0.001, ****p< 0.0001; ns, not significant.

**Fig. 4. VitD acts via VDR in the gut epithelium to alter gut microbiome and permit tumor control.**
(**A, C-D**) Growth profile of 0.2 x 10⁶ 5555 Braf^V600E cancer cells implanted into: (A) WT C57BL/6J mice that received (on days -14 and -12 prior to tumor inoculation) FT from WT C57BL/6J, *Rag1*^-/-, *Batf3*^-/- or *Myd88*^-/- donors that had been fed for 3.5 weeks on a VitD₃ standard or VitD₃ high diet. WT + WT-VitD₃^Standard FT (n=10), WT + WT-VitD₃^High FT (n=10), WT *+ Rag1*^-/-- VitD₃^Standard FT (n=10), WT + *Rag1*^-/--VitD₃^High FT (n=9), WT + *Batf3*^-/--VitD₃^Standard FT (n=11), WT + *Batf3*^-/--VitD₃^High FT (n=11), WT + *Myd88*^-/--VitD₃^Standard FT (n=9), WT + *Myd88*^-/--VitD₃^High FT (n=9). (B) Lysates from the indicated mouse tissues of *Vdr*^+/+ and *Vdr*^{Δ IEC} mice immunoblotted for VDR and GAPDH. (C) *Vdr*^+/+ or *Vdr*^ΔIEC mice kept on a VitD₃ standard⁺ (2 IU/g) diet complemented with 2% calcium, 1.25% phosphorus and 20% lactose were then maintained on the same diet or switched to a VitD₃ high⁺ (10 IU/g) diet (similarly complemented with 2% calcium, 1.25% phosphorus and 20% lactose) from 3.5 weeks before tumor inoculation. *Vdr*^+/+-VitD₃^Standard+ (n=12), *Vdr*^+/+-VitD₃^High+ (n=11), *VdrΔIEC* -VitD₃^Standard+ (n=15), *VdrΔIEC* - VitD₃^High+ (n=15) (D) WT C57BL/6J mice (n=10 per group) received (on days -14 and -12 prior to tumor inoculation) FT from the groups in (C), i.e., *Vdr^+/+* or *Vdr^ΔIEC* donors that were fed with VitD₃ standard⁺ or VitD₃ high⁺ diet. Data in (A, C and D) are presented as tumor volume (mm³) ± SEM and are representative of two independent experiments. Tumor growth profiles (A, C and D) were compared using Bonferroni-corrected two-way ANOVA. *p<0.05, ****p< 0.0001; ns, not significant.

**Fig. 5. *B.fragilis* promotes tumor resistance in a VitD-dependent manner.**
(**A-B**) Meta-analysis of metagenomic data to determine (A) common features in microbial gene products (top 20/62 features in each direction shown, 20/62) and (B) last known taxon associated with differences in VitD availability. Fecal samples were sequenced from WT or Gc^-/- mice that had been fed with VitD₃ standard (2 IU/g), deficient (0 IU/g) or high (10 IU/g) diet for 3.5 weeks. Comparison is of mice with high VitD availability [WT + VitD₃^High (n=13), Gc^-/- + VitD₃^Standard (n=20), Gc^-/- + VitD₃^High (n=13) vs. mice with normal or low VitD availability [WT + VitD₃^Standard (n=22), WT + VitD₃^Deficient (n=10), Gc^-/- + VitD₃^Deficient (n=10)]. In (A, B), count of significant features indicated in the Venn diagram and shown by color scale (top) and ranked bar plots (bottom) show common features across 3 meta-analyses as indicated. (C) Growth profile of 0.2 x 10⁶ 5555 Braf^V600E cancer cells implanted into separately housed WT C57BL/6 groups of mice (n=10 per group) fed with VitD₃ standard (left graph) or deficient diet (right graph), starting 3.5 weeks before receiving *B. fragilis, P. brevis* or vehicle. Mice received 10⁹ *B. fragilis* or *P. brevis* by oral gavage on days -14, -12 and -10 prior to tumor inoculation. Data in (A, B) are presented as average log₂ median fold change from three meta-analyses of data from two independent experiments. Data in (C) are presented as tumor volume (mm³) ± SEM and are representative of two independent experiments for *P. brevis* and 3 independent experiments for *B. fragilis*. In (A, B), p values were calculated using the Mann–Whitney–Wilcoxon U test on parts per million (PPM) relative abundances for that feature in samples within each group for pairwise comparisons. The combined p value (cp) for meta-analysis of within-group comparisons was calculated using Fishers P value. For each feature type, the cut-offs for the meta-analysis were: p< 0.2, cp< 0.1, false discovery rate (FDR)<0.15. Tumor growth profiles (A, C and D) were compared using Bonferroni-corrected two-way ANOVA. ****p< 0.0001; ns, not significant.

