This enables study of the impact of ILEs with modulated composition, such as 0.3 lithium bis(fluoromethanesulfonyl)imide (LiFSI)‐0.7 N‐butyl‐N‐methylpyrrolidinium bis(fluoromethanesulfonyl)imide (Pyr14FSI) and 0.3 LiFSI‐0.35 Pyr14FSI‐0.35 N‐butyl‐N‐methylpyrrolidinium bis(trifluoromethanesulfonyl)imide (Pyr14TFSI), on the interfacial stability of PTNB@Li||PTNB@Li and PTNB@Li||LiNi0.8Co0.1Mn0.1O2 cells. The addition of Pyr14TFSI leads to better thermal and electrochemical stability. Furthermore, Pyr14TFSI facilitates the formation of a more stable Li|hybrid electrolyte interface, as verified by the absence of lithium “pitting corrosion islands” and fibrous dendrites, leading to a substantially extended lithium stripping‐plating cycling lifetime (>900 h). Even after 500 cycles (0.5C), PTNB@Li||LiNi0.8Co0.1Mn0.1O2 cells achieve an impressive capacity retention of 89.1 % and an average Coulombic efficiency of 98.6 %. These findings reveal a feasible strategy to enhance the interfacial stability between Li and LATP by selectively mixing different ionic liquids.To tackle the poor chemical/electrochemical stability of Li Incorporating a small amount of ionic liquid electrolyte (0.3LiFSI‐0.35Pyr14FSI‐0.35Pyr14TFSI) within a hybrid electrolyte that also contains Li1.3Al0.3Ti1.7(PO4)3 (LATP) and the polymer PVDF‐TrFE yields drastically enhances interfacial stability against lithium metal, prolonging the lithium stripping‐plating lifetime by a factor of 3.7 and giving superior cycling stability over 500 cycles (0.5C) in full cells with LiNi0.8Co0.1Mn0.1O2 (NCM811) as the cathode material. Among the options, the NASICON‐type Li14FSI ILE thin interlayers on the surface of lithium lanthanum zirconate (LLZO) resulted to substantially lowered interfacial impedances  during lithium stripping‐plating tests of symmetric Li||Li cells and charge‐discharge tests of Li||LiFePO4 cells. Although the introduction of the ILE interlayer may raise the overall battery cost, it certainly enables improved performance even avoiding the need of applying external compression to the cells. Furthermore, reduced price is expected when large scale production of ILE is realized for commercial batteries.Another remaining challenge of LATP lies in the poor interfacial contact with the electrodes resulting in high overall charge transfer impedances. The employment of solid‐liquid hybrid electrolytes can substantially reduce the electrode|electrolyte contact impedance resulting from the improved interface wetting.N‐oxycarbonyl)‐norbornene] (PTNB) interlayer impregnated with the 0.4LiFSI‐0.6Pyr14FSI ILE strongly enhances the cycle ability of the Li metal electrode against LATP‐based hybrid electrolyte. However, the impact of using multi‐anion ILEs, which have been found to have beneficial synergic effects, remains to be studied.Recently,14TFSI exhibits higher stability towards oxidation with respect to Pyr14FSI, further allowing for an increased tolerance with the lithium metal electrode by inhibiting the electrolyte degradation during cycling.It is known that Pyr14FSI (ILE) and the mixed anion 0.3LiFSI‐0.35Pyr14FSI‐0.35Pyr14TFSI (MILE). The resulting hybrid electrolytes are denoted as H‐ILE and H‐MILE, accordingly. As expected, the H‐MILE is found to provide a larger electrochemical stability window  benefiting from the presence of TFSI anions.+ deposition and stripping and thus mitigates the irreversible interfacial reactions between LATP and Li extending the lithium stripping‐plating cycling lifetime. Due to slightly reduced ionic conductivity and higher viscosity, the rate capacity is unfortunately sacrificed as revealed in cells comprising PTNB‐coated lithium (PTNB@Li), LiNi0.8Co0.1Mn0.1O2 (NCM811) and H‐MILE hybrid electrolyte, (PTNB@Li|H‐MILE|NCM811). However, the average Coulombic efficiency (CE) upon cycling at 0.5C is greatly ameliorated, achieving 99.3 % after 200 cycles and 98.6 % after 500 cycles. The remarkable cycling stability is associated to the stable interfacial impedance upon cycling. Taken together, the results reported herein demonstrate that enhancing the interfacial stability between Li and LATP by mixing different ILs is a feasible strategy. Such a method is expected to be also applicable to other solid‐state electrolytes suffering from poor stability against lithium metal.Thus, we hereby compare the performance of hybrid electrolytes employing different ILEs, in particular, the single anion 0.3LiFSI‐0.7Pyr−2, the anodic stability of H‐MILE is revealed to be as high as 5.459 V, which is 0.1 V higher than that of the H‐ILE. This is in a good agreement with previous reports showing that the TFSI anion is more stable toward oxidation than FSI anion.