Wrong and missed diagnoses contribute substantially to medical error. Can a prompt to generate alternative diagnoses (prompt) or a differential diagnosis checklist (DDXC) increase diagnostic accuracy? How do these interventions affect the diagnostic process and self‐monitoring?N = 90) were randomly assigned to one of four conditions to complete six computer‐based patient cases: group 1 (prompt) was instructed to write down all diagnoses they considered while acquiring diagnostic test results and to finally rank them. Groups 2 and 3 received the same instruction plus a list of 17 differential diagnoses for the chief complaint of the patient. For half of the cases, the DDXC contained the correct diagnosis (DDXC+), and for the other half, it did not . Group 4 (control) was only instructed to indicate their final diagnosis. Mixed‐effects models were used to analyse results.Advanced medical students , P = 0.602. The number and relevance of diagnostic tests acquired were not affected by condition, nor was self‐monitoring. However, participants spent more time on a case in the DDXC−, 4:20 min (2:36), P ≤ 0.001, and DDXC+ condition, 3:52 min (2:09), than in the control condition, 2:59 min (1:44), P ≤ 0.001.Students using a DDXC that contained the correct diagnosis had better diagnostic accuracy, mean (standard deviation), 0.75 (0.44), compared to controls without a checklist, 0.49 (0.50), Being provided a list of possible diagnoses improves diagnostic accuracy compared with a prompt to create a differential diagnosis list, if the provided list contains the correct diagnosis. However, being provided a diagnosis list without the correct diagnosis did not improve and might have slightly reduced diagnostic accuracy. Interventions neither affected information gathering nor self‐monitoring. The authors demonstrate that a differential diagnosis checklist reduces diagnostic error without influencing the extent and relevance of information gathering, but only if the correct diagnosis is contained in the checklist. We also examine the moderating effects of case difficulty, as an earlier study indicated that DDXC may improve diagnostic performance only in more difficult cases.22.1N = 300) were eligible to participate in the present study. The required sample size was estimated using G*Power for a priori power analysis.d = 0.8 for diagnostic accuracy,All medical students in their fourth (out of six) academic year at the Charité – Universitätsmedizin Berlin  test,Per case, participants were then presented with 30 diagnostic tests on their computer screen from which they were free to choose the type, order and number of tests to administer; repeated acquisition was allowed. When clicking on a test, participants were presented with the test result in the form of a text , an image  or an audio clip  they had to interpret. Participants could finish data gathering at any time to provide a final diagnosis and related confidence on the next screen. They then proceeded to the next case.DDXC with correct diagnosis provided condition (DDXC+) or DDXC without correct diagnosis provided condition (DDXC−), respectively. In other words, whether the DDXC contained the correct diagnosis or not varied within subject. Participants were pointed to the fact that the DDXC would not necessarily contain the correct diagnosis. They were instructed to list all generated diagnoses including diagnoses from the DDXC as well as other diagnoses they thought of. In the control condition, participants were only asked to type in their final diagnosis at the end of their information search.There were four study conditions Figure : in the 2.3Upon arrival, participants read the study description and signed the consent form. After answering demographic questions, participants took a 22‐item multiple‐choice test to assess their subject‐relevant knowledge. Next, participants received a demonstration of how to work on the clinical cases. Participants were then randomised to one of the four conditions.Participants first familiarised themselves with the interface through a practice case with the condition‐specific instructions and then worked on the six randomly ordered main cases in a computer pool while wearing headphones, without receiving any feedback on their performance. The complete session lasted about 1 h, for which participants were compensated with €20 ($22 USD at the time).2.4Participation was voluntary and the Institutional Review Board of the Charité – Universitätsmedizin Berlin granted study approval (EA4/096/16).2.52.5.1Case difficulty was determined as the mean accuracy across students per case across conditions.2.5.2We recorded the number of diagnostic hypotheses considered and whether the correct diagnosis was on the self‐generated lists in the intervention conditions and, if so, ranked in which position.2.5.3P < 0.001).Diagnostic accuracy of all final diagnoses was rated by three experts as either incorrect (0) or correct (1).2.5.4Similar to previous studies,2.5.5least confidence) to 100 (highest confidence).M = 100 for all their correct answers and the lowest confidence level of M = 10 for all their incorrect answers) to −90.Participants recorded their confidence in the correctness of the diagnosis on a 10‐point ordinal scale ranging from 10 ; second, one model including diagnostic accuracy as the dependent variable; third, three models with indicators of data‐gathering behaviour as dependent variables  and, finally, two models where participants' confidence and self‐monitoring indices were entered as dependent variables.+, DDXC− and control) and case difficulty  as fixed effects in all models. Where meaningful, we included the accuracy of the final diagnosis per participant as a fixed effect, too.Across all models, we included subject ID as random intercept. Furthermore, we included condition  and did not adjust these thresholds for multiple comparisons because we did not consider spurious statistically significant effects to be an issue in the current application.We used conventional thresholds for levels of statistical significance (https://osf.io/92swt/?view_only=faec41999e4848ef9535139dfe982c1c).Raw data are available  in case 1 (instable ventricular tachycardia) to M = 0.91 (SD = 0.28) in case 4  alternative diagnoses in the prompt condition, M = 3.86 (SD = 1.61) diagnoses when receiving a DDXC that did not include the correct diagnosis (DDXC−) and M = 3.43 (SD = 1.52) diagnoses when receiving a DDXC that included the correct diagnosis  in the easiest case 4 to M = 4.28 (SD = 1.80) in the second to hardest case 3. In correctly solved cases, list length was on average M = 3.48 (SD = 1.65), and in incorrect cases M = 3.95 (SD = 1.60). Results from mixed models indicated that list lengths did neither differ between intervention conditions, nor between difficulty levels, nor between correctly versus incorrectly solved cases , accuracy of the lists was higher in the DDXC+ condition  but not in the DDXC− condition . The likelihood of the correct diagnosis being listed decreased from M = 0.98 (SD = 0.13) in the easiest case 4 to M = 0.45 (SD = 0.50) in the most difficult case 1. Results from mixed models indicated that the increase in list accuracy from the prompt to the DDXC+ condition was statistically significant, P = 0.002 but not the decrease in the DDXC− condition. Further, the decrease in list accuracy with increasing case difficulty was statistically significant, P < 0.001.Next, we determined the accuracy of the listed diagnoses, that is, whether participants' own lists contained the correct diagnosis or not. We found that, compared to the prompt condition , participants ranked the correct diagnosis earlier in the DDXC+  and in the DDXC− conditions . Also, the position varied with case difficulty, though not linearly, with the correct diagnosis being ranked earliest in the easiest case 4  and latest in the second to hardest case 3 , indicating some case specificity. In correctly solved cases, the correct diagnosis appeared on position M = 2.22 (SD = 1.52) and in incorrectly solved cases on position M = 2.89 (SD = 1.54). Results from mixed models indicated that the difference between control and DDXC+ was statistically significant, P = 0.038 but not the difference between control and DDXC−. Also, the differences in the position of the correct diagnosis between cases of different difficulty levels were statistically significant, P < 0.001, but not between correctly versus incorrectly solved cases.As shown by Figure 3.3.2SD = 0.50) in the control condition to 0.75 (SD = 0.44) in the DDXC+ condition. Accuracy only slightly increased in the prompt condition  and slightly decreased in the DDXC− condition . Results from mixed models revealed a statistically significant increase in the DDXC+ condition, P < 0.001, and no statistically significant differences between control and DDXC− nor between control and the prompt condition  in the prompt condition and M = 24.12 (SD = 10.12) tests in the control condition. The extent of information gathering ranged from M = 21.26 (SD = 8.83) tests in the easiest case 4 to M = 25.28 (SD = 12.13) tests in the intermediate case 6. More tests were administered when the final diagnosis was incorrect  than when it was correct . In line with this result, participants spent more time on a case when the final diagnosis was incorrect  than correct . Compared to the control condition , participants took more time to make a diagnosis in the prompt condition , in the DDXC+ condition , and even more in the DDXC− condition . The time spent on information gathering varied between M = 2:49 min (SD = 1:37) in the easiest case 4 and M = 4:16 min (SD = 2:19) in the second to hardest case 3. Results from mixed models revealed no statistically significant differences in the number of tests between conditions, nor between cases of different difficulty levels, but between correctly versus incorrectly solved cases, P = 0.040  in the control condition and M = 60.69 (SD = 9.43) in the DDXC− condition. Average relevance was lowest in the easiest case 4  and highest in the intermediate case 5 . Average relevance in correctly solved cases was as high  as in incorrectly solved cases . Results from mixed models revealed no statistically significant differences between conditions nor between correct and incorrect cases, but between cases of different levels of difficulty, P = 0.005, indicating some case specificity.Relevance of acquired tests was similarly high across conditions, varying between 3.3.4M = 59.11, SD = 23.36), confidence was higher in the prompt condition  and even higher in the DDXC−  and DDXC+ conditions  and highest in the easiest case 4 . Confidence in correct diagnoses was higher  than in incorrect diagnoses . We then calculated the difference in confidence between incorrect and correct answers per person, known as the self‐monitoring index .M = 3.24, SD = 18.70) and highest in the prompt condition . Results from mixed models indicated that the observed difference in confidence between the control and DDXC− and DDXC+ conditions were statistically significant, P = 0.007 and P = 0.006, respectively, but not the difference between control and prompt  in our study. It may be worthwhile to test a more dynamic version of a checklist such as an electronic diagnostic decision support tool,Further, we found that providing participants with DDXCs (vs. not) increased their confidence in their diagnosis. This result is in line with a large body of research showing that receiving advice generally increases confidence.not receive any prompt.Only using a prompt instead of a DDXC would arguably be the more efficient and handy (because symptom‐independent) solution to the problem of diagnostic error. Our failure to show its effectiveness might have different reasons. First, the prompt may not have been specific enough to trigger the generation of more diagnostic hypotheses. Analysing the generated lists revealed that participants indeed entertained only few  alternative diagnoses, which is in line with previous studies where participants did how to perform those steps such as on how to generate more alternative diagnoses or on how to test those hypotheses, for example, with the help of structured reflection.Second, only reminding participants of one—despite crucial—step might not have been enough to improve diagnostic accuracy. Similar to other generic checklists, it might be necessary to provide a reminder of several  steps. It might also be necessary to give more concrete guidance on Together, our findings corroborate with research suggesting that providing content‐specific knowledge (here in the form of symptom‐related checklists) rather than general debiasing procedures4.1Our findings of limited benefits of checklists may call into question the suitability of checklists for the diagnostic process as a whole. However, it needs to be acknowledged that we tested only three types of interventions here. Also, our experimental setup sets limitations to the generalizability of our results to clinical practice, to different presenting complaints, to more experienced physicians and other (less ill‐defined) settings. Also, generalisability to more atypical case presentations is limited, as cases here were designed as rather unambiguous, prototypical case presentations, which are common in the emergency department. Previous studies have argued that common cases tend not to benefit from (checklist‐induced or instructed) reflection.4.2To conclude, we provide evidence of the potential benefit of using a symptom‐specific differential diagnosis checklist during the diagnostic process for diagnostic accuracy without altering data‐gathering behaviour. At the same time, our finding that the correct diagnosis needs to be on the checklist limits the potential benefit of this type of intervention and suggests that how to design the most effective checklists and how to integrate them in the diagnostic process are still important questions to be answered.The authors report no conflict of interest. The authors alone are responsible for the content and writing of the paper.The study was approved by the Institutional Review Board at the Charité – Universitätsmedizin Berlin (EA4/096/16) and was performed in accordance with ethical standards.JEK, SKS, SCH, FS, and WEH conceptualized the study. JEK, SCH, and FS collected the data. JEK analyzed the data. JEK drafted the manuscript. SKS, SCH, FS, and WEH conducted the revision of manuscript and substantial contributions: JEK, SKS, SCH, FS, and WEH approved the final version to be published.Table S1. Results of the mixed effects models for outcome variables concerning list characteristicsTable S2. Results of the mixed effects models for accuracy of the final diagnosisTable S3. Results of the mixed effects models for outcome variables concerning data‐gathering behaviorTable S4. Results of the mixed effects models for outcome variables concerning confidenceClick here for additional data file.


