Caveolin‐1 (Cav‐1) is an integral membrane protein present in all organelles, responsible for regulating and integrating multiple signals as a platform. Mitochondria are extremely adaptable to external cues in chronic liver diseases, and expression of Cav‐1 may affect mitochondrial flexibility in hepatic stellate cells (HSCs) activation. We previously demonstrated that exogenous expression of Cav‐1 was sufficient to increase some classical markers of activation in HSCs. Here, we aimed to evaluate the influence of exogenous expression and knockdown of Cav‐1 on regulating the mitochondrial plasticity, metabolism, endoplasmic reticulum (ER)‐mitochondria distance, and lysosomal activity in HSCs. To characterize the mitochondrial, lysosomal morphology, and ER‐mitochondria distance, we perform transmission electron microscope analysis. We accessed mitochondria and lysosomal networks and functions through a confocal microscope and flow cytometry. The expression of mitochondrial machinery fusion/fission genes was examined by real‐time polymerase chain reaction. Total and mitochondrial cholesterol content was measured using Amplex Red. To define energy metabolism, we used the Oroboros system in the cells. We report that GRX cells with exogenous expression or knockdown of Cav‐1 changed mitochondrial morphometric parameters, OXPHOS metabolism, ER‐mitochondria distance, lysosomal activity, and may change the activation state of HSC. This study highlights that Cav‐1 may modulate mitochondrial function and structural reorganization in HSC activation, being a potential candidate marker for chronic liver diseases and a molecular target for therapeutic intervention. Mission shRNA nontarget  was used as a nonsilencing control. GRX2.32.3.1Cells were fixed in 4% paraformaldehyde plus 2.5% glutaraldehyde in 0.1 M phosphate‐buffer solution (PBS). Afterward, the samples were fixed in 1% osmium tetroxide solution (Sigma Aldrich), gradually dehydrated in acetone (Merck), and soaked in epon resin. Ultrafine cuts were obtained and counterstained with 1% uranyl acetate (Merck) and with 1% lead citrate (Merck). Ultrastructural imaging was obtained by transmission electron microscopy  at an 80‐kV acceleration voltage  and perimeter (P) were measured by delineating mitochondria. The shape coefficient Z was used to quantify the mitochondrial elongation, using the following equation: Shape Z = P/√Â. Low shape Z values indicate more rounded mitochondria, while high values indicate more elongated mitochondria. The electron density of mitochondria was evaluated by measuring the grayscale of 8‐bits images (255 shades of gray). According to this method, 0 = is equivalent to absolute black and 255 = is equivalent to absolute white. The real distance between ER and outer mitochondrial membrane (OMM) was selected within 10–30 nm from ER and manually traced and quantified (images with 0.2 µm to 75k) by two blind researchers , shape 2.3.3Samples were prepared as previously described  with an ×60 (numeric aperture 1.35) oil‐immersion objective . For each sample, images of six fields were acquired and processed with Olympus FluoView FV1000 software. For LYSR, cell fluorescence intensity was analyzed using ImageJ  and Lysotracker Red DND 99 (LYRS) (Invitrogen) and were cultured in appropriate glass‐bottom culture plates . Images were collected using Olympus FV1000 laser‐scanning confocal microscopy at 37°C with humidified 5% CO2.4.2For immunofluorescence labeling, cells were fixed with 4% paraformaldehyde before incubation with the primary antibody Cav‐1 . Sequentially, cells were incubated with the secondary antibody Alexa Fluor 555 . All images were acquired in the Olympus FV1000 laser‐scanning confocal microscope as previously described. All experiments were performed at least four times for each sample. Images from six random fields were acquired, and cell fluorescence intensity was analyzed using ImageJ . After, cells were scratched in PBS and transferred to a precooled glass Potter homogenizer with lysis buffer . Cells were homogenized with 7 min strokes at medium speed and centrifuged for 5 min at 100g, 4°C. Next, the supernatant was centrifuged at 4°C for 15 min at 10,000g. Then, the supernatant was stored (cytosolic fraction), while the mitochondrial pellet was resuspended in lysis buffer for cholesterol quantification  and was reverse‐transcribed with SuperScript‐II (Invitrogen). RNA expression levels were quantified by measuring the SYBR Green on Step One Plus real‐time cycler (Applied‐Biosystems). The geNorm software was used to indicate the most stable gene  and used to design specific primers in (Table www.idtdna.com)  and LYSR (Invitrogen), respectively. Briefly, cells were cultured in 12‐well plates, trypsinized, and incubated for 30 min with Mitotracker® Red or LYRS, following the manufacturer's instruction. All data  were acquired with a FACS cytometry system  controlled by Cell Quest software (BD Bioscience), then analyzed using FCS Express 4 Software (De Novo Software).2.82 per second per number of cells. The software DatLab 4  was used for data acquisition. The protocol used consisted of the application of oligomycin (Sigma®), carbonyl cyanide‐4‐(trifluoromethoxy) phenylhydrazone (Sigma®), rotenone (Sigma®), and antimycin A (Sigma®)  in the Oroboros Oxygraph‐2k in standard configuration, with 2 ml final volume on both chambers, at 37°C, and 750 rpm stirrer speed. For all experiments, 0.8 million cells were used per chamber and the data was acquired in pmol of O2.9p ≤ .05. Data are expressed as means ± standard deviations, as well as the number of experiments performed, and are indicated in each figure. All analyses were performed using the statistical software GraphPad Prism 6 for Windows .Samples were tested for normality using D'Agostino and Pearson tests. Data were obtained at least from three biological replicates and submitted to one‐way analysis of variance followed by Bonferroni's post hoc test. Statistical significance was accepted at 33.1EGFP‐Cav1 cell line that constitutively expresses more than 83% Cav‐1 protein than GRX cells   ; thus, this gene was used as a constitutive normalizer in the RT‐PCR‐based experiments. Exogenous expression or knockdown of Cav‐1 led to important changes in genes responsible for the mitochondrial fusion and fission processes. GRXEGFP‐Cav1 cells presented a decrease in the dynamin 1 like (DRP1) and optic atrophy protein 1 (OPA1) mRNA expression while GRXGFPshCav1 cells showed an increase in the DRP1 mRNA expression. Furthermore, we found no changes in RNA ribosomal 2 (RnR2) expression, which is an important gene for mitochondria biogenesis  and perimeter , which may indicate that there is no change in the lipid composition of mitochondrial membranes. Also, we checked that mitochondrial cholesterol content correlates with ER‐OMM distance , Cav‐1, and DRP1 mRNA expression .Based on the final ranking of geNorm we found that MFN1 was the most stable gene .Cav‐1 coordinates the autophagic process to deliver cytoplasm compounds to lysosomes, a process that can interfere with mitochondrial dynamics and can drive HSC activation (Shi et al., C Figure . InteresC Figure . Moreove4GFPshCav1 cell line, a robust tool to understand the role of Cav‐1 in HSC. Considering our previous results, we investigate the influence of Cav‐1 on mitochondrial bioenergetic, dynamics, and inter‐organelle communication through modulating the mitochondrial plasticity in HSC activation.Over the years, the efficiency of mitochondrial respiration, loss of mitochondrial plasticity, and increase of Cav‐1 expression in HSC have been considered critical for the progression of liver fibrosis (Longo et al., 2 expression. On the other hand, the knockdown of Cav‐1 increases the fission process, and decreases the ER‐mitochondrial distance, without changing the metabolic demand and mitochondrial biogenesis. These results suggest that an increase in mitochondrial fusion or decrease of mitochondrial fission by exogenous expression of Cav‐1 is a direct response to the increased energetic demand during HSC activation (Trivedi et al., The first question of this study sought to determine the actual contribution of exogenous expression or knockdown of Cav‐1 on mitochondrial plasticity, dynamics, and respiration in HSCs activation. Knockdown of Cav‐1 showed a smaller mitochondrial area and perimeter, more individual dots with thin mitochondrial tubules distributed along with the periphery of the cells, increase in DRP1 expression, similarly to less activated HSCs. However, exogenous expression of Cav‐1 demonstrated an increase in area and perimeter, a highly fused ring‐shaped mitochondrial network extended towards the cell periphery, decrease in DRP1 and OPA1 expression, equivalently with more activated HSCs (Friedman et al., EGFP‐Cav1 is the protective mechanism of Cav‐1 signaling in the formation of supercomplexes with the ETC. Importantly, dynamics changes in mitochondrial architecture can promote supercomplexes assembly as a regulatory mechanism for the bioenergetic adaptation to metabolic demand in HSC activation (Baker et al., Interestingly, we observed that alterations in Cav‐1 expression did not affect mitochondrial crest density but increased respiration when submitted to metabolic challenges. This characteristic is a feature of mitochondrial plasticity to adapt to the increase of electrons flow and OXPHOS production, driving to activation of HSCs in the early stages of liver damage (Longo et al., We found that exogenous expression of Cav‐1 triggered an increase in lysosomal bioactivity, ER enlargement, and changes in inter‐organelle communication, a mechanism that could drive HSC to activation (Luo et al., In conclusion, we observed that Cav‐1 can modulate mitochondrial plasticity, flexibility, and function during HSCs activation. Future studies are required to understand the mechanistic role and how Cav‐1 can influence mitochondrial plasticity during the process of HSC transdifferentiation. Altogether, our findings suggest that Cav‐1 is a promising candidate for further studies aimed at identifying new treatments for chronic liver diseases.Mariana Ilha: Conceptualization, methodology, validation, data curation, formal analysis, investigation, visualization, and writing – original draft. Leo A. Meira Martins: Methodology, investigation, formal analysis, and writing – review and editing. Ketlen da Silveira Moraes: Methodology and validation. Camila K. Dias: Methodology, validation, investigation, and writing – review and editing. Marcos P. Thomé: Methodology, validation, and investigation. Fernanda Petry: Methodology, validation, and investigation. Francieli Rohden: Methodology, validation, and investigation. Radovan Borojevic: Resources. Vera M. T. Trindade: Investigation, resources, writing – review and editing, and supervision. Fábio Klamt: Investigation, resources, funding acquisition, writing – review and editing, and supervision. Florência Barbé‐Tuana: Investigation, resources, writing – review and editing, and supervision. Guido Lenz: Investigation, resources, writing – review and editing, and supervision. Fátima C. R. Guma: Conceptualization, investigation, resources, writing – review and editing, supervision, funding acquisition, and project administration. All the authors studied and approved the final manuscript.The authors declare no conflict of interest.Supporting information.Click here for additional data file.