**Fig. 6. VitD correlates with lower risk of cancer and increased patient survival.**
(A) Prognostic value of VitD-VDR gene signature levels for overall survival and hazard ratio comparing samples with the lowest (VitD-VDR sign^Low) versus highest (VitD-VDR sign^High) expression in the indicated TCGA datasets. Skin cutaneous melanoma (SKMC, n=460), sarcoma (SARC, n=259), liver hepatocellular carcinoma (LIHC, n=370), bottom and top 25% of patient cohort. (B) Hazard ratio, adjusted for age, sex and tumor stage, comparing samples with the lowest (VitD-VDR sign^Low) or highest (VitD-VDR sign^High) versus medium (VitD-VDR sign^Medium) expression in the indicated TCGA datasets as in (A). (C) Prognostic value of VitD-VDR signature levels for tumor stage comparing samples with the lowest (VitD-VDR sign^Low) versus highest (VitD-VDR = sign^High) expression in the indicated TCGA datasets. Breast cancer (BRCA, n=1092), prostate adenocarcinoma (PRAD, n=497), bottom and top 25% of patient cohort. (D) VitD-VDR signature levels in samples with no response vs. exceptional response (left) and rapid vs. standard disease progression (right) of patients (n=1008) treated with checkpoint inhibitors (CPI1000⁺ cohort). (E) Estimated hazard ratio, adjusted for sex, age and Charlson’s comorbidity index, in the VitD deficient (<25 nmol/L) or insufficient (25-50 nmol/L) group versus the VitD sufficient (50-125 nmol/L) group of individuals (n=1,496,766) that were living in Denmark between 2008-2017. In (A) data are presented as mean of log2 normalized expression ± SEM. In (C) data are represented as number of patients that are subdivided based on the tumour stage. In (D) data are presented as log2 normalized expression box-and-whisker plot with median, 25^th and 75^th percentiles represented by the box and min/max by the whiskers. Survival (Kaplan-Meier) curves in (A) were compared using Log-rank (Mantel-Cox) test. In (A, B and E) hazard ratios (HR) with 95% confidence interval showed. In (A and C) gene signature levels between groups were compared using two-tailed unpaired t test with Welch’s correction. In (C) frequency of tumour stage was compared between groups using Chi-squared test. In (D) expression of gene signature between the groups was compared using Wilcoxon signed-rank test. *p<0.05, **p<0.01, ***p< 0.001, ****p< 0.0001; ns, not significant.

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Microbes and vitamin D aid immunotherapy.
Franco F, McCoy KD. Franco F, et al. Science. 2024 Apr 26;384(6694):384-385. doi: 10.1126/science.adp1309. Epub 2024 Apr 25. Science. 2024. PMID: 38662852

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Grant WB. Grant WB. Nutrients. 2024 May 11;16(10):1450. doi: 10.3390/nu16101450. Nutrients. 2024. PMID: 38794688 Free PMC article.
Connecting vitamin D, the microbiome and anticancer immunity.
Flemming A. Flemming A. Nat Rev Immunol. 2024 Jun;24(6):378. doi: 10.1038/s41577-024-01044-2. Nat Rev Immunol. 2024. PMID: 38745002 No abstract available.

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Vitamin D regulates microbiome-dependent cancer immunity

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Vitamin D regulates microbiome-dependent cancer immunity

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