[14TFSI. Both H‐MILE and H‐ILE enable lithium plating with a rather low overvoltage. In fact, a current density flow of −15 μA cm−2 is attained at −0.013 V and −0.001 V, respectively. The better stability towards reduction is also in a good agreement with literature.[14FSI, Pyr14TFSI and LiFSI into a ternary ionic liquid electrolyte may lead to better electrode|electrolyte interfacial compatibility and a wider operating voltage range.Figure SI anion.23, 24Tstart) as the temperature at which 1 wt % of the sample weight is lost, the Tstart is revealed to be 175 °C and 189 °C for H‐ILE and H‐MILE, respectively. Above 335 °C, an additional slope is evidenced for H‐MILE, attributed to the decomposition of the TFSI anion. Besides of a higher Tstart, the decomposition of H‐MILE occurs at higher temperatures highlighting its improved thermal stability. The higher weight loss of H‐MILE compared to H‐ILE (ca. 1.5 wt %) is attributed to a higher density of MILE (1.442 g cm−3 vs. 1.409 g cm−3) resulting in its larger weight fraction in the hybrid electrolyte (given that the porosity of the LATP/PVDF‐TrFE film is the same for both hybrid electrolytes).The thermal stability of H‐ILE and H‐MILE Figure  is deternd cycle) to 20 mV (125th cycle). However, the cell failed due to internal short circuit right after, achieving a cycling lifetime of 250 h. The large initial overpotential is induced by the formation of a rather resistive interphase resulting from the native passivation film on the Li foil and the SEI spontaneously formed through the interaction between Li and ILE.nd cycle). After 290 h, the overpotential gradually reduces to 64 mV. The overall higher overpotential in the PTNB@Li|H‐MILE|PTNB@Li cell can be explained by the lower ionic conductivity of H‐MILE, 0.76 mS cm−1, with respect to H‐ILE, 1.48 mS cm−1 . However, no apparent overpotential growing is observed afterwards, achieving a highly stable cycling performance up to 924 h before soft short circuits were observed . Overall, the overpotential evolution shows a very similar trend to that seen in Figure Z′ values (collected at 69.3 Hz) upon cycling is shown in Figure To understand the reason for the enhanced cycling stability of the cells using H‐MILE, electrochemical impedance spectroscopy (EIS) measurements were performed on PTNB@Li|H‐ILE|PTNB@Li and PTNB@Li|H‐MILE|PTNB@Li cells upon cycling. Figure 1 and C_Li 2, respectively. Notably, at low magnification , residual LATP particles are observed on the surface of both cycled lithium electrodes. This could be due to partial breakdown of the native passivation layer, PTNB protection interlayer, and SEI formed upon cycling as a result of huge volume changes of lithium metal. The surface of C_Li 1 shows many “pitting corrosion islands” . Even under such circumstances, C_Li 2 exhibits substantially better‐preserved surface morphology than C_Li 1. Therefore, the incorporation of MILE favors the establishment of a stabilized interface between Li metal and the hybrid electrolyte, preventing the formation and growth of lithium dendrites.The ex situ surface morphology of cycled lithium electrodes was subsequently observed by scanning electron microscope (SEM) investigation Figure  are here Figure S, residua2N(−)−SO2−, Li3N, −SOx−, Li2S, and LiF, as indicated by the F1s, N1s and S2p XP spectra.[+ sputtering. Nevertheless, the better electrochemical performance of H‐MILE might be related with the formed fluorine‐rich interphases, for example, LiF and reduced species of FSI/TFSI anions such as ‐SO2N(−)−SO2−. In order to elucidate that the higher concentration of fluorine species is not due to the longer cycling time of PTNB@Li|H‐MILE|PTNB@Li cell, we performed the XPS measurements on the Li electrodes extracted from two PTNB@Li|H‐ILE|PTNB@Li and PTNB@Li|H‐MILE|PTNB@Li cells, which were cycled for the same time (150 h). PTNB@Li cycled with H‐MILE showed a higher LiF concentration than that cycled with H‐ILE . It is known that LiF is formed at the initial stages of electrolyte decomposition and subsequent bonding with Li surface.14+ decomposed more intensively in the H‐ILE, resulting in higher concentration of hydrocarbons and CN‐containing species. Therefore, the possible explanation for the highest LiF concentration is that the interphase of PTNB@Li tested in H‐MILE system is thinner. LiF is known as a good electrical insulator, effectively blocking the interfacial side reactions, meanwhile enabling homogeneous Li+ flux at the interface.In the next step, X‐ray photoelectron spectroscopy (XPS) was conducted to analyze the interphase formed on the surface of PTNB@Li in H‐ILE and H‐MILE  as the electrolyte, was investigated. Figure 811 and PTNB@Li|H‐MILE|NCM811 cells at