The role of social networks, especially weaker ties , in self‐management of long‐term consequences of cancer is unexplored. This study aimed to explore the structure of cancer survivors' social networks and their contribution to self‐management support and health‐related quality of life (HRQoL).The study used a sequential, exploratory mixed methods design. Phase 1 surveyed 349 lymphoma, colorectal, breast and prostate cancer survivors. Phase 2 analysed 20 semi‐structured interviews with respondents recruited from Phase 1.p < 0.0005 adj. R2 = 0.631). These findings were explored in Phase 2, identifying underlying mechanisms. Four themes were identified: disrupted networks after cancer treatment; navigating formal support and building individual capacity; peer networks and self‐management knowledge and linking networks to enable adaptation in recovery.Phase 1 results suggested participants' HRQoL increased if they participated in an exercise group, if their self‐management skills increased, and social distress and negative illness perception decreased (This study suggests engagement with community groups, particularly those not directly related to illness management and social interaction with weak ties, make a valuable contribution to self‐management support, increase HRQoL and enhance well‐being. Seminal studies demonstrated relationships between increased social engagement and reductions in mortality and morbidity . Research nurses identified and recruited survey participants. Principal investigator (G. H. J.) received survey responses and recruited interview participants.2.3Over 11 months 621 participants meeting the eligibility criteria were approached across 5 Trusts, with 349 consenting to Phase 1 of the study, eliciting a 56% response rate   in three domains: illness work (e.g. managing medication), day to day work (e.g. housework) and emotional work  and indicated how close members lived to them, for example, a short walk. Face validity was achieved through PPI review and pilot study.A social network assessment tool was developed based on the name generator approach    and 44% men (n = 154). Mean age was 63 years (standard deviation 13.04). Women with breast cancer were the largest diagnostic group 39% (n = 138), followed by men with prostate cancer 23% (n = 82), lymphoma 21% (n = 75) and bowel cancer 15% (n = 54). Participants were most frequently within 2–6 months of completing cancer treatment . The sample was 98% white (n = 341). Most respondents 71.6% (n = 250) were married or in a civil partnership. Educational attainment ranged from 15.1% (n = 53) having no educational attainment and 32% (n = 110) having a first degree. Most had retired 51.2% (n = 183) , health professionals  and children . Spouses were frequently listed , reflecting that most of the 349 participants were married. There were 1.7% (n = 36) pets, indicating 10% of participants listed an animal. The overall mean number of members listed in a participant's social network (network size) was 6 (SD 4.7). The overall diversity mean  was 3.6 (SD 1.8). The most commonly attended social groups (attended at least monthly) were social clubs and hobby groups (45.0%), sports and exercise groups (37.5%) and participation in volunteering groups (21.8%); 12% of participants in the study also attended religious groups or places of worship. Overall, 84.2% of participants were satisfied with their current social opportunities , everyday work (M = 4.2) and emotional work (M = 4.4) Figure . The low3.1.3R2) F = 5.637, p < 0.001, adj R2 = 0.164. There was evidence supporting non‐zero effects for five of the 12 variables: age; working status; time spent self‐managing; self‐management skills and self‐management monitoring. Social network engagement explained 16.4% of the variation in HRQoL (according to the adjusted R2) F = 4.494, p < 0.001, adj. R2 = 0.164. There was evidence supporting non‐zero effects for: satisfaction with social opportunities, weekly, monthly and less often sport/exercise participation. Network contribution to self‐management characteristics did not explain variation with HRQoL, F = 1.785, p > 0.05 (0.116), adj. R2 = 0.012. There was evidence supporting non‐zero effects for one variable, emotional work. Social distress characteristics explained 60.2% of the variation in HRQoL (according to the adjusted R2) F = 253.287,Participant characteristics explained 16.4% of the variation in HRQoL  F = 37.757, p < 0.0005, adj. R2 = 0.631. There was evidence supporting non‐zero effects for: self‐management skills, sport participation , social distress and illness perception p < 0.05. I sort of focused in and I focussed on the family because they were the ones looking after me. So when I got back home I got even smaller and it  stayed basically with a small family …. I stopped socialising outside with friends or contacts I had. I chose, well I did it slowly. Participants described significant reshaping of their social networks during and after treatment. The impact of network reshaping influenced participants' ability to engage with opportunities and resources to support self‐management in recovery. When cancer treatment was complete, participants were viewed by network members as returning to health and support withdrawn, or participants shielded close family and friends from their ongoing distress, concerned that they would be overburdened.3.2.2ID05, female, high SESI did not think it was something I would need, but actually it (counselling at cancer centre) was the right thing …. You cannot load on people that are close to you all the time, I needed someone who could listen to my upset. Cancer support offered by third sector networks gave participants the opportunity to engage in cancer‐related self‐management resources free of charge, at different time points during treatment and recovery, which promoted an increased sense of control and well‐being. Cancer support centres were valued because they offered illness‐related support beyond family and friends, consciously relieving carers from further support. Centres were also valued because they created a sense of equality between cancer survivors through the shared experience of cancer, which took priority over SES.3.2.3There's a lady at our work went through a similar thing … she told me with reference to her (bowel) symptoms because we speak together, it took her three years after her operation before she felt she was fit enough, like before the op, so I mean I can see where she is coming from now. Peer to peer weak tie networks with other cancer survivors appeared to be an acceptable and credible approach to share and engage in self‐management support during and after treatment. Peer network engagement created subtle opportunities to share knowledge and resources to support self‐management, which was more acceptable than formal peer support groups. Peer networks developed through weak tie groups, when the purpose was not focused on cancer management (e.g. work) were highly valued by participants. For some participants with low SES and family dominate networks, employment provided the only opportunity to develop such networks. The combination of a shared non‐cancer environment, engagement with weaker ties and shared illness experience appeared to have a beneficial impact on shaping participants' abilities to self‐manage the consequences of cancer.3.2.4ID09, female, high SESMy friend, who went to the same exercise class with me before I was ill suggested that exercise teacher might help me at home. I did not know she could do that. So the exercise teacher sees me 1:1, she has kept a slot for me, I'm very lucky. Recreational, work and faith networks facilitated day‐to‐day adaptation in recovery. Established and new networks of non‐illness and non‐family members recognised the need to support adaptation to new physical and emotional circumstances after cancer treatment, enabling participants to engage in a full range of network activities. These weak tie networks made a valuable contribution to participants' ability to navigate their redefined relationships by giving a sense of personal fulfilment without burdening family and enabling successful adaptation to a new normal in the wider social environment.44.1Our findings indicate that weak tie network groups appear to have a positive impact on self‐management and HRQoL through engagement in a range of opportunities, resources and experiences, suggesting such networks could have a valuable and previously overlooked role in supporting cancer survivors' to self‐manage long‐term health needs.Close family members are frequently turned to in times of acute need  and the potential burden that self‐management support could put on network members , educated, middle income and did not reflect greater ethnic diversity or a broader SES population.The HRQoL measure Fact‐G /National Institute for Health Research (NIHR) for this research project. Professor Richardson is a National Institute for Health Research (NIHR) Senior Investigator. The views expressed in this article are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. The authors have no conflict of interests to declare.