Soil compaction affects many soil functions, but we have little information on the resistance and resilience of soil microorganisms to this disturbance. Here, we present data on the response of soil microbial diversity to a single compaction event and its temporal evolution under different agricultural management systems during four growing seasons. Crop yield was reduced (up to −90%) in the first two seasons after compaction, but mostly recovered in subsequent seasons. Soil compaction increased soil bulk density (+15%), and decreased air permeability (−94%) and gas diffusion (−59%), and those properties did not fully recover within four growing seasons. Soil compaction induced cropping system-dependent shifts in microbial community structures with little resilience over the four growing seasons. Microbial taxa sensitive to soil compaction were detected in all major phyla. Overall, anaerobic prokaryotes and saprotrophic fungi increased in compacted soils, whereas aerobic prokaryotes and plant-associated fungi were mostly negatively affected. Most measured properties showed large spatial variability across the replicated blocks, demonstrating the dependence of compaction effects on initial conditions. This study demonstrates that soil compaction is a disturbance that can have long-lasting effects on soil properties and soil microorganisms, but those effects are not necessarily aligned with changes in crop yield. Modern agriculture depends on a high level of mechanization to efficiently and economically manage cropping systems. However, the use of heavy machinery often leads to soil compaction, in particular during field operations under unfavorable soil conditions, such as wet soils . Once soSoil compaction adversely affects soil structure by increasing bulk density and soil mechanical resistance, as well as reducing macroporosity and pore connectivity , 7. As aCompaction affects soil biological processes in multiple ways, and effects could be direct, e.g., increased mechanical resistance for bioturbation, or indirect via changes of the soil environment, e.g., soil oxygen and moisture levels. Soil mechanical resistance increases under compaction, and can lead to a decrease in root elongation and rooting depth , resultiPore space accessibility for bacteria and fungi can become limited in compacted soils , resultiThere is a relatively good understanding of soil compaction impact on physical soil properties, but we still lack comparable knowledge about its consequences for the soil microbiome. Most studies have focused on microbial biomass and functional activity of certain microbial processes, for example, greenhouse gas fluxes , 26. MetThe aim of this study was to assess the impact of soil compaction on soil microbial diversity, and its temporal evolution under different agricultural management systems in the first four growing seasons following compaction. Such information is essential to link our knowledge of changes in soil physicochemical properties and crop yield , 30 to tBased on previous observations in forest ecosystems , we hypoThe Soil Structure Observatory   and cropLolium perenne; 3 kg ha−1), Kentucky bluegrass , Timothy , creeping red fescue , tall fescue , and white clover , supplemented with 4 kg ha−1 of Lucerne (Medicago sativa L.). For harvesting, two frames (0.5 m × 0.5 m each) were placed per plot and the grass was cut within each frame, and weighed (dry biomass) in 2014 and 2017. The crop rotation consisted of triticale , silage maize , winter wheat , and winter rapeseed . Crop yield was measured as grain weight for triticale, wheat, and rapeseed, and as dry above-ground biomass for silage maize for each growing season.The PL (grass–legume) mixture is based on the mixture SM442  constitu3; diameter: 0.05 m; height: 0.05 m) per field block, compaction treatment, and agricultural management system were sampled from the topsoil. Bulk soil cores were collected at fixed distances around three random GPS coordinates within each field block for both “control” and “areal” compaction treatment, and inside the wheel tracks for the “tracks” compaction treatment. Sampling in close vicinity to the plants was avoided. Soil cores were analyzed for bulk density, gas diffusion and air permeability directly after compaction (spring 2014) and 3 years after compaction (spring 2017). Air permeability was obtained by measuring the air flow through the sample at an overpressure of 2 hPa, and gas diffusivity was measured in a one-chamber apparatus that uses O2 as the diffusing gas assuming steady-state diffusion [Soil samples for physical properties were collected in fall 2013 ~6 months before the compaction, as well as a couple of weeks after compaction (spring 2014), 1 year (spring 2015), 2 years (spring 2016), and 3 years (spring 2017) after the compaction event. At each time point, three undisturbed cylindrical soil cores . In total, 104 randomized samples (52 × 2) were collected at evenly spaced locations across the three blocks to assess initial heterogeneity of the field. After the compaction event, soil samples were collected after half a year , 1 year (spring 2015), 2 years (spring 2016), and 3 years (spring 2017). At each sampling event, bulk soil samples (0–20 cm) were collected by pooling four soil cores (2 cm diameter) located at three randomly selected positions within each field block for both “control” and “areal” compaction treatment, and inside the wheel tracks for “tracks” compaction treatment, alongside the physical property sampling spots, giving a total of 81 soil samples per time point . Soils were transported on ice to the laboratory, immediately sieved (mesh size of 4 mm), and stored at −20 °C until nucleic acid extraction.® assay . DNA concentrations were adjusted to 5 ng μl−1 in H2O containing bovine serum albumin  and heated to 95 °C to bind potential PCR-inhibiting substances, such as humic acids.Nucleic acids were extracted from 0.5 g soil according to the protocol of Bürgmann et al.  using a For sequencing, PCR amplification of the bacterial and archaeal (V3 and V4 regions of the 16S rRNA gene)—further termed prokaryotic—as well as fungal (ITS2 region of the rrn operon) markers was performed as described previously , using p−2 to 10−7 ng of DNA per reaction. qPCR was performed on 10 ng DNA with specific primers targeting the 16S rRNA gene (515F/806R) [nirK gene (5F/890F) [nirS gene (CD3aF Throback/ R3cd Throback) [TM Universal SYBR® Green Supermix , and 0.6 mg ml−1 of BSA. The conditions for each qPCR were as follows: 3 min for enzyme activation at 98 °C, then a cycle of 15 s for denaturation at 95 °C, 30 s primer hybridization at 58 °C for nirK and nirS or at 52 °C for 16S, and 30 s for extension at 72 °C was carried out 30 times for nirK and nirS primers and 35 times for 16 S primers. Prior to the previous detailed qPCR cycles, six cycles of touch down qPCR for nirK and nirS primer were done with hybridization at 63–1 °C/cycle for 30 s (the other steps being unchanged). Melting curves were generated by increasing the temperature from 75 to 95 °C by 0.5 °C every 5 s at the end of the amplification cycles, in order to verify the amplification specificity. The qPCR efficiencies (E) were equal to 97.4 (R2 = 0.999) for the 16S rRNA gene, 99.5 (R2 = 0.998) for the nirK gene, and 94.8 (R2 = 0.995) for the nirS gene. Estimated copy numbers for each targeted gene were calculated by the following formula:For functional gene quantification via quantitative PCR (qPCR), the lack of amplification inhibition was tested by qPCR amplification of the pGEM-T plasmid  with the plasmid specific primers SP6 and T7, introduced at equimolar concentration in all samples, in order to assess the amplification variability across treatments. The DNA standards were prepared from purified PCR products obtained by amplifying the targets from a pool of DNA from all samples. The concentrations used for the standard curves ranged from 105F/806R) , the nir5F/890F) , and thehroback) . All the23 being the Avogadro’s number; 660 being the average weight of a single base pair. The length of each target was estimated by in silico PCR with the primers used on all gene-specific sequences obtained from the NCBI nucleotide database and averaging the amplicon size.6.022 × 10fastq_mergepairs function and quality filtered using the fastq_filter function with a maximum expected error of one [derep_fulllength function in VSEARCH and delineated into amplicon sequence variants (ASVs) using the UNOISE algorithm [uchime3_denovo function in VSEARCH. Remaining ASV sequences were tested for the presence of ribosomal signatures, using Metaxa2 [usearch_global algorithm implemented in VSEARCH with settings maxrejects 100, maxaccepts 0, maxhits 1, and a minimum identity of 97%. Taxonomic classification of each verified ASV sequence was performed by running the SINTAX algorithm [The microbial sequence data were processed using a customized pipeline largely based on VSEARCH . PCR prir of one  both implgorithm  in VSEARlgorithm  implemen Metaxa2  and ITSx Metaxa2  for the lgorithm  implemenlgorithm  for the lgorithm  for the p values < 0.05 were considered significant in all statistical tests. Change in yields (kg ha−1) for PL and arable crops in the rotations were presented as percent change in comparison to (a) PL in the uncompacted control or (b) the respective crop in the uncompacted control under CT, respectively. For PL, the significant differences in yield between the control and the compaction treatments were assessed using Kruskal–Wallis test with the kruskal.test function in R, since the homogeneity of variance and normal distribution of the residuals were not supported. For the arable crop in the rotation, the significant differences in yield over all blocks between the control and the compaction treatments, as well as the tillage effect within each year were assessed with ANOVA using the aov function in R since the homogeneity of variance and normal distribution of the residuals were supported. The multiple pairwise comparisons were done by the Tukey test with the TukeyHSD function in R from the package stats.All statistical analyses were performed in R  and p vakruskal.test, as described earlier. The multiple pairwise comparisons were done by Dunn’s test with the dunn.test function in R from the package dunn.test v.1.3.5 [Significant differences of the soil physical properties  between the various conditions were assessed using the Kruskal–Wallis test with the  v.1.3.5 .rarecurve function from the vegan package [rrarefy, specnumber, diversity, and vegdist functions in vegan. The effect of space (block), time (date), compaction treatment, and agricultural management systems on α-diversity and on β-diversity were assessed using univariate or multivariate permutational analysis of variance  as implv.0.9–73 . Differev.0.9–73  with thetes (CAP ) with th package , with 99q values  implemenv.2.16.0  and q vaL v6.1.2  based onx v1.2.4  and FUNGx v1.2.4  for proknirS and nirK genes were normalized by dividing the values by the 16S rRNA gene copies, and significant differences in relative estimated copy numbers between the various conditions were assessed using ANOVA with the aov function, as described earlier.The estimated copy numbers per gram of dry soil of the In PL and NT, soil compaction significantly increased soil bulk density by ~10–15%, and significantly reduced air permeability by 60–94% and gas diffusion by 48–66% Fig. . In the Ley biomass in compacted plots was ~20% lower in 2014 (81 ± 21% of the biomass in the uncompacted control) and 30% lower in 2017 (68 ± 33%) than in “control” plots, although the differences were not statistically significant due to the large variation between blocks. In the crop rotations, compared to the yield in the uncompacted tillage treatment, the average yield of triticale (2014) and maize (2015) in NT was ~51–58% under the “tracks” compaction  and ~8–35% under the “areal” compaction  and between the different agricultural management systems  distinct microbial community over the whole experiment  distinct microbial communities (Table p < 0.029) shift in microbial community structure was observed over time  shaping the microbial community structure with each field block having significantly  out of the 3871 prokaryotic ASVs and ~43% (13% assigned at genus level) out of the 1141 fungal ASVs responded significantly to soil compaction, i.e., “tracks” versus “control” and/or “areal” versus “control”, across the different agricultural management systems. Most of these sensitive ASVs (increasing or decreasing under compaction) were unique for a specific agricultural management system and only very few ASVs were responding across two (up to 31) or all three management systems (up to 10) under “tracks” or “areal” compaction , Anaerolinea, Longilinea (Chloroflexi), Intrasporangium, Cellulomonas, Agromyces, Streptomyces, Micromonospora (Actinobacteria), and Mathanosarcina (Euryarchaeota), as well as Mortierella (Mortierellomycota), Mucor (Mucor) Tetracladium, Preussia, Podospora, Pseudobillarda, Botryotrichum, Scutellinia, Trematosphaeria, and Thelebolus . Conversely, genera with ASVs showing higher relative abundance in the uncompacted control plots , Nitrospira (Nitrospirae), Mycobacterium, Demequina (Actinobacteria), Pseudomonas (Proteobacteria), Bacillus (Firmicutes), Flavobacterium (Bacteroidetes), and Candidatus Nitrososphaera (Thaumarchaeota), as well as Ustilago, Calyptella (Basidiomycota), Paramicrosporidium (Rozellomycota), Glomus (Glomeromycota), Trichoderma, Aspergillus, Penicillium, Devriesia, Periconia, Chlamydocillium, Microdochium, Cordana, Plectosphaerella, Paraphaeosphaeria, Clonostachys, and Chaetomium (Ascomycota). In addition to the ASV level response, these shifts in relative abundance were also statistically evaluated by aggregating the data at all assigned taxonomic levels from genus to phylum , and substantially reduced air permeability (60–63%) and gas diffusion . Precipitation and associated changes in soil moisture are known to have direct short-term, as well as long-term legacy effects on microbial diversity [The temporal component was the second strongest driver of prokaryotic and fungal diversity Table , with a iversity .In contrast to less frequently managed ecosystems like forests , this agAround 10% of the microbial ASVs responded to soil compaction, and these responses were often specific to the agricultural management systems and compaction treatments with very few (28 out of 1237 ASVs) universal responses across all systems and treatments Figs. . In the Desulfuromonas, Anaeromyxobacter, Geobacter, Anaerolinea, Longilinea, Intrasporangium, Dechlorosoma, and Methanosarcina significantly increased in relative abundance under soil compaction (Fig. Demequina, Mycobacterium, and Nitrospira, as well as the archaeal taxa Candidatus Nitrososphaera, which are known to exhibit a strictly aerobic lifestyle, significantly decreased in relative abundance in the compacted soils (Fig. Candidatus Nitrosophaeraceae and Nitrospira are both known as nitrifiers and their reduced relative abundance raises the question of potential interference with important aerobic processes, such as nitrification in compacted soils, as already suggested previously [The main driver of prokaryotes appeared to be the pore space and oxygen limitation due to the increase in bulk density leading to a reduction in air permeability and gas diffusion , as it hion Fig. . Many ofils Fig. . Candidaeviously . Reducedeviously  can ultieviously , 83, whinirS and nirK genes abundances were assessed. Soil compaction tended to increase nirS and nirK gene abundances in 2014 under PL and NT, but not under CT (Supplementary Fig. nirS and nirK gene abundances between compacted and uncompacted plots in block A.In order to further support this hypothesis, Mycobacterium, Flavobacterium, Bacillus, and Pseudomonas [Glomus [Trichoderma [Aspergillus and Penicillium [Devriesia, Clonostachys, Plectosphaerella, Paraphaeosphaeria, Periconia, Chlamydocillium, Microdochium, Ustilago, Calyptella, and Cordana. A reduced development of the plant root system [Aside from soil pore space and oxygen limitation, plants are another main driver of prokaryotic and fungal communities. In our study, many known potentially beneficial and/or pathogenic plant-associated bacteria, such as udomonas  decrease [Glomus , the plachoderma , or the icillium . Many oft system , 90 and t system  due to sLysobacter, Cellulomonas, Agromyces, Sphingomonas, Streptomyces, and Micromonospora [Pseudorobillarda, Botryotrichum, Preussia, Scutellinia, Trematosphaeria, Mortierella, Mucor, Tetracladium, and Thelebolus are known to be involved in lignocellulose degradation [Candidatus Xiphinematobacter and the fungal genus Paramicrosporidium decreased under compaction. Those microorganisms are known to be associated with nematodes [An increase in plant mortality or root die-off can lead to an accumulation of decaying plant tissue in compacted soils, which in turn can explain the increase in some potentially cellulolytic bacteria, such as onospora –94. Simiradation . We furtematodes  and amoeematodes , respectematodes .Overall, our results are in line with our hypothesis and consistent with previous observations in compacted forest soils , i.e., tThe extent to which a single soil compaction event has altered the soil microbial system over four growing seasons and the lack of complete resilience despite ongoing soil management practices, such as tillage and crop production activities is remarkable. A relative increase of anaerobically metabolizing prokaryotes and saprotrophic fungi under soil compaction compared to the uncompacted control accompanied by generally negative effects on microorganisms with aerobic or plant-host-associated lifestyles appears to be a unifying concept that agrees with previous studies carried out in forest soils. Given the small but consistent and long-lasting effect of this single disturbance event, the findings raise concerns about potential cumulative effects of soil compaction events associated with regular agricultural operations and repeated compaction events on the same field. The legacy effects of soil compaction on the microbiome and its functions, as well as the consequences for soil productivity deserve further attention in the debate of how to make cropping systems more sustainable.Supplementary Table S1Supplementary information


P < .001). TARC values had a high discriminatory power . A TARC cutoff level of 942 pg/mL maximized the sum of sensitivity (97.9%) and specificity (100%). TARC plasma levels were associated with age, treatment level, bulky disease, B‐symptoms, and erythrocyte sedimentation rate. TARC was found to be a highly specific and sensitive diagnostic marker for pediatric cHL. This noninvasive marker could be of great value as screening test in the work‐up for pediatric patients with lymphadenopathy.Pediatric classical Hodgkin's lymphoma (cHL) is characterized by Hodgkin Reed‐Sternberg cells located in an inflammatory microenvironment. Blood biomarkers result from active crosstalk between these cells. One promising biomarker in adult cHL patients is “thymus‐and‐activation‐regulated chemokine” (TARC). The objectives of this study were to define normal TARC values in non‐cHL children and to investigate and correlate pretherapy TARC as diagnostic marker in pediatric cHL. In this multicenter prospective study, plasma and serum samples were collected of newly diagnosed cHL patients before start of treatment (n = 49), and from randomly selected non‐cHL patients (n = 81). TARC levels were measured by enzyme‐linked immunosorbent assay. The non‐cHL patients had a median plasma TARC value of 71 pg/mL (range: 18‐762), compared to 14 619 pg/mL (range: 380‐73 174) in cHL patients ( Malignant Hodgkin and Reed‐Sternberg (HRS) cells account for only 0.1‐10% of all cells present in tumor tissue. They are by far outnumbered by reactive immune cells, such as activated T‐cells, histiocytes, macrophages, fibroblasts, and eosinophils , 3. ThisThe gold standard for diagnosing HL is by lymph node biopsy. This is a relatively time‐consuming and expensive procedure, requiring general anesthesia that in patients with mediastinal mass may be contraindicated due to danger of causing respiratory failure. After the diagnosis has been made, staging in pediatric cHL patients requires Fluor‐Deoxyglucose‐Positron Emission Tomography (FDG‐PET) scans. Although the latter is a sensitive test, it carries several disadvantages, including exposure to radiation, time consumption, high costs, and lack of specificity . Blood bOne biomarker reported in adult patients with cHL is thymus and activation‐regulated chemokine  , 7, 8. TTo our knowledge, TARC levels in pediatric patients with cHL have not been reported. Besides, normal values of TARC are not yet determined in children and may differ from those in adults. Because pediatric and adult cHL differ partly with respect to histology, risk factors, clinical presentation, and staging at diagnosis and prognosis , 17, 18,22.1This multicenter prospective study was sponsored by and conducted in the Erasmus Medical Centre—Sophia Children's Hospital (EMC—Sophia) in Rotterdam, in the Princess Máxima Centre for Pediatric Oncology in Utrecht, and in the Amsterdam University Medical Centre, location VUmc, in Amsterdam . Serial plasma and serum samples were collected prospectively from newly diagnosed pediatric cHL patients from January 2016 till March 2020. Samples for the non‐cHL group were collected from October 2017 until August 2019.Inclusion criteria for both newly diagnosed and relapsed cHL patients were as follows: (a) diagnosis of cHL confirmed by