Multiple myeloma (MM) is known for its immune disturbance and patients suffering from MM are thus vulnerable to opportunistic infections, including herpes zoster (HZ). As HZ infection remarkably affects patients' quality of life and poses huge economic burdens on the health system, we aim to identify the risk factors of HZ infection and evaluate the effects of different dosages, types, and durations of anti‐HZ prophylaxis drugs to prevent HZ infection.551 MM patients at Taipei Veterans General Hospital in Taiwan between January 1, 2009 and August 31, 2021 were restrospectively analyzed. The patients' baseline characteristics were recorded. The primary endpoint of the study was the incidence of HZ infection among the studied patient population. Due to the lack of cost coverage from Taiwanese public health insurance on HZ prophylaxis drugs, the use of anti‐HZ drugs mainly depends on physicians' preferences and patients' choices.p = 0.008).In our study, prophylaxis was given to 283 of the patients. In the multivariate analysis, we included non‐prophylaxis, age ≥ 60, corrected serum calcium ≥12 mg/dl, serum creatinine ≥2 mg/dl, serum β2‐microglobulin ≥5500 mg/L, autologous stem cell transplant (SCT), and allogeneic SCT for analysis. Our results demonstrated that the non‐prophylaxis group  and patients receiving autologous SCT  and allogeneic SCT  had higher risk of HZ infection. The difference in dosage and types of anti‐HZ drugs showed similar protective effects. In patients who stopped anti‐HZ prophylaxis before active cancer‐related treatment, a higher risk of getting HZ infection compared to the corresponding group was also observed (adjusted HR 3.09, 95% CI 1.35–7.07, We concluded that MM patients should receive HZ prophylaxis drugs while receiving active cancer‐related treatment. Patients receiving SCT are also at high risk of getting HZ infection, even under prophylaxis. Patients with multiple myeloma should receive herpes zoster prophylaxis drugs while receiving active cancer‐related treatment. Those who receive stem cell transplants, either autologous or allogeneic, are also at high risk of getting herpes zoster infection, even under prophylaxis. Nearly 10%–60% of patients with MM develops HZ infection after cancer‐related treatment,Over the years, multiple studies have proposed that prophylactic use of antivirus drugs is beneficial for HZ prevention, with acyclovir dosage ranging from 200 mg once dailyIn this study, we aim to determine whether preventive use of acyclovir/valacyclovir in patients with MM produces meaningful beneficial clinical outcomes and whether different dosages and durations produce different effects. We conducted a retrospective cohort study to compare clinical outcomes in MM patients receiving prophylaxis or not during a 10‐year follow‐up after diagnosis.22.1This is a retrospective cohort study. The study enrolled patients who were newly diagnosed with MM at Taipei Veterans General Hospital between January 2009 and August 2021. All patients were age 20 or older to be eligible for this study. Diagnosis of MM followed International Myeloma Working Group criteria,2.2We retrospectively reviewed patients' medical records and assembled patients' characteristics compromised of age, sex, Eastern Cooperative Oncology Group (ECOG) performance status, disease classification based on Durie‐Salmon stage and the International Staging System (ISS), immunoglobulin heavy and light chain isotype, and whether the patient has had an organ transplant. Comorbidities including autoimmune disease, human immunodeficiency virus (HIV), heart failure, chronic pulmonary disease, and diabetes mellitus were also collected. The percentage of plasma cells in bone marrow was also recorded. Initial laboratory data, including hemoglobin level, platelet count, serum levels of albumin, corrected calcium, creatinine, lactate dehydrogenase (LDH), β2‐microglobulin, and free light chain ratio were all obtained at the time of diagnosis of MM. As for treatment, we gathered each patient's therapeutic regimens, including the proteasome inhibitor bortezomib, carfilzomib, ixazomib, and the anti‐CD38 agent daratumumab, as well as whether the patient received an autologous/allogeneic SCT.2.3Potential cases of HZ infection were identified by extracting records on all inpatient and outpatient encounters occurring between January 2009 and August 2021. We defined the HZ infection cases based on a description in the medical records. The starting date of vesicle development or HZ infection diagnosis date was documented. Clinical records were reviewed for prophylactic drug type, dosage, and duration.2.4n) and the proportion (%). Categorical variables were compared in categorical forms by using chi‐square tests. The primary endpoint of the study was the incidence of HZ infection among the studied patient population. The accumulated HZ infection incidence from the date of MM diagnosis until the date of HZ infection diagnosis, death, loss of follow‐up, or the end of August 2021. The Kaplan–Meier curve (log rank test) was adopted to analyze the impact of the use of HZ prophylaxis on the cumulative incidence of HZ infection. It was used to measure the fraction of patients suffering from HZ infection for a certain amount of time after the initiation of the treatment. Differences between prophylaxis were tested using a log‐rank test. A Cox proportional hazards model was used to assess the risk factors for HZ infection. Hazard ratios (HRs) and 95% confidence intervals (CIs) for HZ infection risks were calculated, respectively, for age, sex, ECOG performance status, comorbidities, and initial laboratory data to assess the risk factor of HZ infection. As for prophylaxis and treatment, such as bortezomib, autologous SCT or allogeneic SCT, we used a time‐dependent covariate in the Cox proportional hazards model to prevent immortal time bias. A univariate model was used to identify risk factor. Those factors whose p value <0.1 were included in our multivariate analysis.We analyzed the baseline characteristics of MM patients using the total number  and STATA statistical software, version 15.1 (StataCorp). The significance level was set as 33.1Our study enrolled 711 patients with newly diagnosed MM and measurement of prophylaxis treatment of HZ, from January 1, 2009 to August 30, 2021. Of them, 111 patients who were not active MM , 47 patients who were initially diagnosed and treated partially in other hospitals, and two patients without pathology were excluded. The final study cohort included 551 MM patients. Among them, 283 (51.4%) patients used either acyclovir or valacyclovir to prevent HZ infection, while the remaining 268 (48.6%) patients did not receive prophylaxis treatment . Durie‐Salmon stages I, II, III accounted for 34.9%, 24.7%, and 40.5%, whereas 20.9%, 39.6%, and 39.2% of the patients constituted ISS stages I, II, and III, respectively. A total of 283 (51.4%) patients had MM of the IgG type and 148 (26.9%) patients had IgA type. Regarding comorbidities, 104 (18.9%) patients had diabetes mellitus, and 90 (16.3%) had heart failure. Plasma cells in bone marrow equal to or more than 60% were found in 53.8%, and hemoglobin of less than 10 g/dl was documented as 59.3%. For treatment, 113 (20.5%) patients received autologous SCT, and six (1.1%) underwent allogeneic SCT. The use of proteasome inhibitors, including bortezomib (59.9%), carfilzomib (2.7%), and ixazomib (1.3%), and the anti‐CD38 daratumumab (6.4%) were also recorded.3.2p = 0.002). The prophylaxis group was also predominantly female (p = 0.001) and had a better ECOG (p < 0.001). Patients with heart failure seemed to have received fewer prophylaxis drugs (p = 0.010). A lower percentage of prophylaxis was observed in patients with serum β2‐microglobulin ≥3500 mg/L (p = 0.029), whereas a higher percentage was noted in patients with light chain ratio > 100 (p = 0.011). 80 out of 113 patients (p < 0.001) received prophylaxis in the autologous SCT group. Six patients received allogeneic SCT, but only one of them (p = 0.114) received prophylaxis anti‐HZ drugs. In patients treated with either proteasome inhibitors or anti‐CD38, higher ratios of using prophylaxis anti‐HZ drugs were also noted. 235 out of 330 patients were prescribed with prophylaxis drugs in the group using bortezomib (p < 0.001), as were 12 out of 15 (p = 0.024) in the carfilzomib group, 7 out of 7 (p = 0.015) in the ixazomib group, and 33 out of 35 (p < 0.001) in the daratumumab group , receiving autologous SCT  and receiving allogeneic SCT  were associated with a higher rate of HZ infection. These results suggest that the use of prophylaxis drugs and a regimen containing transplantations or not are major factors for determining the risk of HZ infection. Because transplantations were the ultimate treatment goal in eligible patients, the use of a prophylaxis drug might be the only manipulation we could modify. The cumulative incidence of HZ infection was significantly higher in the non‐prophylaxis group, compared to its prophylaxis counterpart  and 1.33  for the 200 mg and 400 mg acyclovir groups, respectively. As for comparison, patients without prophylaxis had incidence rate up to 20.6 cases per 100 person‐years .In the dosage analysis of prophylaxis medication, 49.8% of prophylaxis MM patients had received valacyclovir 500 mg per day, 35.6% received acyclovir 200 mg per day, while the remaining 14.6% received 400 mg acyclovir per day , which might be because the numbers of those who got HZ infevtion was small, and is not significant enough to reach a conslusion. Further studies regarding the duration of prophylaxis following cancer‐related treatment should be explored.As increased susceptibility to HZ infection has been linked to proteasome inhibitors, especially bortezomib,3.6It has been reported that patients still get HZ infection even while undergoing prophylaxis treatment.3.7We further investigated the impact of HZ infection on MM patients' overall survival Table . In the p = 0.257) after controlling for confounding factors. Patients in non‐prophylaxis group also showed no significantly predictive factor of mortality, with HR 1.28 . Patients age ≥ 60 , male , ECOG ≥2 , HIV infection , platelet <100,000/μl , serum albumin <3.5 g/dl , LDH ≥250 U/L , and patients who received allogeneic stem cell transplantations  had a higher mortality rate in the multivariate analysis. Interestingly, patients receiving autologous SCT has lower mortality  in univariate analysis but this difference was not observed in multivariate analysis . Carfilzomib was shown intriguingly to include a lower mortality rate .In the multivariate analysis, HZ infection did not increase mortality rate, with adjusted HR 1.25 ; data curation ; investigation ; methodology ; project administration ; writing – original draft ; writing – review and editing . Chun‐Kuang Tsai: Conceptualization ; data curation ; methodology ; project administration ; writing – original draft . Chiu‐Mei Yeh: Formal analysis ; investigation ; methodology ; writing – original draft ; writing – review and editing . Tin Chian: Data curation . Yao‐Chung Liu: Supervision . Hao‐Yuan Wang: Supervision . Po‐Shen Ko: Supervision . Ting‐An Lin: Supervision . Liang‐Tsai Hsiao: Supervision . Po‐Min Chen: Supervision . Jyh‐Pyng Gau: Supervision . Chia‐Jen Liu: Conceptualization ; data curation ; formal analysis ; funding acquisition ; investigation ; methodology ; project administration ; resources ; software ; supervision ; validation .This study was supported by grants from Taipei Veterans General Hospital , the Ministry of Science and Technology , the Taiwan Clinical Oncology Research Foundation, the Szu‐Yuan Research Foundation of Internal Medicine, the Yen Tjing Ling Medical Foundation, the Melissa Lee Cancer Foundation, and the Chong Hin Loon Memorial Cancer and Biotherapy Research Center at National Yang‐Ming University. The funding sources had no role in the study design or conduct, or in the decision to submit it for publication.The authors declare that they have no conflict of interest.Data management and all statistical analysis were performed using SAS 9.4 software  and STATA statistical software, version 15.1 .This study has been approved by the Institutional Review Board at Taipei Veterans General Hospital.Figure S1Click here for additional data file.


We focused on determining the risk factors, thromboembolic events, and clinical course of New‐Onset Atrial Fibrillation (NOAF) among hospitalized coronavirus disease (COVID‐19) patients.This retrospective study was conducted in the major referral centers in Tehran, Iran. Of 1764 patients enrolled in the study from January 2020 until July 2021, 147 had NOAF, and 1617 had normal sinus rhythm. Univariate and multivariate Logistic regressions were employed accordingly to evaluate NOAF risk factors. The statistical assessments have been run utilizing SPSS 25.0 (SPSS) or R 3.6.3 software.p < 0.001), HF , bilateral grand‐glass opacification . For cases with thromboembolic events, NOAF was the most important prognostic factor . While evaluating the diagnostic ability of prognostic factors in detecting NOAF, Troponin‐I (Area under the curve [AUC] = 0.85), C‐Reactive Protein (AUC = 0.72), and d‐dimer (AUC = 0.65) had the most accurate sensitivity. Furthermore, the Kaplan‐Meier curves demonstrated that the survival rates diminished more steeply for patients with NOAF history.For the NOAF patients, the age was significantly higher, and the more prevalent comorbidities were metabolic syndrome, heart failure (HF), peripheral vascular disease, coronary artery disease, and liver cirrhosis. The multivariate analysis showed the established independent risk factors were; Troponin‐I (hazard ratio [HR] = 3.86; 95% confidence interval [CI] = 1.89−7.87; In hospitalized COVID‐19 patients with NOAF, the risk of thromboembolic events, hospital stay, and fatality are significantly higher. The established risk factors showed that patients with older age, higher inflammation states, and more severe clinical conditions based on CHADS2VASC‐score potentially need subsequent preventive strategies. Appropriate prophylactic anticoagulants, Initial management of cytokine storm, sufficient oxygen support, and reducing viral shedding could be of assistance in such patients. All the COVID‐19 patients have been admitted based on the COVID‐19 clinical criteria, approved by the World Health Organization,2.2Patients' information was gathered and analyzed from the hospital medical records and was comprised of demographical characteristics, clinical course during hospitalization, prior medical history, therapy, and in‐hospital outcome. The data were double‐checked independently by four medical researchers. Laboratory tests have been conducted upon admission to the hospital and during the hospitalization period when clinically indicated. Lung spiral computed tomographic scan, and echocardiography have been carried out upon clinical indications.2.3The NAOF was defined as the first occurrence of AF upon or during admission in the hospital wards or intensive care units (ICU) in those hospitalized with COVID‐19 and did not have any document or self‐statement regarding previous neglected or managed AF The report of NOAF was conducted in each of the following circumstances: (1) AF lasting for more than 1 h in the electrocardiogram, (2) AF with any duration of existence required cardioversion, (3) AF for which anticoagulation therapeutics were initiated based on CHA2DS2–VASc score criteria. Routine electrocardiogram, pulse rate, and oxygen saturation monitoring were conducted for all patients hospitalized in either wards or ICUs in our mentioned centers. The nursing alarm system report performed the initial suspicion of arrhythmia (nurse/patient ratio: 1/3). The final diagnosis of NOAF, requirement for cardioversion, and initiation of anticoagulants were considered and judged by skilled cardiologists, intensivists, or emergency medical professionals. Further, we excluded patients receiving each of the following high‐risk drugs leading to NOAF based on literature2.4The presence of AF and its subtypes were defined based on the recent AF guidelines. NOAF was described as the presence of an AF at ECG throughout the stay in the hospital that was not present upon admission.d‐dimer levels in primary laboratory findings, (2) being bed‐ridden or paralyzed, and (3) Clinical/radiological findings are suggestive of ARDS.Since there was not an organized and common internationally established decision on the administration of prophylactic anticoagulant therapy for hospitalized COVID patients during the time of performing this essay, we defined Prophylactic anticoagulation as administration of heparin‐based anticoagulant components with a prophylactic dose for whom included one of the following criteria upon hospitalization: (1) Noticeably elevated Since alcohol consumption has been shown as an independent factor for NOAF incidence, we added this variable in our history with the definition of >30 cc/day in men and >20 cc/day in women as the remarkable alcohol use.2.5χ2 test or Fisher's exact test; Continuous data having normal distribution have been provided as mean (standard deviation) then compared employing the independent t‐test or the Mann−Whitney U‐test when needed. The Wilcoxon test analyzed the skewed variables reported by medians and interquartile ranges. A Cox regression analysis was conducted to determine the factors substantially related to an elevated risk of NOAF. Moreover, for the Cox regression analysis, the number of occurrences restricted the number of variables. The variables chosen for the ultimate regression model were, in fact, the ones with more clinical significance based on the primary comparative analysis. The variables taken into account for the Cox regression model were age, body mass index (BMI), metabolic syndrome, dyslipidemia, diabetes, hypertension, heart failure (HF), chronic obstructive pulmonary diseases (COPD), chronic kidney disease (CKD), coronary artery diseases (CAD), CURB‐65 score, CHA2DS2VASC Score, creatinine, albumin, troponin‐I, d‐dimer, C‐reactive protein, Erythrocyte sedimentation rate, serum ferritin, bilateral ground‐glass opacification (GGO), diffuse lung infiltration, and pleural infusion. To investigate the risk factors associated with thromboembolic events among patients with NOAF, a multivariable logistic regression model has been utilized. The Hosmer‐Lemeshow test has been used to assess the fit of the multivariable logistic regression. To avoid overfitting the model, five variables were chosen for multivariable analysis according to clinical limitations and prior findings, with a focus on thrombotic events associated with mortality. Hence, history of NOAF, age, BMI, d‐dimer, and the presence of ARDS have been the six variables selected for the multivariable logistic regression model. We have also plotted Kaplan−Meier curves for within‐hospital death stratified by the occurrence of AF during hospitalization. For the sake of these curves, individuals discharged from the hospital were deemed to have survived because our follow‐up only included the duration of the hospitalization. In all analytical measures, statistical significance was defined as a two‐sided α of less than 0.05. The statistical assessments have been run utilizing SPSS 25.0 (SPSS) or R 3.6.3 software.The patients were categorized based on whether they were affected by NOAF or not as a comparison. A total of 1764 patients  were evaluated in the analysis. We attempted to base most of this study's analytical and interpretational works on a guideline developed by Assel et al.33.1p < 0.001). As displayed in Figure p = 0.81). The mean BMI has been recorded to be 28.9 in the NOAF group and 27.7 in the control group (p = 0.05). The rate of alcohol consumption has been similar in both study groups (p  = 0.65). However, the active smoking condition reported as pack‐year had an average of 9.4 years in the NOAF cohort, which was markedly greater than 8.2, the average of the control group (p < 0.001). As Table p < 0.001). Prevalence of metabolic syndrome, hypertension, diabetes, dyslipidemia, CKD, CAD, and HF was significantly more common among patients with NOAF; however previous transient ischemic attack, valvular heart disorders, peripheral vascular disease, liver cirrhosis, asthma, Infection with Human Immunodeficiency Virus and Hepatitis C Virus have not indicated a statistically noteworthy contrast between the two study groups. Among the history of drug usage before hospitalization, Angiotensin‐Converting‐Enzyme Inhibitors and Angiotensin‐II Blockers have shown to be of statistically greater use among patients of patients group . Other drugs, including statins, Beta‐blockers, Aspirin, Warfarin, and Oral direct anticoagulants, whilst having more usage among NOAF patients, did not show a statistically significant difference in comparison to the control group. Regarding clinical signs, the mean oral temperature upon admission was found to be 37.5°C for cases in the NOAF group and 36.9°C for cases in the control group; indicating a statistically remarkable contrast between the two study groups (p < 0.001); and SpO2 without the mask, with an average of 91.1% among all patients, had a mean of 89.8% in the NOAF cohort which was markedly smaller than the 91.3% mean in the control cohort (p = 0.004). Also, the NOAF group had significantly more CURB‐65 score  and CHA2DS2VASC score  comparing to control group upon admission. As an evaluation of inflammatory factors, the patients with NOAF had significantly greater levels of C‐reactive protein (CRP) (p < 0.001)., Erythrocyte sedimentation rate (ESR) (p = 0.003), serum ferritin (p = 0.005), and d‐dimer (p < 0.001) upon admission. In the evaluation of kidney function, glomerular infiltration rate  equation) showed a 46.3 ml/min average in the NOAF group and 75.5 ml/min average in the control group, which was significantly lower among the NOAF group (p < 0.001). Moreover, creatinine levels, with an average of 1.4 mg/dl in the total patient population, had a 1.6 mg/dl mean in the NOAF group, which was markedly higher than the 1.4 mg/dl mean in the control cohort (p < 0.001). Troponin‐I levels, with a mean of 0.01 ng/ml in the total patients population, had a 0.07 ng/ml average in the NOAF group, which was markedly greater than the 0.01 ng/ml mean in the control cohort (p < 0.001); Also, Brain Natriuretic Peptide (BNP), with an average of 54.6 pg/ml in the total patients' population, showed a 118.4 pg/ml average in the NOAF group which was outstandingly lower than 49.3 pg/ml average in the control group (p = 0.006). Additionally, we noted that patients with NOAF had significantly lower levels of Albumin and Magnesium upon hospitalization. Figure d‐dimer, ESR, and Ferritin, have been shown to have AUCs equal to 0.72, 0.65, 0.55, and 0.53, respectively. Eventually, in order for radiological comparison, we obtained that NOAF patients suffered from more severe patterns of lung involvement in which Bilateral GGO , Diffuse lung infiltration , and pleural effusion (14.2% vs. 6.06% p < 0.001) were found significantly more in NOAF group.A comprehensive list of patients' characteristics upon admission has been shown comparatively in Table 3.2p < 0.001), secondly, Antibiotic therapy by Azithromycin  and/or Ceftriaxone , and thirdly oxygen support with FiO2 ≥ 50 . Moreover, whilst hospitalization we found the following thromboembolic events occurs considerably more in NOAF patients comparing to control group: Cerebral infarction (1.4% vs. 0.2% p < 0.001), and Pulmonary embolism . Also, HF , and unfavorable bleeding events  were significantly found more among NOAF patients. However, no statistically significant contrast was noted between the prevalence of myocardial infarction between the two groups (p = 0.58). Additionally, the NOAF patients significantly suffered more form hospitalization period , and ICU admission .Among the administered therapeutics mentioned in Table 3.3p < 0.001). Figure Eventually, in‐hospital mortality, as the ultimate adverse outcome, was recorded for 17.7% of patients in the NOAF group, while for only 6.4% of individuals within the control group (3.4p < 0.001). The multivariate analysis on this resulted in an increased mortality risk of about 1.86 for a 5‐year increase in patients' age (p = 0.007). For a 1‐kg/m2 increase in BMI, the patients would have a 1.55 times more chance of dying in the NOAF group than in the control group (p = 0.014); and the multivariate analysis carried out on this, resulting in an increased mortality risk of about 1.03 for a 1‐kg/m2 increase in patients' BMI (p = 0.013); implying that there is an almost equal hazard in the two groups. In the presence of Metabolic Syndrome, the patients would have a 3.77 times more chance of dying in the NOAF group compared to the control group (p < 0.001). The multivariate assessment performed on this resulted in an increased mortality risk of about 1.66 (p = 0.034). If patients have hypertension, they would have a 2.05 times more chance of dying in the NOAF group in comparison with the control group (p = 0.001). The multivariate evaluation of this led to an elevated mortality risk of about 1.93 (p = 0.020). In the presence of diabetes, the patients would have a 1.85 times more chance of surviving in the control group compared to the NOAF group (p = 0.027). The multivariate analysis carried out on this led to statistically insignificant results. If the patients have dyslipidemia, they would have a 2.19 times more chance of dying in the NOAF group as against the control group (p = 0.003). The multivariate assessment performed on this brought on statistically insignificant results. In the presence of HF, the patients would be 3.58 times more likely to die in the NOAF cohort as against the Control cohort (p < 0.001). The multivariate evaluation conducted on this resulted in heightened mortality risk of about 2.54 (p < 0.001). If patients have CAD, they would have 1.65 times more chance of surviving in the control group as against the NOAF group (p = 0.027). The multivariate analysis carried out on this led to statistically insignificant results. In the presence of CKD, the patients would have 2.28 times more chance of dying in the NOAF group compared with the control group (p < 0.001). The multivariate assessment performed on this resulted in an increased mortality risk of about 1.88 (p = 0.001). If patients have COPD, they are 1.37 times more likely to die in the NOAF group than they are in the control group (p = 0.021). The multivariate evaluation carried out on this led to statistically insignificant results. For a 1‐point increase in CURB‐65 Score, the patients would have a 1.50 times more chance of surviving in the control group, in comparison with the NOAF group (p = 0.014). The multivariate analysis conducted on this, brought on statistically insignificant results. For a 1‐point rise in CHA2DS2VASC Score, the patients would be 1.77 times more likely to expire in the NOAF group, compared to the Control group (p = 0.004). The multivariate assessment on this resulted in an increased mortality risk of about 1.68 for a 1‐point increase in patients’ CHA2DS2VASC Score (p = 0.004). If creatinine levels increased by 0.1 mg/dL, the patients would have 1.75 times more chance of dying in the NOAF group as against the control group (p < 0.001). The multivariate evaluation of this led to an increased mortality risk of about 1.48 for a 0.1 mg/dl increase in patients' creatinine levels (p = 0.002). For a 0.1 g/dl increase in albumin levels, the patients would have a 1.57 times more chance of survival in the control group, in comparison with the NOAF group (p = 0.005). The multivariate analysis carried out on this resulted in elevated mortality risk of about 1.42 for a 0.1 g/dl increase in patients' albumin levels . If Troponin‐I levels increased by 0.01 ng/ml, the patients would be 4.89 times more likely to die in the NOAF cohort compared to the control cohort (p < 0.001). The multivariate evaluation of this led to a heightened mortality risk of about 3.86 for a 0.01 ng/ml increase in patients' Troponin‐I levels (p < 0.001). For a 10 ng/ml increase in d‐d‐dimer levels, the patients would have 2.46 times more chance of dying in the NOAF group as against the control group (p < 0.001). The multivariate assessment of this resulted in an increased mortality risk of about 1.56 for a 10 ng/ml increase in patients’ d‐dimer levels (p = 0.020). If CRP levels rose for 10 mg/ml, the patients would be 2.37 times more likely to die in the NOAF group in comparison with the control group (p < 0.001). The multivariate analysis carried out on this led to an elevated mortality risk of about 1.55 for a 10 mg/ml increase in patients’ CRP levels (p = 0.011). For a 10 mm/h increase in ESR levels, the patients would have 1.69 times more chance of dying in the NOAF group, compared to the Control group (p = 0.001). The multivariate analysis performed on this resulted in an increased mortality risk of about 1.51 for a 10 mm/h increase in patients' ESR levels (p = 0.009). If serum ferritin levels increased by 10 ng/ml, the patients would be 1.47 times more likely to expire in the NOAF group than in the control group (p = 0.003). The multivariate evaluation of this led to a heightened mortality risk of about 1.13 for a 10 ng/ml increase in patients' serum ferritin levels (p = 0.014). If the GGOs were radiologically present, the patients would have 3.87 times more chance of dying in the NOAF cohort as against the control group (p < 0.001). The multivariate assessment carried out on this, resulted in an increased mortality risk of about 2.26 (p = 0.002). If radiologic evidence of diffuse lung infiltration were present, the patients would be 1.46 times more likely to die in the NOAF group than they would be in the control group (p = 0.017). The multivariate analysis performed on this resulted in an increased mortality risk of about 1.48 (p = 0.029). If pleural effusion were radiologically present, the patients would have a 1.33 times more chance of survival in the NOAF group compared with the control group (p = 0.038). The multivariate analysis conducted on this led to statistically insignificant results.Table 3.5p < 0.001). The following prognostic factors of age , hospitalization duration , ARDS , CHA2DS2VASC score , BMI , CURB‐65 score , d‐dimer levels , active cancer , CRP , intubation , and active smoking  were respectively assessed as possible prognostic factors of thromboembolic events. Regarding to multivariate analysis, NOAF , d‐dimer , CHA2DS2VASC score , hospitalization duration , age , and BMI , have been evaluated as independent predictive factors for prognosis of thromboembolic events. Ultimately, it's concludable that NOAF is the most considerable prognostic factor for the occurrence of thromboembolic events.As shown in Table 4d‐dimer, (3) NOAF during hospitalization in COVID‐19 patients is an independent predictor of within‐hospital thromboembolic events, (4) NOAF is related to poorer clinical characteristics during the hospital stay, such as more prevalent bleeding episodes and more events in the combined end‐point , (5) And, NOAF has been linked to a lengthier hospital stay. Arrhythmogenic stimuli, such as persistent hypotension, insufficient oxygen supply, or past use of vasopressors, might enhance NOAF while having COVID‐19 disease, particularly in critical illness. According to this viewpoint, NOAF might be both a measure of illness severity and a possible factor in adverse results. In fact, we suggest the connection between COVID‐19 and AF is bilateral. Multiple studies have shown that AF might elevate the severity and incidence of COVID‐19; in turn, the COVID‐19 disease might favor further episodes of AF.d‐dimer and Troponin‐I, which correspond with the severity of COVID‐19 disease; and thirdly, administering corticosteroids, which, quite apart from potential drug effect, is commonly directed against those exhibiting the greatest hyperinflammation response. Supporting our findings, elevated inflammatory mediating factors such as Tumor Necrosis Factor‐α (TNF‐α), Interleukin‐6, and CRPIn this study, we found out that: (1) Factors including age, metabolic syndrome, arterial hypertension, HF, renal dysfunction, and diffused lung involvement acted as independent factors for the NOAF in patients with COVID‐19, (2) Levels of troponin‐I have a more accurate diagnostic role in the detection of NOAF rather than inflammatory factors of CRP, ESR, ferritin, and Notwithstanding, it is of essence to note that comparing NOAF patients with the control group of this cohort, the temperature on admission appeared subfebrile, and oxygen saturation reduced but not critically. However, the CURB‐65 Score was moderate‐high, and lung damage had been more significant in the AF group. But the difference between the groups was not that remarkable. Some biomarkers, such as NT‐pro‐BNP, reflected the presence and possible worsening of HF, which was more common in the NOAF group. It could be concludable that patients who were believed to develop AF were older and sicker individuals who were more likely to have AF irrespective of COVID‐19, and subsequently, the impact of COVID‐19 per se on the development of AF could be modest.5Of note, our study has multiple limitations: For starters, our cohorts were rather comparatively elderly, with an already higher incidence of cardiovascular disease. Because we could not exclude the remaining confounding effect, the predictive function of AF history should be considered as such. Meanwhile, significant clinical and demographic confounding factors were accounted for in the multivariable model.Second, since we didn't have access to follow‐up data after discharge, we were unable to analyze the occurrence and subsequent implications of NOAF after hospitalization.Third, in our study, AF was diagnosed using in‐hospital ECG and considered new‐onset if it was not present on admission; however, it is not thoroughly clear whether pre‐hospitalization/pre‐COVID‐19 AF was present, particularly with regard to paroxysmal AF.Fourth, the unavailability of long‐term ECG monitoring or Holter monitoring for all participants was one of the study's major weaknesses, raising the possibility that silent AF had gone unnoticed.Fifth, it is the possibility of underdiagnosing ischemic strokes due to the difficulties of conducting brain imaging studies on infected individuals. Sixth, since we excluded the patients with a previous history of high‐risk drugs for developing NOAF to decrease the effect of confounding factors, we did not assess the probable role of in‐hospital medications received upon COVID‐19 admission in our regression model. As data about the potential role of corticosteroid pulse therapy, antibacterial agents as co‐infection management, or antivirals such as Remdesivir on developing arrhythmias specifically regarding NOAF is scarce; it is of the essence for medical researchers to consider these factors for ongoing studies. Seventh, we could not consider all the potential risk factors of NOAF, such as obstructive sleep apnea, in our model regression. Last but not least, one should bear in mind that all the mentioned findings only apply to hospitalized individuals; nonhospitalized COVID‐19 cases may have different determinants of NOAF development and thereby different outcomes.62VASC score potentially need subsequent therapeutic strategies. Further research is required to develop approaches that can anticipate the development of new AF to recognize high‐risk individuals, offer early therapy, and thereby reduce the thromboembolic risks of COVID‐19 and AF.In conclusion, patients with NOAF in the setting of COVID‐19 have a poorer prognosis due to an increased risk of embolic events, hospital stay, intubation, and fatality. The development of AF in individuals with COVID‐19 has ramifications beyond the mere existence of the arrhythmia, and the prognosis in these patients is poorer than that in those with sinus rhythm. Patients with older age, higher inflammation states, and more severe clinical conditions based on CHADSFatemeh Sadat Rahimi: Conceptualization; data curation; formal analysis; investigation; methodology; visualization; writing − original draft; writing − review and editing. Siamak Afaghi: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Visualization; Writing − original draft; Writing − review and editing. Farzad Esmaeili Tarki: Conceptualization; Formal analysis; Methodology; Project administration; Supervision; Validation; Visualization; Writing − original draft; Writing − review and editing. Hossein Salehi Omran: Data curation; Methodology; Writing − original draft. Mohammad Hossein Nasirpour: Formal analysis; Methodology; Writing − original draft.The authors declare that no conflict of interest.The lead author Farzad Esmaeili Tarki affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned  have been explained.