reference pathology, (b) aged below 18 years, (c) receiving standard treatment according to the European Network of Pediatric Hodgkin's Lymphoma First (interim phase) and Second International Inter‐Group study for Classical Hodgkin's Lymphoma in Children and Adolescents (EuroNet‐PHL C1(interim) or C2, EudraCT number 2006‐000995‐33 and 2012‐004053‐88) protocol or treatment for relapsed or refractory patients, (d) availability of a blood (plasma and/or serum) sample before start of treatment, and (e) written informed consent of the patient and/or the patient's parents or guardians according to national laws. Exclusion criteria were HIV positivity or underlying immunologic disorder except for an  Epstein Barr Virus infection. The diagnosis of cHL was made by lymph node biopsy, confirmed both by a local pathologist and by Dutch central review. All patients were staged and evaluated to conform the Cotswold revision of the Ann Arbor staging criteria with FDG‐PET and MRI/CT imaging . PatientThe non‐cHL group consisted of randomly selected patients from different departments, including hematological, oncological, and endocrinological outpatient clinics, excluding children with cHL. This non‐cHL group was treated in EMC‐Sophia. Inclusion criteria were (a) age below 18 years, (b) no diagnosis of cHL, (c) written informed consent of the patient and/or the patient's parents or guardians according to national laws, and (d) with a scheduled blood draw. Exclusion criteria were HIV positivity or underlying immunologic disorder except for an  Epstein Barr Virus infection. Atopic dermatitis and eczema can cause elevated TARC levels in both children and adults. Therefore, these diseases were checked and recorded , 21, 22.This study was IRB‐approved, and registered under Dutch Trial registry number 6876.2.2Recently, Zhao et al showed that TARC levels are significantly lower in platelet‐free plasma samples than in platelet containing serum samples, because platelets contain TARC. , 24. To 2.3g. Afterward serum and plasma supernatant was collected and frozen immediately at –80°C. TARC levels were measured by enzyme‐linked immunosorbent assay (ELISA) .After collection, serum tubes were stored at room temperature in a vertical position prior to centrifugation. Plasma tubes were centrifuged within 30 min after arrival at the laboratory. Blood sample centrifugation was performed at room temperature for 15 min at 1000 × TARC measurement by ELISA technology was performed in the laboratory of Pediatric Oncology at the EMC—Sophia, Rotterdam. Since June 2018, all measurements were performed at the diagnostic laboratory of the Princess Máxima Centre, and the Laboratory of Translational Immunology of the Wilhelmina Children's Hospital in Utrecht. For quality assessment, 20 samples were tested in both laboratories to make sure TARC levels were comparable.2.4U‐test for differences in continuous variables. For differences in continuous variables in paired samples (TARC plasma vs TARC serum), Wilcoxon's signed‐rank test was used.Statistical comparison of categorical variables was done using Pearson's chi‐square test for differences in the distribution of categorical variables (Fisher's exact test in case of sparse data), and the Mann‐Whitney Simple and multiple linear regression was used to investigate associations between clinical characteristics and (log) TARC levels. Residual diagnostics were used to assess adequacy of model fit.To evaluate how TARC levels discriminate cHL patients from the non‐cHL controls, a receiver operating characteristic (ROC) curve analysis was performed. For age‐matched cHL patients and non‐cHL controls, a covariate‐adjusted ROC curve was estimated. Diagnostic accuracy was given by the area under the ROC curve (AUC), and leave‐one‐out cross validation (LOOCV) was implemented. The optimal threshold was determined based on Youden's index, which maximizes the sum of sensitivity and specificity. Before study start, it was obtained that with at least 40 cHL patients available, a 90% two‐sided confidence interval for sensitivity with lower limit 85% would be produced when the sample sensitivity is 95% .To compare TARC plasma and serum AUCs, DeLong's method for paired data AUCs was used.All statistical tests were two‐sided. We used R (version 3.4.1) and SAS (version 9.4) for analyses.33.1For the non‐cHL group, plasma and serum samples were collected from 82 children. Two patients had to be excluded, one because the samples were hemolytic and one because no samples were available. For the cHL patient group, 53 patients were included . Six cHL patients had to be excluded because there were no pretreatment samples available. Plasma TARC data were available for 47 patients. For serum analysis, 13 patients had to be excluded due to absence of pretreatment samples. Serum TARC data were available for 40 patients.P = .012).Baseline characteristics of the 47 newly diagnosed cHL patients, including a comparison with patient data from the GPOH‐HD‐2002 study, used as a representative sample of cHL patients, are summarized in Table The median age of the non‐cHL group was 13 (range: 1‐17) years. Slightly less male patients  were included. Atopic dermatitis was found in eight (10%) patients.3.2Non‐cHL patients had a median TARC value of 71 (range: 18‐762) pg/mL for plasma and 317 (range: 27‐1300) pg/mL for serum. TARC levels were associated with age: for 1‐year increase in age, a 4% decrease in TARC plasma was found, and a 4% decrease for TARC serum. We did not detect elevated TARC plasma and serum levels in the eight cases of atopic dermatitis in this group (Table S1).3.3The median pretreatment plasma TARC level in 47 cHL patients was 14 619 pg/mL (range: 380‐73 174). All but one patient had elevated TARC levels (97.8%).The median serum pretreatment TARC level in 40 cHL patients was 38 263.5 pg/mL (range: 2257‐176 451). Thirty‐nine patients had elevated TARC levels (97.2%).3.4P < .001) than in non‐cHL patients . In order to improve statistical precision, analyses 1:1 matching cHL to non‐cHL patients by age were also performed, although this lead to a reduction of the number of cHL patients used in the plasma analyses from 47 to 39. For the matched dataset, AUC = .999  and LOOCV AUC = .996  for plasma. The age‐dependent cutoffs ranged from 742.2 to 1108.5 . Cutoffs ranged from 3300.13 to 4100.28.The non‐cHL group had a higher representativeness of younger age strata than the cHL group. Median age was 12 (range: 0‐18) in non‐cHL patients compared to 15 (range: 7‐18) in cHL patients (P = .413).No difference was found in the AUCs for the correlated ROC curves of paired plasma and serum data . B, Plasma and serum TARC levels were significantly associated with the presence of B‐symptoms at diagnosis . C, Plasma and serum TARC levels were associated with ESR > 30 mm/h . D, Patients with a higher treatment level and patients with organ involvement and E‐lesions had significantly higher plasma TARC levels (at least 190% increase), but this was not the case for serum TARC*Other: The patient with relapsed HL was treated with a relapsed treatment protocol where treatment levels were not used.No significant associations were found with CRP or stage. The fact that TARC is associated with treatment level but not with stage could be due to the strong correlation of TARC with bulky disease, B‐symptoms, and ESR, characteristics that are used to determine treatment level. When introducing these three variables together with stage in a multiple regression model, bulky disease remains significantly associated with TARC plasma and serum levels (slightly less than threefold increase or higher in TARC for presence of bulky disease vs not).4This is the first study to report normal TARC values in children. We define a cutoff level for TARC elevation in children with cHL. Our data demonstrate that TARC level in plasma or serum is a sensitive and specific diagnostic marker for pediatric cHL, with histology as gold standard. These results are even more promising than the results of TARC levels in adult patients with cHL , 7, 8. OBecause TARC is produced by HRS cells, we hypothesized that plasma TARC levels would correlate with tumor burden (defined by systemic symptoms and amount of tumor tissue) and staging. Consistent with adult studies, TARC levels in pediatric cHL patients correlated with bulky disease , 11, 13 TARC levels in pediatric non‐cHL patients closely correspond to TARC levels of adult non‐cHL patients investigated in other studies , 23. TARIn contrast to earlier studies, where children and adult patients with atopic dermatitis and eczema had elevated levels of TARC , 22, 23;TARC is measurable in both plasma and serum, although normal values and cutoff levels are different. No difference was found in the AUCs for the correlated ROC curves of paired plasma and serum data. Therefore, both serum and plasma can be used to measure TARC as diagnostic marker.In this study, plasma and serum samples were all taken before start of treatment with chemotherapy. A possible limitation of this study is the fact that in the majority of cases, a complete lymph node was removed for pathological diagnosis before the TARC plasma and serum samples were drawn. In patients with lower tumor burden and stage, this might have influenced the amount of TARC, because TARC is produced by HRS cells present in the lymph nodes.We propose that blood TARC levels can be of great value as screening test in the diagnostic work‐up for pediatric patients with lymphadenopathy. Especially cHL is sometimes difficult to differentiate from other pathologies due to the slow growing pattern and, sometimes, the lack of specific symptoms. Diagnostic markers such as ESR are useful but nonspecific in this work‐up. Based on this study, elevated TARC has high sensitivity and specificity for cHL. Further research in patients with other causes of lymphadenopathy is necessary to define how TARC should be implemented in the diagnostic algorithm.In conclusion, our study shows that TARC is a valuable diagnostic biomarker for children with cHL, with a sensitivity of 97.9% and specificity of 100% for plasma (cutoff level of 942 pg/mL) and 100% sensitivity and 100% specificity for serum (cutoff level of 2257 pg/mL). TARC levels are correlated with bulky disease, treatment level, the presence of B‐symptoms, and ESR.AB and FM‐W conceived the project and provided leadership. EZ, RH, WW, FM‐W, and AB organized the study. WW collected the non‐cHL group samples. EZ, RH, and ML analyzed the data and contributed to the manuscript. ML made the figures. EZ wrote the manuscript. FM‐W and AB supervised the study. All authors reviewed and accepted the contents of the article.The authors declare no conflict of interest.Please contact the corresponding author for access to original data.Supporting InformationClick here for additional data file.