NPC‐21 (EV2038) is a fully human monoclonal antibody that targets the antigenic domain 1 of glycoprotein B on the human cytomegalovirus (hCMV) envelope. NPC‐21 has been shown to have broadly neutralizing activity and to inhibit cell‐to‐cell transmission of hCMV in preclinical studies. It is currently in development for the prophylactic or preemptive treatment of hCMV in patients receiving a solid‐organ transplant or hematopoietic stem cell transplant. A first‐in‐human phase 1 study was conducted to assess the pharmacokinetics, safety, and tolerability of NPC‐21 in healthy adult men. Forty participants  were randomly assigned to receive a single intravenous dose of NPC‐21 1, 3, 10, or 20 mg/kg or placebo. Six Japanese participants were included in each dose group and six White participants received a 10‐mg/kg dose. The placebo group included 8 Japanese participants and 2 White participants. All 40 participants completed the study. Serum concentration, maximum serum concentration, area under the plasma concentration–time curve from time 0 to the last measurable concentration, and area under the plasma concentration–time curve from time 0 to infinity increased dose dependently; dose proportionality was linear. NPC‐21 demonstrated a biphasic elimination pattern, with an estimated half‐life between 612 and 790 hours. NPC‐21 was safe and well tolerated up to 20 mg/kg. All adverse events were mild, and none led to treatment discontinuation or were considered related to the study drug. There were no differences in pharmacokinetics or safety between Japanese and White participants. These results support further investigation of NPC‐21. Human cytomegalovirus (hCMV) is a β‐herpesvirus (type 5). Infection with hCMV is common, with an estimated 50% to 90% incidence of infection globally.SOT recipients with hCMV infection have an increased risk of morbidity and mortality within 6 months of transplantation.In clinical practice, prophylactic or preemptive treatment is given to SOT and HSCT recipients.Outside of Japan, high‐titer CMV–immune globulin intravenous (IVCytoGam) is available and can be used alone or in combination with antiviral drugs. Because CMV–immune globulin IV is a blood product, there are concerns related to the transmission of blood‐borne viruses and lot‐to‐lot variations in quality. Therefore, development of a nonblood product with antibody activity would be beneficial for the control of hCMV infection in susceptible individuals.NPC‐21 (EV2038) is a fully human immunoglobin G1λ monoclonal antibody isolated from Epstein‐Barr virus–transformed peripheral B cells of a healthy donor. NPC‐21 targets antigenic domain 1 (AD‐1) of glycoprotein B (gB), which is the dominant antigen on the hCMV envelope.As a fully human antibody, NPC‐21 has the primary pharmacologic action of neutralizing exogenous antigens. In a 4‐week repeated‐dose toxicity study conducted in cynomolgus monkeys (once weekly intravenous [IV] administration), the dosage level at which no adverse effects were observed was 50 mg/kg .The purpose of this randomized, placebo‐controlled, first‐in‐human phase 1 study was to evaluate the pharmacokinetics, safety, and tolerability of a single IV administration of NPC‐21 in both Japanese and White healthy men participants in Japan.The study protocol was approved by the institutional review board of the study site . All participants provided written informed consent. The study was conducted in compliance with ethical principles that have their origins in the Declaration of Helsinki (1964) and its revised version; the standards specified in Paragraph 3, Article 14, and Article 80‐2 of the Pharmaceuticals and Medical Devices Act; relevant laws and regulations, including the Ordinance on Good Clinical Practice ; and the study protocol.2 for Japanese participants or 18.5 to <27.5 kg/m2 for White participants, who had not used other drugs or related products within 2 weeks or nicotine within 3 months before receiving the study treatment, did not have any significant disease or abnormal screening results, and did not have a history of serious drug allergy. All participants provided written informed consent.Study participants were healthy Japanese or White men aged 20 to <45 years, with a body mass index of 18.5 to <25.0 kg/mThis was a randomized, placebo‐controlled, double‐blind, dose‐ascending, phase 1 study to determine the pharmacokinetics, safety, and tolerability of a single IV injection of NPC‐21 in Japanese and White healthy adult male participants in Japan. A randomization schedule was created for each step using a random number generator function. The individual responsible for investigational product allocation created the randomization schedule. All operations related to randomization were conducted in a locked location accessible only to the individual responsible for investigational product allocation, their assistant, and unblinded dispensing staff.The study design is shown in NPC‐21  or placebo  was administered by IV infusion  in a volume of ≈300 mL. Participants were hospitalized from day −1 to day 4, and outpatient visits were conducted on days 8, 15, 22, 29, 43, 57, and 85. A randomized, placebo‐controlled, double‐blind study design was adopted to minimize bias.0‐t), AUC from time 0 to infinity (AUC0‐∞), total body clearance (ClB), maximum serum concentration (Cmax), half‐life (t1/2), time to reach maximum serum concentration (tmax), and volume distribution (Vd[area]).Pharmacokinetic outcomes included serum concentrations over time and pharmacokinetic parameters, including area under the plasma concentration–time curve from time 0 to the last measurable concentration  were collected from the antebrachial vein at each of the following sampling times: before study drug administration and at 0.5, 1, 2, 4, 8, 12, 24, 36, 48, and 72 hours, and days 8, 15, 22, 29, 43, 57, and 85 after administration. After separation, serum was stored at −70˚C until analysis. Separate fully validated electrochemiluminescence‐based assays were used to determine serum NPC‐21 concentration and to detect anti–NPC‐21 antibodies.Serum NPC‐21 concentrations were determined by sandwich enzyme‐linked immunosorbent assayAEs , laboratory tests , vital signs , resting 12‐lead electrocardiogram , and anti–NPC‐21 antibodies  were monitored to evaluate safety. AEs were coded according to System Organ Class and Preferred Terms using the Medical Dictionary for Regulatory Activities version 21.1.A bridging format with biotin‐labeled NPC‐21 (capture antibody) and ruthenium‐labeled NPC‐21 (detection antibody) was used to detect serum anti–NPC‐21 antibodies.Because this was an exploratory study, no formal statistical analyses were used to determine sample size. The number of participants needed to evaluate the tolerability, safety, and pharmacokinetics of NPC‐21 was estimated to be 6 for the NPC‐21 group and 2 for the placebo group at each dose level . The pharmacokinetic population consisted of all participants receiving NPC‐21 or placebo and having serum concentration obtained at 1 or more time points after the start of study treatment. The safety population consisted of all participants receiving NPC‐21 or placebo.max, tmax, AUC0‐t, AUC0‐∞, t1/2, ClB, and Vd[area]) were calculated from serum NPC‐21 concentrations using noncompartmental analysis. Dose linearity of pharmacokinetic parameters  was calculated by both linear regression analysis and a power model . For the linear regression analysis, the following equation was used: Y = α + βX, where X is the dose and Y is the pharmacokinetic parameter . The dose linearity was examined by calculating an estimate of α, CI, and the P value. For the log‐transformed linear regression analysis, the following equation was used: ln(α) + βln(X), where X is the dose and Y is the pharmacokinetic parameter . Dose linearity was examined by calculating an estimate of β, CI, and the P value.Summary statistics were calculated for continuous data, and frequency distribution was calculated for categorical data. Pharmacokinetic parameters  and SAS version 9.4 .Participant disposition is shown in The background characteristics of the study participants are shown in Table Changes in serum concentration of NPC‐21 over time are shown in Figure max was lowest in the 20 mg/kg group (2 hours) and greatest in the 3 mg/kg group (3.67 hours). The t1/2 was estimated to be between 612 and 790 hours and the elimination pattern was considered biphasic. In the comparison of Japanese and White participants, the mean AUC0‐t was 82 700 μg • h/mL vs 91 600 μg • h/mL, and the mean Cmax was 273 μg/mL vs 274 μg/mL, respectively. Cmax, AUC0‐t, and AUC0‐∞ increased in a dose‐dependent fashion; dose proportionality was demonstrated by a linear regression analysis and a power model analysis. Linear regression analysis showed an intercept (95%CI) for α of 3580 (−4617.99 to 11 786.22) for AUC0‐t .Summary statistics for each of the pharmacokinetic parameters by dose group are shown in Table t Figure , 5420  who were positive for anti–NPC‐21 antibodies had these antibodies before receiving the study drug; therefore, there were no cases of newly developed anti–NPC‐21 antibodies after study drug administration. None of the participants who had anti–NPC‐21 antibodies before study drug administration had increased antibodies after administration.0‐t, AUC0‐∞, and Cmax increased in a dose‐dependent manner, while all other pharmacokinetic parameters did not differ significantly by dose . NPC‐21 was generally safe and well tolerated at all doses, thus confirming the safety and tolerability of the study drug up to 20 mg/mL. All AEs were mild, and there were no serious or severe AEs, no AEs related to the study drug, and no AEs that led to study discontinuation. Finally, there were no differences in the pharmacokinetics or safety observed between Japanese and White participants who received a 10‐mg/kg dose of NPC‐21. The similar pharmacokinetic and safety findings between Japanese and White participants indicate that NPC‐21 clinical studies are appropriate for both Japanese and White patients.We report that, at all doses tested, AUC1/2 tends to be around 18 to 21 days.1/2 for NPC‐21 of 26 to 33 days (612‐790 hours).Overall, the pharmacokinetics of NPC‐21 are similar to those reported for other monoclonal antibody drugs. Linear pharmacokinetic behavior is typical for most immunoglobulin G1 antibody drugs and tInfusion‐related reactions are known to occur with a number of monoclonal antibody drugs. Rates are particularly high (>50%) for rituximab, avelumab, and daratumumab.There is potential for the development of antidrug antibodies against monoclonal antibody drugs. Antidrug antibodies can reduce the efficacy of monoclonal antibodies through neutralization and alter the pharmacokinetic and pharmacodynamic properties of the drug.GCV is used as first‐line prophylactic treatment for SOT recipients and as preemptive treatment for HSCT recipients. However, given the concerns with serious adverse drug reactions  and drug‐drug interactions, both of which may require dose adjustment for hCMV treatment, safer alternatives are needed. There are generally few concerns regarding drug‐drug interactions and dose adjustment for renal dysfunction with antibody drugs.This study had several limitations. First, only male participants were included, limiting the generalizability of the study findings. Second, only healthy individuals participated, meaning further studies will be needed to assess safety and pharmacokinetics in individuals with reduced organ function. Third, the age of study participants was limited to 20 to <45 years of age; therefore, it is unknown how patients in other age groups may react to NPC‐21. Fourth, although it is anticipated that NPC‐21 will be administered monthly for multiple cycles when used to treat patients, there was no repeated dosing. Finally, as with all phase 1 studies, the number of participants was low. The previously mentioned ongoing phase 2 study will address many of these limitations.0‐t, AUC0‐∞, and Cmax increased in a dose‐dependent manner, and the t1/2 of NPC‐21 was 612 to 790 hours.NPC‐21 was safe and well tolerated up to 20 mg/kg in healthy Japanese men and at the dose of 10 mg/kg in healthy White men. The pharmacokinetic parameters of AUCThe data that support the findings of this study are available from the corresponding author upon reasonable request.K.F. received grant/funding from Nobelpharma Co., Ltd. and CiCLE of the Japan Agency for Medical Research and Development. I.H., T.N., and T.W. are employees of Nobelpharma Co., Ltd. S.E. has received consultation fees from Nobelpharma Co., Ltd.Supporting informationFigure S1: Study design.Figure S2: Participant disposition.Click here for additional data file.


Eleocharis dulcis (Burm. f.) Trin. ex Hensch. Three isomers of hydroxycoumarin, namely, O‐feruloyl‐4‐hydroxycoumarin, O‐feruloyl‐3‐hydroxycoumarin, and O‐feruloyl‐2‐hydroxycoumarin, exhibited increased levels in BCWC, while p‐coumaric acid and vanillic acid did not show any significant differences in their content in BCWC and SCWC peels. This study, for the first time, provides novel insights into the differences among metabolite profiles between BCWC and SCWC.The Chinese water chestnut (CWC) is among the most widespread and economically important vegetables in Southern China. There are two different types of cultivars for this vegetable, namely, big CWC (BCWC) and small CWC (SCWC). These are used for different purposes based on their metabolic profiles. This study aimed to investigate the metabolite profile of CWC and compare the profiles of peels collected in different harvest years using ultraperformance liquid chromatography/mass spectrometry (UPLC–MS)‐based metabolomics analysis. Three hundred and twenty‐one metabolites were identified, of which 87 flavonoids, 25 phenylpropanoids, and 33 organic acids and derivatives were significantly different in the content of the two varieties of BCWC and SCWC. The metabolite profiles of the two different cultivars were distinguished using principle component analysis (PCA) and orthogonal projections to latent structures discriminant analysis, and the results indicated differences in the metabolite profile of  The big Chinese water chestnut (BCWC) and small Chinese water chestnut (SCWC) peels using ultraperformance liquid chromatography/mass spectrometry (UPLC/MS)‐based metabolomics analysis. A total of 321 significant metabolites, including 90 flavonoids, 25 phenylpropanoids, and 33 organic acids and derivatives, were identified/annotated from BCWC and SCWC. In total, flavonoids and phenylpropanoids were identified with different levels, and which higher levels were found in SCWC than in BCWC. The chemical compositions of the two cultivars of CWC were distinguished here, which is of great significance to the future utilization of CWC peels.22.1BCWC and SCWC were planted in the same field and under the same conditions at the same time, which is the typical size of the selected Chinese varieties , and identified by Prof. Yanghe Luo, Hezhou University . The peels of BCWC and SCWC were stored at −80°C until further analysis.2.2Freeze‐dried BCWC and SCWC peels were crushed using a mixer mill  with a zirconia bead for 1.5 min at 30 Hz. Hundred milligrams of the powder was weighed and extracted overnight at 4°C using 1.0 ml of 70% aqueous methanol. The samples were then centrifuged at 10000 × g for 10 min, and the extracts were absorbed  and filtered  for UPLC–MS analysis ; solvent system, mobile phase A: water (0.04% acetic acid); mobile phase B: acetonitrile (0.04% acetic acid), gradient program, 95:5 v/v at 0 min, 5:95 v/v at 11.0 min, 5:95 v/v at 12.0 min, 95:5 v/v at 12.1 min, 95: 5 v/v at 15.0 min; flow rate, 0.40 ml/min; temperature, 40°C; injection volume: 2 μl. The effluent was channeled into the ESI‐triple quadrupole‐linear ion trap (Q TRAP)‐MS.Samples were injected into the LC‐ESI‐MS/MS system . The analytical conditions were as follows: HPLC: column, Waters ACQUITY UPLC HSS T3 C2.4Linear ion trap (LIT) and triple quadrupole (QQQ) scans were performed using a QQQ TRAP API Q TRAP LC/MS/MS System, equipped with an ESI Turbo Ion‐Spray interface, operating in the positive ion mode and controlled by Analyst 1.6.3 software (AB Sciex). Operation parameters of the ESI source were as follows: ion source, turbo spray; source temperature 500°C; ion spray voltage (IS) 5500 V; ion source gas I (GSI), gas II (GSII), and curtain gas (CUR) were set at 55, 60, and 25.0 psi, respectively; the collision gas (CAD) was high. Instrument tuning and mass calibration were performed with 10 and 100 μmol/L polypropylene glycol solutions in QQQ and LIT modes, respectively. QQQ scans were acquired in the multiple reaction monitoring (MRM) mode with the collision gas (nitrogen) set to 5 psi. DP and CE for individual MRM transitions were determined and further optimized  strategy and the MS2T data were analyzed by comparing the accurate precursor ion Q1) and product ion (Q3) values, retention time (RT), and a self‐compiled database MWDB  and publicly available metabolite databases if the standards were unavailable : values for BCWC and SCWC peels were segregated in the PCA score plot of sesame metabolites. Furthermore, they were clearly divided into two classes on the heat map, indicating significant differences in levels of secondary metabolites in BCWC and SCWC peels.All 321 metabolomics were analyzed. To eliminate the effect of concentration on pattern recognition, the logarithm (log10) of the peak area matrix of BCWC and SCWC metabolites was determined, which was followed by Poisson normalization . Furthermore, high levels of chrysoeriol were observed in both BCWC and SCWC peels, and it exhibited O‐hexoside or O‐rutinoside modifications. Unlike rice, no C‐glycosides were detected in BCWC and SCWC peels  is often used to study the internal structure of multiple variables using a few principal components or to derive a few principal components from the original variable so that they can retain as much information about the original variable as possible. Moreover, these components/variables are not related to each other, and usually, a mathematical formula is used to represent the original indicators as a linear combination or a new comprehensive index. In the PCA plot Figure , PC1 and3.32) and strong goodness of fit  of the OPLS‐DA models were observed on comparing BCWC and SCWC , biosynthesis of secondary metabolites , flavone and flavonol biosynthesis , flavonoid biosynthesis  et al. Among them, the biosynthetic pathway of flavonoids and phenylpropanoid compounds has been relatively clear  Trin. ex Hensch.Marked differences were observed in the metabolites of SCWC and BCWC. In total, 321 differential metabolites were identified, comprising flavonoids, phenylpropanoids, organic acids, and derivatives. In particular, 15 alkaloids and 13 terpenes were identified, for the first time, in Eleocharis dulcis (Burm. f.) Trin. ex Hensch.CWC is among the most widespread and economically important vegetables, with a wide array of uses in the food and medicine industries. To the best of our knowledge, the difference in chemical profiles between different cultivars may inform different functions. Our work focused on the different flavonoids and phenylpropanoids contained in the two different CWC cultivars. In total, 87 flavonoids were identified with different levels in BCWC and SCWC. We also identified 25 phenylpropanoids, of which higher levels were found in SCWC than in BCWC. Feruloyl and dihydroxycoumarin were the predominant phenylpropanoids in SCWC. Our results help further the current understanding of metabolic mechanisms accounting for the differences in different cultivars of The National Key R&D Program of China, Grant/Award Number: 2018YFD0901003; Natural Science Foundation of Guangxi, China, Grant/Award Number: 2020GXNSFBA297083; The Project to Improve the Basic Research Ability of Young and Middle‐Aged Teachers in Guangxi, China, Grant/Award Number: 2021KY0704; School Level Project of Hezhou University, China, Grant/Award Number: 209200214.The authors declare no competing financial interest.This research does not include any human or creature testing.Supinfo S1Click here for additional data file.Figure S2Click here for additional data file.Supinfo S3Click here for additional data file.