Isoetes sinensis was formed by the natural hybridization and polyploidization of two diploid progenitors, Isoetes taiwanensis and Isoetes yunguiensis, and was believed to have the potential to adapt to plateau environments. To explore the expression pattern of homoeologous genes and their contributions to altitude adaptation, we transplanted natural allotetraploid I. sinensis (TnTnYnYn) along the altitude gradient for a long‐term, and harvested them in summer and winter, respectively. One year after transplanting, it still lived well, even in the extreme environment of the Qinghai‐Tibet Plateau. Then, we performed high‐throughput RNA sequencing to measure their gene expression level. A total of 7801 homoeologous genes were expressed, among which 5786 were identified as shared expression in different altitudes and seasons. We further found that altitude variations could change the subgenome bias trend of I. sinensis, but season could not. Moreover, the functions of uniquely expressed genes indicated that temperature might be an important restrictive factor during the adaptation process. Through the analysis of DEGs and uniquely expressed genes, we found that Y subgenome provided more contributions to high altitude adaptation than T subgenome. These adaptive traits to high altitude may be inherited from its plateau progenitor I. yunguiensis. Through weighted gene co‐expression network analysis, pentatricopeptide repeats gene family and glycerophospholipid metabolism pathway were considered to play important roles in high‐altitude adaptation. Totally, this study will enrich our understanding of allopolyploid in environmental adaptation.Allopolyploids are believed to inherit the genetic characteristics of its progenitors and exhibit stronger adaptability and vigor. The allotetraploid  Isoetes taiwanensis and Isoetes yunguiensis (diploid progenitors) were used as references to separately obtain the two subgenomes transcriptome expression data of Isoetes sinensis (subgenome T and Y).Through transcriptomic analysis, we demonstrated that the potential adaptation of I. sinensis to high altitude inherited from its plateau progenitor, I. yunguiensis.Long‐term transplanting experiment was performed to simulate real local habitats. The genomes of  However, few studies have focused on the effects of environmental factors on the expression of homoeologous genes. Even though there are some controlled experiments involving the response of homoeologous gene expression to environmental temperature , while I. yunguiensis occurs at the Yunnan‐Guizhou Plateau (YGP)  and I. orientalis (6n = 66), occur at low‐altitude area on the Middle‐lower Yangtze Plain (MYP) , a mid‐altitude , and a low‐altitude . To avoid the interference caused by transplanting to our study, we choose to sample after 1 year. We sampled the fresh leaves during summer and winter. Then the RNA sequencing of all samples were performed. In short‐term adaptation, the regulation of gene expression can serve as a mechanistic link from genetic and cellular processes to the physiological responses that allow organisms to rapidly accommodate the changing environments  and I. yunguiensis (unpublished data) genome were integrated and used as reference genome in this study. The software HISAT2 was used to map the clean reads to reference genome (version 2.2.1) . To further get the homoeologous gene expression, we used OrthoFinder (version 2.5.4)  < .001 were identified as DEGs. All homoeologous genes were annotated by online website eggNOG‐mapper  .DESeq2 method (version: 1.34.0) was used to identify DEGs between T and Y subgenome  was used to construct homoeologous gene co‐expression networks for I. sinensis. Genes that clustered in modules were associated with altitude and seasonal mean temperature (SMT)  exceeded 0.90 indicating that the correlations of gene expression between three biological replicates of each group were high  clean reads were obtained from 834.87 million raw reads. All Q20 and Q30 percentages of 18 samples exceeded 96.79% and 91.80%, indicating that our sequencing data was of high quality for subsequent analysis. An average 63.46% of reads per sample were uniquely mapped to the integrated reference genome Table . Most ofh Figure .3.2I. sinensis transplanted in WH‐low and KM‐mid were always biased toward Y subgenome while biased toward T subgenome in LS‐high. However, statistical results indicated that the degree of all bias was very slight that most ratios of subgenome bias were in the range of 50% ± 2%.We studied the shared and all homoeologous gene expression, respectively. Ultimately, a total of 5786 homoeologous gene pairs were identified as shared expression that expressed in all six different spatiotemporal conditions Table . In summHierarchical clustering revealed different expression bias patterns, including genes having consistent bias, distinct bias to different environments, or no bias. For instance, areas I, III, and II showed consistent T‐biased, Y‐biased and no bias, respectively Figure . The heaWe further analyzed all homoeologous genes in six groups. In summer, 6445, 6295 and 6170 homoeologous genes were detected in WH‐low, KM‐mid, and LS‐high, while the corresponding counts in winter were 6434, 6568 and 6351, respectively. More detailed information was provided in Table 3.3I. sinensis that transplanted at three altitudes. In summer, 229 genes exhibited expression bias toward T subgenome, while 346 genes toward Y subgenome  were detected in summer and winter, respectively . Among all of them, 38 pathways were from Y‐biased genes, while only 7 pathways were from T‐biased genes. We further compared these pathways between different seasons and between different altitudes. Notably, for Y‐biased genes, ribosome pathway was significantly enriched in all three altitudes, phosphatidylinositol signaling system pathway was significantly enriched in KM‐mid and LS‐high (plateau areas), and photosynthesis pathway was significantly enriched in WH‐low and KM‐mid. Moreover, several unique pathways were significantly enriched in specific altitude. For example, carotenoid biosynthesis were uniquely enriched in KM‐mid. More detail information was displayed in Figure Furthermore, we, respectively, performed KEGG enrichment analysis of the DEGs in 12 groups which contained different seasons, altitudes, and subgenome bias, for example, Summer‐WH‐Tbias, Summer‐WH‐Ybias   gene family Figure , while tFrom a quantitative point of view, gene expression may be more affected by seasonal mean temperature. The result of redundancy analysis (RDA) indicated that both altitude and SMT contributed variously to gene expression Figure . As expe4I. sinensis inherited the potential of adapting to plateau from its high altitude adapted parents I. yunguiensis. To test the hypothesis, we design the transplanting experiment. One year after transplanting, it still lived well, even in the extreme environment of the Qinghai‐Tibet Plateau. Through comparative transcriptome analysis, we measured the contribution of T and Y subgenomes to environmental adaptation. In conclusion, the allopolyploid I. sinensis is adaptive to plateau and the potential of adapting to plateau may inherit from its high altitude adapted progenitor.Polyploids are characterized by increased genomic content and genome size, which potentially provide strong tolerance to environmental stresses and increase the possibility to colonize new habitat. Previous studies of plant polyploidization support the potential for increased adaptability in polyploid lineages . We thus speculate that I. sinensis retains the characteristics inherited from its parent I. yunguiensis during its long evolutionary history, and these potential characteristics manifested in the process of retransplantation to high altitude areas.Cold stress is another important stress that can prevent the expression of genetic potential. Cold stress can directly inhibit the metabolic reactions  were all belonging to PPR gene family. PPR proteins are encoded by nuclear genome, but they are mostly targeted to plastids and mitochondria ; formal analysis (lead); methodology (lead); writing – original draft (lead). Xiao‐lei Yu: Writing – review and editing (lead). Yu‐jiao Yang: Methodology (supporting). Zhu‐yifu Chen: Formal analysis (supporting). Shu‐qi Zhao: Data curation (supporting). Xin‐zhong Li: Investigation (supporting). Wen‐cai Zhang: Investigation (supporting). Chen‐lai Liu: Formal analysis (supporting). Xiao‐yan Li: Supervision (supporting). Xing Liu: Writing – review and editing (lead).https://www.ncbi.nlm.nih.gov/bioproject/PRJNA857183.This article has earned an Open Data badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at Table S1Click here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.Figure S1Click here for additional data file.Figure S2Click here for additional data file.


Antiretroviral drugs have dramatically improved the prognosis of HIV‐infected patients, with strikingly reduced morbidity and mortality. However, long‐term use can be associated with signs of premature aging. Highly active antiretroviral therapy generally comprises two nucleoside reverse transcriptase inhibitors (NRTIs), with one of three additional antiretroviral drug classes, including protease inhibitors (PIs). One commonality between mitochondrial dysfunction (induced by NRTIs) and defects in lamin A (induced by PIs) is they can cause or accelerate cellular senescence, a state of essentially irreversible growth arrest, and the secretion of many bioactive molecules collectively known as the senescence‐associated secretory phenotype (SASP). We hypothesized that senescent cells increase following treatment with certain HIV therapies. We compared the effects of two distinct HIV PIs: ritonavir‐boosted atazanavir (ATV/r) and ritonavir‐boosted darunavir (DRN/r), used in combination treatments for HIV infection. Upon ATV/r, but not DRN/r, treatment, cells arrested growth, displayed multiple features of senescence, and expressed significantly upregulated levels of many SASP factors. Furthermore, mice receiving sustained ATV/r treatment showed an increase in senescent cells and age‐related decline in physiological function. However, removing treatment reversed the features of senescence observed in vivo and cell culture. Given how these features disappeared with drug removal, certain features of senescence may not be prognostic as defined by an irreversible growth arrest. Importantly, for patients that are treated or have been treated with ATV/r, our data suggest that switching to another PI that does not promote premature aging conditions (DRN/r) may improve the associated age‐related complications. Patients infected with HIV are at increased risk for multiple diseases of aging. Here, Kuehnemann et. al. show that treating cells and mice with protease inhibitors (PIs) used in antiretroviral therapies for HIV induces many characteristics of cellular senescence. Surprisingly, senescent phenotypes triggered by PI treatment were reversed after removal of the drug, and treated mice regained lost physiological function. These findings suggest that intermittent treatment or switching to therapies that do not cause senescence could reduce PI‐induced premature aging. It is characterized by a loss of physical integrity, leading to a progressive decline in cellular and tissue function over time. This decline increases the risks for many age‐related diseases, including atherosclerosis, heart failure, sarcopenia, renal failure, osteoporosis and neurodegenerative diseases such as Alzheimer's and Parkinson's diseases Campisi, . The divClinical studies show that HIV‐infected patients have on average a 10‐year shorter life expectancy relative to those without infection  for the treatment of HIV infection. A HAART regimen is generally a combination of two nucleoside reverse transcriptase inhibitors (NRTIs), with a third inhibitor from one of three antiretroviral drug classes including an integrase strand transfer inhibitor, a nonnucleoside reverse transcriptase inhibitor (NNRTI), or a protease inhibitor (PI)  , reduced EdU incorporation, and significantly upregulated levels of SASP factors. Upon ATV/r removal, however, many cells resumed proliferation. T Furthermore, mice receiving sustained ATV/r treatment showed increased features of senescence and deficits in function, including decreased heart function. When ATV/r was removed, mice regained physiological heart function, commensurate with a loss of senescence markers. Since these features are used to identify senescent cells in vivo, our results suggest that certain features of senescence may not be prognostic of cellular senescence as defined by irreversiblegrowth arrest. Biomedically, these results matter for patients who are taking/have taken HIV protease inhibitors (PIs)—going off these medications and/or switching to another PI that does not promote premature aging conditions .Here, we show that the PI atazanavir, boosted with ritonavir (ATV/r), induces features of senescence which, surprisingly, are reversible upon removal of the drug. Cells cultured in the presence of ATV/r exhibit features of cellular senescence includingupregulated senescence‐associated β‐galactosidase  activity, increased expression of the tumor suppressors p1622.1To examine the effects of HIV PIs on senescent phenotypes, we treated human IMR‐90 fibroblasts in culture with atazanavir boosted with ritonavir, as administered in patients. Ritonavir, although itself a PI, is generally used as a pharmacokinetic enhancer in combination therapies to increase the effectiveness of HIV medicines. As fibrosis‐associated genes were found elevated in HIV patients taking anti‐retroviral therapies (ART) , another commonly prescribed HIV PI combination. Although several PIs, including atazanavir, are implicated in pro‐aging effects through inhibition of the metalloproteinase ZMPSTE24, which cleaves farnesyl‐prelamin A to mature unfarnesylated lamin A (LMNA)  or DRN/r‐treated cells , monomeric red fluorescent protein (mRFP), and truncated herpes simplex virus 1 (HSV‐1) thymidine kinase (HSV‐TK) , normalized for mouse body surface area  for 8 weeks, and then removed the drug for 10 weeks, after which we assessed physiological function and collected tissues for analyses Figure . Mice ree Figure . Furthert Figure . We fount Figure . Whole bt Figure , suggestWaf1 expression increased in the heart, then decreased 8 weeks after drug removal  treatment with ATV/r results in features of senescence, including a SASP, in relevant human and mouse cell types in culture, (ii) ATV/r‐treated cells undergo a p53‐dependent growth arrest that is ultimately reversible, (iii) mice administered ATV/r show accelerated aging phenotypes, (iv) features of senescence appear at sites of ATV/r‐induced pathology, and (v) cessation of ATV/r treatment reverses these senescence features.Upon treatment with ATV/r, we show multiple features characteristic of senescent cells that are not prognostic of irreversible growth arrest. These data raise questions about the accuracy of using these features as biomarkers of senescent cells if we define senescence by irreversible growth arrest. Since these features are used to identify senescent cells in vivo, and here we show we can reproduce many of these features without irreversibility, perhaps some of these features do not necessarily mark cells that are irreversibly growth arrested. Comparing outcomes between the senescence characteristics imposed by treatment with doxorubicin (doxo), a chemotherapeutic drug known to induce senescence , as immune cells attracted by the SASP can remove nearby damaged cells  were cultured in Dulbecco's Modified Eagle Medium  supplemented with penicillin and streptomycin (5000 U/ml and 5000 μg/ml)  and 10% fetal bovine serum . Mouse dermal fibroblasts were isolated from the dorsal skin of 3‐month‐old mice as described  or drugs dissolved in DMSO at 10–20 μM (Cmax plasma concentrations) according to the literature  with the Direct‐zol RNA MiniPrep Kit (Genesee Scientific) as recommended by the supplier. mRNA was extracted, followed by reverse transcription . cDNA synthesis and qRT‐PCR were performed as described  transthoracic echocardiographic analysis was performed using a high frequency (20–46 MHz) Visualsonics Vevo 2100 micro‐ultrasound system with the echocardiography MS‐400 transducer . Individual mice were placed on a heating pad (37°C) and anesthetized with isoflurane at concentration of 1.5–2%. Data acquisition was performed B‐, and M modes, from parasternal long axis view. Left ventricle volumes, diameters and wall thicknesses were calculated using VevoStrain software .4.7For cell cultures, a Renilla Luciferase Assay System (Promega) was used according to the manufacturer's protocol, measuring luminescence with a luminometer. For in vivo studies, mice were injected i.p. with 15 μg of Xenolight RediJect Coelentarazine h . 25 min later, the mice were anesthetized with isofluorane, and luminescence measured using the Xenogen IVIS‐200 Optical In Vivo Imaging System at 5 min medium binning .4.8WAF1, Cell Signaling #2947, 1:1000; p‐p53, Cell Signaling #9289, 1:1000; HMGB1, abcam# ab18256, 1:2000; prelamin A, Millipore Sigma #mabt858, 1:500; β‐actin, Sigma‐Aldrich #A2228, 1:10000). Membranes were washed and incubated with horseradish peroxidase‐conjugated  secondary antibodies for 45 min at room temperature and washed again. Signals were detected by enhanced chemiluminescence.Cells were washed with cold PBS, lysed, and subjected to SDS‐PAGE using 4%–12% Bis‐Tris gels. Separated proteins were transferred to polyvinylidene fluoride membranes, which were blocked and incubated overnight at 4°C with anti‐rabbit primary antibodies . For each condition and replicate, cells were counted and 7000 cells seeded into 8‐well culture slides coated with poly‐lysine . After 24 h, the staining assay was performed as per the manufacturer's protocol. For each experiment, approximately 100–150 cells were counted.4.10To trace multiple generations of cells in culture using dye dilution and flow cytometry, the CellTrace™ Violet Cell Proliferation Kit was used according to the manufacturers protocol . CellTrace™ Violet dye was diluted to a final concentration of 5 μM in PBS in a final volume of 1 ml. Cells were then incubated for 20 min at room temperature, protected from light. To remove free dye, 5 ml of culture medium containing 10% FBS was added to the cells, incubated for 5 min, and then the cells were centrifuged and resuspended in FACS buffer .4.11Cells were analyzed using a BD FACS Aria (Becton Dickinson). CellTrace™ Violet was excited with the 405 nm laser and detected with the 450/50 band passfilter. The detector voltage was established using unlabeled controls fully on‐scale, then labeled cells were run to confirm they were fully on‐scale. If >5% of labeled cells were off‐scale, voltage was reduced as needed to bring them on‐scale and re‐acquire unlabeled controls. After voltages were configured, fluorescent Rainbow 6‐Peak Calibration Particles were acquired and mean intensity values from all peaks that were well resolved and fully on‐scale in each detector were recorded. To ensure consistency and enable direct comparison of data collected on separate days, the recorded intensities were used as target values when re‐establishing detector voltages on subsequent days. Data were analyzed using FlowJo_v10.6.2. The stained samples were gated at day 0 to encompass >95% of events, and the same gates were used at day 4 to ascertain the frequency of undivided cells.4.12t test. Differences between means were considered significant when values were p < 0.05 or lower (*); ns denotes nonsignificant. Data are presented as mean values ± SD for in vivo and cell culture experiments. All culture experiments were replicated at least three times.Statistics were assessed using GraphPad Software. For in vivo experiments with multiple comparisons, one‐way ANOVA and Sidak test for multiple comparisons were used. For pairwise comparisons, data were analyzed using the unpaired two‐tailed Student CK, CW and JC designed the experiments; CK and JWBH conducted the experiments; CK, PYD, CW, SM and JC analyzed the data; CK, PYD, JWBH, CW and JC wrote and edited the manuscript.JC is a founder and stockholder of Unity Biotechnology, which is developing drugs to clear senescent cells. JC and SM are partially supported by ONO Pharmaceuticals to explore senolytic mechanisms. The other authors declare no competing financial interests.Figures S1‐S6.Click here for additional data file.


Hypertriglyceridemia is associated with subclinical atherosclerosis and vascular inflammation even when low-density lipoprotein cholesterol levels are normal. However, few cohort studies on hypertriglyceridemia have been conducted in males with higher susceptibility to human immunodeficiency virus (HIV)-related deterioration of arterial structure and function. Our objective was to investigate the incidence of hypertriglyceridemia during treatment with combination antiretroviral therapy (cART) in males with HIV and explore its related risk factors.In this retrospective study, we included 309 males living with HIV (median age 31 years [interquartile range 26–42.5]) who initiated cART treatment in our hospital from January 2013 to December 2018. We collected follow-up data on serum triglycerides and other related information as of June 31, 2021. A Cox proportional hazards regression model was used to analyze the related risk factors.P = 0.004}, body mass index (BMI) ≥ 24.0 kg/m2 , borderline high triglyceride at baseline , and 3TC + zidovudine (AZT) + EFV regimen , or 3TC + TDF + lopinavir/ritonavir (LPV/r) regimen  were independent risk factors for hypertriglyceridemia.In 666.7 person-years, hypertriglyceridemia occurred in 140 patients (triglyceride ≥2.3 mmol/L [200 mg/dL]), and the incidence rate was 21.0 per 100 person-years . Multiple Cox regression analysis showed that baseline CD4/CD8 ratio < 0.20 : 1.381–5.296]; During the course of cART treatment, the incidence of hypertriglyceridemia in males with HIV was high. The main risk factors influencing its occurrence are a low baseline CD4/CD8 ratio, overweight and obesity, and the use of AZT or LPV/r in the cART regimen. With the advent of combination antiretroviral therapy (cART) with high antiviral efficiency, human immunodeficiency virus (HIV) infection has changed from a fatal to a chronic disorder, but the clinical use of cART has been associated with a wide range of metabolic disorders , 2. FurtLow-level inflammation and chronic immune activation act on lipids through various mechanisms, making them more prone to atherosclerosis . Heighte+ T lymphocyte count; and (4) the time interval from diagnosis to initial treatment was < 12 months. Exclusion criteria were: (1) patients who were taking lipid-lowering drugs; (2) baseline serum TG ≥ 2.3 mmol/L (200 mg/dL); (3) patients who were treated with cART before follow-up; and (4) those with tumors, autoimmune disease, kidney failure, and liver cirrhosis.This was a retrospective cohort study of people living with HIV who initiated cART treatment at the Fifth Medical Center of PLA General Hospital from January 1, 2013 to December 31, 2018. Inclusion criteria were: (1) individuals aged 18 years or older; (2) males living with HIV; (3) relevant clinical data such as baseline serum triglyceride (TG) levels, baseline HIV ribonucleic acid (RNA) level, and baseline CD4For eligible participants, serum TG levels and other relevant clinical data were collected from patients up to June 31, 2021. During the first 3 months after the start of cART, patients were followed up approximately once a month and then every 3 months . A patient was considered lost to follow-up if the interval between visits exceeded 12 months, or if the patient’s lipid data were missing for > 12 months.The primary endpoint was