The a priori risk for infections in patients with myelodysplastic syndromes (MDS) is unknown. This study examines prescription rates of anti‐infective agents in MDS patients before and after diagnosis, in both in‐ and outpatient settings, to provide information on infection management in clinical practice.We performed a population‐based study using the HemoBase registry, containing data of all MDS patients diagnosed since 2005 in Friesland, the Netherlands. Community and hospital pharmacies provided prescription data from 1995 to 2020.p < 0.01), respectively, corresponding to median 23.5 and 7.6 treatment days/year. Higher‐risk (449.9 DID) and lower‐risk patients (129.1 DID) both received significantly more anti‐infective agents after diagnosis; comorbidities, neutropenia, and age did not show significant differences relative to prescription rates. Before diagnosis, 10% of patients had infection‐related hospital admissions versus 38% after diagnosis.Data were obtained for 203 of 292 patients (70%). Patients received significantly more anti‐infective agents, predominantly antibacterials (70%), after diagnosis compared to before: 148.7 defined daily dose/1000 days (DID) (95% CI: 146.9–150.5) and 55.1 DID (95% CI: 54.5–55.8, In conclusion, MDS patients received significantly more anti‐infective agents compared to before diagnosis. This is the first study that has quantified the prescription rate of anti‐infective agents within and beyond the clinical setting in MDS. MDS is Current estimates of infection rates are based on results from clinical trials or retrospective analyses that only reviewed hospital records , 11, 12.In contrast to the inpatient setting, outpatient care generally involves empirical treatment, as bacterial cultures are rarely available. One method of studying infections in an outpatient setting is to investigate the prescription of anti‐infective agents. This approach is particularly useful in examining the burden of possible infections , 19. TheThis study investigates the prescription rate of anti‐infective agents in MDS patients (lower‐ and higher‐risk MDS) in an outpatient and inpatient setting; it assesses the prescription of anti‐infective agents over time before and after diagnosis of MDS and differences in prescription rates according to MDS risk groups, comorbidities, neutropenia, and age.2A retrospective, population‐based study was performed using the HemoBase registry , 20, 21.All persons newly diagnosed with MDS between January 1, 2005 and December 31, 2017 were included in the study and followed through December 31, 2020 also see  [2]. ComThe prescription rate of anti‐infective agents was presented as prescriptions/year, days of treatment (DOT)/year, defined daily dose (DDD) per 1000 inhabitant days (DID) for total prescriptions and outpatient prescriptions, and DDD per 100 hospital days for inpatient prescriptions according to the anatomical therapeutic chemical classification (ATC)/DDD index of the World Health Organization . An infePatients served as their own control; we compared the prescription rate of anti‐infective agents in patients with MDS to their prescription rate before diagnosis, with a maximum of 10 years prior to diagnosis. Complete case analysis was performed for DID calculations . In‐ and outpatient data were combined and calculated with the total outpatient follow‐up before and after diagnosis to calculate the total DID. Descriptive statistics were used to describe patient characteristics. Day‐to‐day use of anti‐infective agents in all patients before versus after MDS diagnosis was graphically depicted. In this analysis, each day of follow‐up was assessed for the prescription of anti‐infective agents. More than one prescription of an anti‐infective agent on the same day in an individual patient was counted as a single prescription. Univariate analyses were performed to assess differences in the delta of prescription rates for anti‐infective agents before diagnosis compared to after diagnosis in patients according to different baseline parameters: IPSS‐R, CCI score, absolute neutrophil count (ANC), and age. In these Kruskal–Wallis tests, there was no a priori ranking of groups. Patients with a follow‐up <6 months were excluded from this analysis. Statistical analyses were performed using IBM SPSS version 24 and SAS version 9.4.3A total of 292 MDS patients were identified, and their pharmacists and (dispensing) GPs were asked to provide information. We received the data on prescribed anti‐infective agents for 203 patients (70%), which comprised 3868 outpatient and 466 inpatient prescriptions for anti‐infective agents. For the remaining 89 patients (30%), data were present but not made available by the GP and/or pharmacist for our study. The patient characteristics are listed in Table p < 0.01).The percentage of patients who received anti‐infective agents was relatively stable over the follow‐up period before MDS diagnosis was established, increased at the time of diagnosis, and remained at a stable level through the end of follow‐up Figure . Patientp < 0.01). The DOT/year significantly increased from a median of 7.6 (95% CI: 5.3–10.1) DOT/year before diagnosis to 23.5 (95% CI: 15.9–30.7) after diagnosis (p < 0.01). Patients received 53.8 DID (95% CI: 53.2–54.5) before diagnosis and 135.9 DID (95% CI: 134.2–137.7) after diagnosis . Antibacterial agents for systemic use (ATC‐group J01) were predominantly prescribed  compared to before diagnosis , indicating no differences in anti‐infective treatment once admitted to hospital. Patients mostly received antibacterial agents for systemic use  had an infection‐related hospital admission during a median follow‐up of 10 years compared to 77 patients (38%) after diagnosis during a median follow‐up of 1.9 years. Twelve of these patients had an infection‐related hospital admission before and after diagnosis of MDS. Patients received 141.8 DDD/100 hospital days (95% CI: 132.6–151.6) before diagnosis compared to 151.6 DDD/100 hospital days (95% CI: 145.6–157.8) after diagnosis . Patients with a CCI score of 0–1 showed an increase of 1.1 weeks/year after diagnosis versus 2.7 weeks/year for those with a CCI score ≥2 (p = 0.12), and patients with an ANC ≤1 showed an increase of 2.8 weeks/year after diagnosis versus 1.2 weeks/year for those with an ANC > 1 (p = 0.12). Patients across the quartiles for age showed an increase of 1.4 weeks/year after diagnosis for Q1 (30–68 years), 3.3 for Q2 (68–75 years), 1.0 for Q3 (75–81 years), and 1.6 for Q4 (81–92 years) (p = 0.25). The DID, prescriptions/year, DOT/year, and hospital admissions for each subgroup are presented in Table Univariate analyses were performed to assess differences in the delta of prescription rates for anti‐infective agents before diagnosis compared to after diagnosis among patients with differing IPSS‐R risk groups, CCI scores, ANCs, and ages. There was a statistically significant difference among IPSS‐R scores: higher‐risk MDS patients showed an increase of 5.95 weeks of treatment/year after diagnosis compared to before diagnosis versus 1.45 and 1.20 weeks/year for lower‐risk MDS patients and patients with an unknown IPSS‐R score, respectively .Both the lower‐ and higher‐risk MDS groups showed a relatively stable percentage of patients that received anti‐infective agents before diagnosis, which increased at the time of diagnosis until the end of follow‐up for both groups Figure . Patient4This study showed a significantly increased prescription rate of anti‐infective agents in patients after diagnosis of MDS as compared to before diagnosis. To our knowledge, this is the first descriptive population‐based study that provides an overall insight on prescribed anti‐infective agents within and beyond the clinical setting in a well‐defined representative MDS patient cohort. MDS patients had a 2.5 times higher chance of receiving anti‐infective agents in an outpatient setting after diagnosis. Higher‐risk MDS patients were particularly at risk for receiving anti‐infective agents; however, lower‐risk patients also showed a significant increase, as the prescription rate doubled following diagnosis. Most prescriptions were antibacterial agents and prescribed for the treatment of an active infection.Our results showed that the percentage of patients who received anti‐infective agents was stable up to the time of diagnosis and increased at the time of diagnosis. This increase persisted through the end of follow‐up, indicating that anti‐infective agents were prescribed during the entire disease duration and did not decrease with time. This finding aligns with our expectations, as there is, besides HSCT, no cure for these patients. In addition, the prescriptions/year and DOT/year significantly increased by a factor of three since diagnosis, meaning patients had more than two prescriptions for anti‐infective agents per year and more than 3 weeks of treatment per year, a clinically significant increase compared to before diagnosis.These results support the findings of previous research and observations that MDS patients are at increased risk for infections after diagnosis , 7, 10. In particular, higher‐risk MDS patients showed an increased risk for receiving outpatient anti‐infective agents after diagnosis. This may be explained by the severity of the disease, as this group more often has severe neutropenia, impaired function of (regulatory) T cells, a higher transfusion burden, and more interventions compared to lower‐risk patients and are therefore prone to infections , 28, 29.This study found that patients received 55.1 DID before MDS diagnosis, which is higher than prescription rates presented in other studies , 33, 34.There are several possible explanations for the increased prescription rate of anti‐infective agents after diagnosis of MDS and the increased susceptibility to infections in this patient population. The first explanation is a functional impairment on a cellular level , 37, 38.Regarding the study's strengths and weaknesses, one notable strength of this study is the large amount of detailed information we collected in an unselected MDS population. We had the opportunity to compare prescription rates of anti‐infective agents before and after diagnosis of MDS based on comprehensive prescription data from Dutch pharmacies. The detailed, up‐to‐date pharmacy information systems provided a complete overview without potential recall bias. Moreover, Dutch policies on prescribing anti‐infective agents are strict and therefore may act as an accurate reflection of infection rates , 24. In A limitation of this study is the age difference in the pre‐post MDS diagnosis comparison. Due to this design, the estimates of prescriptions of anti‐infective agents after MDS diagnosis were based on an older population. To limit this effect, a maximum follow‐up was set to 10 years before diagnosis. Furthermore, age at the time of diagnosis was not associated with increased prescription rates. Because of this design, characteristics such as gender and medical history were equal before and after diagnosis. Therefore, we feel this study shows an accurate comparison of the real‐world prescription rate of anti‐infective agents before and after diagnosis of MDS. Another limitation was that not all information could be retrieved for all patients, as this was a retrospective study, and it is possible that some inpatient data might have been missed. In addition, it was not feasible to study the effect of factors like neutropenia or comorbidities over time. These parameters should ideally be collected prospectively to avoid selection bias and confounding by indication. These data were unavailable in our dataset and should be further investigated. Still, this study encompassed many detailed data on MDS patients and their prescription rates over time, and we feel it provides important information on the management of MDS in clinical practice.In conclusion, this study showed a significantly increased prescription rate of anti‐infective agents after diagnosis of MDS compared to before diagnosis. Higher‐risk patients were particularly at risk for receiving anti‐infective agents; however, lower‐risk patients also showed a significantly increased risk, as the prescription rate doubled after MDS diagnosis. To our knowledge, this is the first population‐based study that has quantified the prescription rate of anti‐infective agents within and beyond the clinical setting in a well‐defined representative MDS patient cohort. Further research on infectious complications in this patient group and the rational use of anti‐infective agents in a real‐world setting is warranted.The authors declare no conflict of interest.Johanne Rozema, Iris Potma, Eric N. van Roon, Mels Hoogendoorn, and Robby E. Kibbelaar designed the project. Johanne Rozema, Iris Potma, and Inge ten Seldam collected the data. Johanne Rozema wrote the manuscript. Johanne Rozema, Iris Potma, Inge ten Seldam, and Nic J.G.M. Veeger analyzed the data. Eric N. van Roon, Mels Hoogendoorn, Robby E. Kibbelaar, Arjan A. van de Loosdrecht, and Nic J.G.M. Veeger provided input in data analysis. All authors contributed to critical revision and gave final approval of the manuscript.Supplementary dataClick here for additional data file.