hypertriglyceridemia (serum TG ≥ 2.3 mmol/L [200 mg/dL]). The occurrence of serum TG ≥ 2.3 mmol/L was considered a target event during the follow-up period . For patients who switched to cART regimens during the follow-up, the first appearance of TG ≥ 2.3 mmol/L was taken as a node, and the cART regimen at that time was recorded. The relevant information of the participants included in the group mainly included demographic and clinical characteristics, and laboratory results. The demographic and clinical data collected included age, sex, body weight, height, mode of HIV acquisition, cART regimen, education level, time of HIV diagnosis, and presence of diabetes. Laboratory tests included TG (mmol/L), plasma glucose (mmol/L), total cholesterol (mmol/L), CD4 cell count (cells/μL), CD8 cell count (cells/μL), plasma HIV RNA (copies/mL), and alanine aminotransferase .According to current clinical practice guidelines for the treatment of dyslipidemia, it is recommended to start using statins when the serum TG > 2.3 mmol/L (200 mg/dL) . Hypertr2): BMI ≥ 24.0 kg/m2 was defined as being overweight or obese; 18.5–23.9 kg/m2 as normal; and <18.5 kg/m2 as being underweight [The diagnosis of HIV infection was made in accordance with the diagnostic criteria of the Chinese guidelines for the diagnosis and treatment of HIV/acquired immunodeficiency syndrome (AIDS) (2021 edition)” . Body maerweight . Liver ierweight . Hyperglerweight .All analyses were performed using Statistical Package for the Social Sciences (SPSS) version 25.0  or GraphPad Prism 9. The Shapiro–Wilk test was used to test whether the data conformed to a normal distribution. When continuous variables were normally distributed, the mean and standard deviation (SD) were used. When continuous variables were non-normally distributed, the median with interquartile range (IQR) was used. Categorical variables were summarized as numbers (%).As patients entered the cohort at different times, the incidence density was calculated to account for the occurrence of hypertriglyceridemia during follow-up. Person-years were counted on an individual basis, starting from the date cART treatment was initiated and ending with the time of hypertriglyceridemia detection, a follow-up period of 48 months, discontinuation of the cART regimen, last lipid test, or June 31, 2021, the final date of the follow-up, whichever occurred first. The incidence of hypertriglyceridemia was defined as the number of new cases during the follow-up period divided by the number of person-years of follow-up. The cumulative incidence and Kaplan–Meier curves of hypertriglyceridemia were drawn using GraphPad Prism 9, and the results of the log-rank test were calculated using GraphPad Prism 9. The approximate normal distribution method was used to calculate the 95% confidence interval (CI) for the incidence rate.P < 0.20 in univariate analysis were entered into a multiple Cox proportional hazard model. All reported P-values were two-sided. Statistical significance was set at P < 0.05. The adverse effects of dyslipidemia on blood vessels are a relatively slow process. In the first few months of initiating cART for people living with HIV, the relevant indicators may fluctuate due to the sudden effects of antiviral drugs. To make the research results more practical, we excluded events within 3 months of the baseline.Cox proportional hazards models were used to analyze risk factors for hypertriglyceridemia. Variables with A total of 309 males with HIV met the study criteria from January 1, 2013 to June 31, 2018 , CD8 cell count ≥1000 cells/μL (P = 0.045), HIV RNA load ≥5 log10 copies/mL (P = 0.017), borderline high TG at baseline (P < 0.001), 3TC + AZT + EFV (P < 0.001), and 3TC + TDF + LPV/r (P < 0.001) regimen were risk factors for hypertriglyceridemia in patients receiving cART treatment. Age, mode of HIV acquisition, education, ALT, plasma glucose, and TC levels were not significantly associated with hypertriglyceridemia in the univariate analysis , during which 140 patients developed hypertriglyceridemia with an incidence of 21.0/100 person-years , CD4/CD8 ratio < 0.20 , borderline high TG , 3TC + AZT + EFV regimen , or 3TC + TDF + LPV/r regimen  were statistically significant  during antiretroviral treatment in cART-naive people was 227.7/1000 person-years . One posMultivariate analysis showed that the incidence of hypertriglyceridemia in overweight or obese patients was higher than in those with normal weight. This suggests that overweight or obese patients should pay more attention to cART regimen selection. Simultaneously, they should pay attention to changing their lifestyle and reducing their weight to the normal range as soon as possible. The presence of CD4 cell counts < 200 cells/μL before treatment in people living with HIV usually indicates that the patient’s immunity has been severely damaged. At this time, even if patients with CD4 cell counts < 200 cells/μL receive the same cART regimen as those with CD4 cell counts > 350 cells/μL, patients’ immunity recovery is weak, never reaching the level achieved by patients with CD4 cell counts > 350 cells/μL. Grunfeld C et al. found that TG levels were above normal in patients with CD4 cell counts < 200 and approximately twice normal in symptomatic patients with AIDS . In our The HIV viral load is associated with the disease stage in patients and is relatively high in those who have just been infected or are in the AIDS phase. Statistically significant differences in the incidence of hypertriglyceridemia between the subgroup with geometric viral loads between 4.00 and 4.99 and the subgroup with geometric viral loads < 4.00 may reflect differences in the patient’s immunity. Patients with geometric viral loads < 4.00 had greater immunity. Because our inclusion criteria required < 12 months from the initial diagnosis to treatment, the time to infection for most patients may be short, and the virus causes less damage to the patient’s immunity. This may explain the small difference between the subgroup with a geometric viral load > 5.00 and the subgroup with a geometric viral load < 4.00. In combination with the associated CD4/CD8 ratio results, the high risk of hypertriglyceridemia may be associated with the low baseline immunity in our patient. The patient’s low immunity may lead to an increase in susceptibility to related inflammation and infection and eventually to an increase in TG levels .Our study showed that the risk of hypertriglyceridemia in patients with borderline high TG was 3.457 times higher than that in those with normal TG levels. For these patients, special lipid-lowering measures, such as strengthening physical exercise or targeted dietary changes, should be carried out at the same time as choosing the cART regimen. Ozemek et al. showed that physical activity and sports training could improve the cardiovascular health of adults living with HIV, and even in the presence of cardiovascular disease, it can help reduce the mortality rate . Among tThe advantages of our study lie in its longitudinal cohort design, which is more reliable than that of the cross-sectional design in confirming causality, and it is of great theoretical and practical significance to guide clinical practice by analyzing related risk factors from a clinical perspective. However, there are also limitations, such as only male participants and single centers containing only people living with HIV-1 were included. In the future, females can be included in the study, and the sample size can be expanded to verify further and promote the conclusion of this study.2, or borderline high TG at baseline during antiviral treatment, and pay special attention to patients who use the antiviral regimen, including AZT or LPV/r, and check their TG levels regularly.In conclusion, the incidence of hypertriglyceridemia was high in male cART-naive participants during antiviral treatment. Therefore, clinicians should pay attention to the changes in TG levels of patients with baseline CD4/CD8 ratio < 0.20, BMI ≥ 24.0 kg/m


Obesity is accompanied by hormonal, inflammatory and endothelial alterations. These alterations induce a stimulation of several other mechanisms that contribute to the hypertensive state and to increase the cardiovascular morbidity. This pilot, open - label, single- center, prospective clinical trial aimed to evaluate the effect of very low- calorie ketogenic diet (VLCKD) on blood pressure (BP) in women with of obesity and hypertension.A total of 137 women, who met the inclusion criteria and accepted to adhere to VLCKD, were consecutively enrolled. Assessment of anthropometric parameters , body composition , systolic (SBP) and diastolic blood pressure (DBP) and blood sample collection were carried out at baseline and after 45 days of the active phase of VLCKD.After VLCKD all the women experienced a significant reduction in body weight and an overall improvement of body composition parameters. In addition, high sensitivity C reactive protein (hs- CRP) levels were significantly diminished (p < 0.001), while phase angle (PhA) increased by almost 9% (p < 0.001). Interestingly, both SBP and DBP were significantly improved . At baseline, SBP and DBP showed statistically significant correlations with body mass index (BMI), waist circumference, hs-CRP levels, PhA, total body water (TBW), extracellular water (ECW), Na / K ratio, and fat mass. Even after VLCKD, all correlations among SBP and DBP with the study variables were statistically significant, except for the association between DBP and Na / K ratio. Changes (%) in both SBP and DBP were associated with ∆BMI%, ∆PhA% and ∆hs- CRP levels (p < 0.001). In addition, only ∆SBP% was associated with ∆waist circumference (p = 0.017), ∆TBW (p = 0.017), and ∆fat mass (p < 0.001); while only ∆DBP% was associated with ∆ECW (p = 0.018), and ∆Na / K ratio (p = 0.048). After adjusting for ∆BMI, ∆WC, ∆PhA, ∆TBW, and ∆fat mass, the correlation between changes in ∆SBP and ∆hs -CRP levels remained statistically significant (p < 0.001). Similarly, the correlation between ∆DBP and ∆hs- CRP levels also remained statistically significant after adjustment for ∆BMI, ∆PhA, ∆Na / K ratio, and ∆ECW (p < 0.001). From multiple regression analysis ∆hs- CRP levels seemed to be the main predictor of changes of BP (p < 0.001).VLCKD reduces BP in women with of obesity and hypertension in a safely manner. Cardiovascular diseases (CVD) continue to be the main cause of mortality and morbidity worldwide . DespiteEpidemiologic data consistently demonstrate a direct linear relationship between elevated body mass index (BMI) and blood pressure: as weight increases, blood pressure increases . CorrespWhile bariatric surgery has proven to be a viable treatment option for the severe obesity and its complications, there is clearly a need for less invasive alternatives. Recent research has suggested that long-acting analogs of the gut hormone, glucagon-like peptide 1 (GLP-1) as an anti-obesity treatment . Few yeaThe combination drug therapy of naltrexone / bupropion is another anti -obesity drug option. However, in phase 3 trials, naltrexone / bupropion was associated with placebo adjusted increase in SBP and diastolic blood pressure (DBP) changes of + 1.5 mmHg and + 1.2 mmHg, respectively, at 1 year . Blood pThus, unlike bariatric surgery, which is associated with a significant reduction in blood pressure and the use of antihypertensive drugs for most patients , anti-obCentro Italiano per la cura e il Benessere del paziente con Obesità “C.I.B.O.” and European Association for the Study of Obesity (EASO), Collaborating Centre for Obesity Management “EASO-COMs”), Clinical Medicine and Surgery Department, University of Naples Federico II, Naples, Italy. This clinical study has previously been approved by the Federico II Ethical Committee with protocol number 50 / 20. All women were informed of the study design and purpose, subsequently giving their informed consent.This pilot, open-label, single-center, prospective clinical trial was initiated by the investigator to evaluate the effect of VLCKD on blood pressure in a population of women with obesity and newly diagnosed treatment-naïve hypertensive patients. Women were enlisted from May 2020 to March 2022 at the Unit of Endocrinology, Obesity Unit , hearth failure (NYHA III–IV), unstable angina, liver failure, a recent stroke or myocardial infarction (< 12 months), cardiac arrhythmias, atrio-ventricular block, unbalanced hypokalaemia, hypo-hyperthyroidism, active / severe infections, eating disorders and other severe mental illnesses, pregnancy and breastfeeding, alcohol and substance abuse; unable to give informed consent. In addition, women taking the following drugs were excluded from the study: hypertensive medication and beta-blockers, statins, corticosteroids, opioid pain medications, nonsteroidal anti-inflammatory drugs and drugs or dietary supplements known to affect weight and / or fat loss.A total of 137 Caucasian women that had a history of failed dietary attempts and the desire to lose weight, were consecutively enrolled in this clinical study. The fundamental requirement was that the individuals to be enrolled were women patients with obesity and newly diagnosed hypertensive treatment naïve patients with blood pressure high normal and grade 1 hypertension, according to the 2018 ESC / ESH Guidelines for the management of arterial hypertension . InclusiAt baseline (T0), all women underwent to an endocrinological and nutritional visit to evaluate the exclusion and inclusion criteria in the study Fig. . When en2(m2)]. According to BMI values, women were classified into overweight (25.0–29.9 kg / m2), grade I obesity (30.0–34.9 kg / m2), grade II obesity (35.0–39.9 kg / m2), and grade III obesity (≥ 40.0 kg / m2) [Anthropometric measurements were performed by the same-trained health care operator at a time between 8 and 10 am. During the measurements, taken after an overnight fast, women wore light clothing and no shoes. Body weight was determined using a calibrated beam scale  to the nearest 0.1 kg while as well as height was measured using a wall -mounted stadiometer  to the nearest 0.5 cm. BMI was calculated as weight divided by the square of height [weight (kg)/heightkg / m2) . Waist ckg / m2) . A nonelkg / m2) .c) / resistance (R) (180/π) from the conditions at 50 kHz. Fat mass (FM), fat-free mass (FFM) and skeletal muscle mass (MM), expressed in kg, and total body water (TBW), intracellular water (ICW) and extracellular water (ECW), expressed in liters, were evaluated.Body composition was assessed by multifrequency bioelectrical impedance analysis  performed by the same nutritionist with the same BIA device to reduce variability between devices and between observers. The examination was performed according to the guidelines of the European Society of Parenteral and Enteral Nutrition (ESPEN) . SpecifiSBP and DBP were measured in all women three times using a random zero sphygmomanometer  after 10 min sitting at rest, finally reporting the average of the last two values.Women who fulfilled inclusion / exclusion criteria underwent VLCKD protocol with total meal replacement consisting of three main phases  . A commeCompliance to the recommendations for VLCKD and physical activity was assessed by weekly individual telephone counseling by an endocrinologist and nutritionist. In addition, once a week, women were asked to measure β-hydroxybutyrate from capillary blood using test strips . More specifically, at baseline and at the end of the dietary intervention (day 45), β-hydroxybutyrate levels were measured by the nutritionist at the outpatient clinic. Furthermore, blood ketone levels were also checked directly by the women at home once a week, in the morning on an empty stomach, preferably at the same time, and the levels were reported to the nutritionist during the telephone interview, which as carried out once a week.Venous blood samples were collected in the morning between 8.00 and 10.00 am after an overnight fast (at least 8 h). Hs-CRP levels were assessed by a high-sensitivity nephelometric assay . The lower limit of detection was 0.01 mg / L and the CV of intra- and interassay was < 7%. According to Centers for Disease Control and Prevention and the American Heart Association, based on baseline and 45 days of VLCKD hs-CRP levels, all women were further classified into three groups: low cardiovascular risk (< 1.0 mg / L), intermediate cardiovascular risk (1.0–3.0 mg / L), and high cardiovascular risk (≥ 3.0 mg / L) .t-test. The chi square (χ2) test was used to test differences in frequency distribution across categories of physical activity, BMI, and hs-CRP levels. Pearson’s correlation was used to assess the association among SBP and DBP with anthropometric measurements, inflammatory biomarker, and body composition parameters of the study population at baseline and after 45 days of VLCKD (active phase). In addition, Pearson’s correlation also been used for assessing the association among percentage changes (delta ∆%) pre / post intervention of study parameters . Two partial correlations were performed to adjust the associations among ∆SBP and ∆DBP with ∆hs -CRP levels for confounding factors . Finally, two multiple linear regression analysis models (stepwise method), expressed as R2, Beta (β) and t, with the SBP and DBP as a dependent variable  were used to estimate the predictive value of anthropometric measurements, inflammatory biomarker, and body composition parameters. Variables with a variance inflation factor (VIF) > 10 were excluded to avoid multicollinearity. The p-values below 5% were considered statistically significant. Because this was a pilot study, no power calculations were performed. Therefore, all findings need to be confirmed by larger clinical trials.Were included in this statistical analysis all women who completed the study, including all anthropometric measurements, blood pressure, inflammatory biomarker, and body composition parameters at baseline and after 45 days of VLCKD (active phase). These data were analyzed using MedCalc® package  and the IBM SPSS Statistics Software . The Kolmogorov–Smirnov test was used to test data distribution. Skewed parameters were normalized by logarithm transformation and re-converted into tables and figures. The mean ± standard deviation (SD) or percentages (%) were used to present the data. The outcomes between the baseline and after 45 days of VLCKD (active phase), in particular weight, BMI, WC, hs-CRP levels, PhA, TBW, ECW, Na / K ratio, FM, SBP, and DBP were compared using the Student’s paired A total of 137 women with obesity, aged 46.48 ± 10.19 years, met the inclusion / exclusion criteria, were included in these statistical analyses. All women were evaluated at baseline and after 45 days of VLCKD (active phase). Adherence to VLCKD was assessed and confirmed in all women by a qualified nutritionist through a telephone interview once a week. In detail, all women were called the day before the interview to instruct them to do the ketosis capillary test and the results were registered on the day of the interview by the nutritionist. In addition, patients were asked about their levels of physical activity. No patients changed their physical activity levels as required at the baseline visit .Table Interestingly, as showed in Fig. Table The correlation among changes in ∆SBP% and ∆DBP% with changes in anthropometric measurements, inflammatory biomarker, and body composition parameters were reported in Table = 0.542, p < 0.001).As reported in Fig. Similarly, the correlation between ∆DBP and ∆hs -CRP levels also remained statistically significant after adjustment for ∆BMI, ∆PhA, ∆Na / K ratio, and ∆ECW , Fig. To compare the relative predictive power of ∆BMI, ∆WC, ∆hs - CRP levels, ∆PhA, ∆TBW, and ∆FM associated with ∆SBP, we performed a multiple regression analysis (Model 1), while to compare the relative predictive power of ∆BMI, ∆hs - CRP levels, ∆PhA, ∆Na / K ratio, and ∆ECW associated with ∆DBP, we performed a second multiple regression analysis showed as Model 2. In the first model, ∆hs - CRP levels were entered at the first step (p < 0.001), followed by the ∆FM (p < 0.001). In the second model, only ∆hs - CRP levels were entered at the first step (p < 0.001); Table To our knowledge, this is the first study to provide information on the effects of VLCKD on blood pressure in women with obesity and hypertension. Not surprisingly, all the women experienced a significant reduction in body weight and an overall improvement of body composition after 45 days of the active phase of VLCKD. As a new result we reported that VLCKD reduced blood pressure after a short period of active phase in women with obesity and hypertension. In particular, we found that SBP and DBP decreased by -12.89% and − 10.93%, respectively. This result seems particularly promising since, in addition to confirming the safety and efficacy of VLCKD as a tool for managing obesity, no other dietary intervention reported in the literature to date seems to achieve the same results in such a short time. Regarding the currently most widely used nutritional approach for the treatment of hypertension, the Dietary Approaches to Stop Hypertension (DASH), this produces significant reductions in blood pressure (− 5.2 mmHg for SBP and − 2.60 mmHg for DBP) after an average of 8 weeks of intervention . The othInterestingly, at the end of the active phase of VLCKD, the levels of low - grade inflammation were statistically reduced in our women, and it was evidenced by significantly lower and higher hs - CRP levels and PhA, respectively. Moreover, at baseline and after 45 days, both SBP and DBP values correlated with hs - CRP levels, body weight and body composition parameters. The percentage change in blood pressure was also significantly correlated with the percentage changes in hs - CRP levels, PhA, and BMI. The positive association between SBP and DBP with hs - CRP levels were still evident after adjusting for confounding variables, in particular the percentage of weight loss. Finally, the reduction in inflammatory state induced by VLCKD was the main predictor of changes in blood pressure, including SBP and DBP after 45 days of VLCKD. As well - known inflammation contributes to the pathogenesis of hypertension ; indeed,Finally, we found significant reductions in TBW (− 2.21%), ECW -6.43%), Na / K ratio (-1.68%) and this led us to hypothesize that changes in body water compartments observed at the end of the active phase of VLCKD may have contributed to the observed changes in blood pressure. To our knowledge this is the first study evaluating the relation between body water compartments and blood pressure in women with obesity and hypertension. In this regard, we found out positive correlations of SBP and DBP values with BIA analysis parameters and, among other, statistically significant correlations with TBW, ECW and Na / K ratio. In agreement with our findings, in a study of 72 children to assess the relationship between body water compartments (assessed by BIA) and blood pressure, SBP and DPB correlated significantly with TBW and ECW %, Na / K. A suffiWe certainly want to mention some limitations of our study. Firstly, the design of our study did not include a control arm so we cannot establish with certainty the superiority of VLCKD in reducing blood pressure compared to other dietary interventions. However, we found no data in the literature superior to ours on the same outcome with other dietary interventions. Second, we assessed blood pressure using a random zero sphygmomanometer, an approach that has limitations due to poor reproducibility, observer, and patient variability and the ‘white coat’ effect . In futu The study provides the first evidence of safety and efficacy of VLCKD in the management of obesity and hypertension. Due to its beneficial metabolic and anti-inflammatory effects Fig. , VLCKD s