various C‐rates. Initially, the PTNB@Li|H‐ILE|NCM811 cell delivers a slightly higher capacity than the PTNB@Li|H‐MILE|NCM811 cell (ca. 181 vs. 177 mAh g−1). Similar capacities are delivered at low current densities (0.1C and 0.2C) by both cells . However, a further increasing of C‐rates from 0.3C to 2C leads to more obvious differences of delivered capacities. Specifically, PTNB@Li|H‐ILE|NCM811 cell delivers capacities of 155 (0.5C), 146 (0.75C), 137 (1C) and 104 (2C) mAh g−1, higher than those obtained by PTNB@Li|H‐MILE|NCM811 cell (147 (0.5C), 131 (0.75 C), 116 (1C), and 66 (2C) mAh g−1). Particularly, the sudden capacity drop from 1C to 2C and, afterwards, the gradual capacity increase of the PTNB@Li|H‐MILE|NCM811 cell  since a fast ion transfer is vital for the performance at high (dis)charge rates.The above characterizations and analyses clearly demonstrate that H‐MILE exhibits higher compatibility with lithium metal. Thus, the electrochemical performance of full cells comprising PTNB@Li as the negative electrode, NCM811 and PTNB@Li|H‐ILE|NCM811 cells deliver an initial capacity of 147 and 151 mAh g−1 after the formation cycles. The discharge capacity gradually increases upon cycling and stabilizes afterwards in both cells, achieving capacities of 149 and 155 mAh g−1 after 200 cycles. Notably, the PTNB@Li|H‐MILE|NCM811 cell shows an apparent superiority over PTNB@Li|H‐ILE|NCM811 cell in terms of CE . The high CE of the PTNB@Li|H‐MILE|NCM811 cell is comparable to (or even superior to) that of other quasi‐solid‐state electrolytes.811 cell is related to the formation of small lithium dendrites generating soft short circuits, which lead to extra charge capacity whereas the discharge capacity remains rather stable. To verify this, ex situ SEM images of PTNB@Li recovered from PTNB@Li|H‐ILE|NCM811 and PTNB@Li|H‐MILE|NCM811 cells cycled for 200 cycles were taken. The former shows severe lithium corrosion pits . Additionally, long fibrous Li dendrites are seen , which explain the low CE . A few small Li dendrites are also observed . However, the Li dendrite formation is effectively mitigated in the PTNB@Li|H‐MILE|NCM811 cell, in good consistency with its higher CE. It again verifies that the employment of H‐MILE substantially promotes the cycling compatibility against PTNB@Li. Such a distinct behavior is highly consistent with the results demonstrated in lithium stripping‐plating test , the depressed semicircle has two major contributions.811|H‐MILE interfacial impedance. In the initial 60 cycles, the NCM811|H‐MILE interfacial impedance gradually decreases, primarily due to the slow electrode activation resulting from the viscous MILE. Afterwards, it stays constant for over 200 cycles, reflecting the high stability of NCM811|H‐MILE interface.At the charged state (4.3 V), the interfacial impedance of PTNB@Li|H‐MILE slightly increases after 80 cycles, which could be due to the thickening of the interphase on the Li surface. Additionally, the newly emerged depressed semicircle in the medium‐to‐low frequency range is ascribed to the NCM14FSI with the ternary 0.3LiFSI‐0.35Pyr14FSI‐0.35Pyr14TFSI, the detrimental interfacial side reactions are effectively reduced as evidenced by the elimination of the lithium “pitting corrosion islands”, the fibrous dendritic lithium, as well as the nearly constant interfacial resistance upon cycling. Thus, the stabilized interface contributes to the significantly improved cycling performance in both symmetric cells and full cells. Impressively, the lithium stripping‐plating lifetime is prolonged by a factor of 3.7. The evaluation of PTNB@Li|H‐MILE|NCM811 full cells demonstrates a stable cycling for 500 cycles at 0.5C, retaining 89.1 % of initial capacity and achieving an average CE of 98.9 %. Therefore, the strategy of selectively mixing different ionic liquids may provide new insights for the establishment of stable Li|solid electrolyte interface for high‐performance quasi‐solid‐state lithium metal batteries.The impact of incorporating different ILEs into hybrid electrolytes on the oxidation/reduction stability, thermal stability, and most importantly, the interfacial stability against lithium metal has been investigated. By replacing the binary 0.3LiFSI‐0.7PyrThe preparation of all electrodes and electrolytes, the assembly of cells and the handling of ex situ samples were conducted in a dry room with a dew point always below −60 °C at 20 °C.14FSI) or MILE (0.3LiFSI‐0.35Pyr14FSI‐0.35Pyr14TFSI) was added into the above‐mentioned LATP/PVDF‐TrFE film (1.5 cm×2 cm) with the aid of ambient vacuum. The excess ILE was squeezed to avoid any free‐flowing liquid electrolyte. The as‐prepared hybrid electrodes are labeled as H‐ILE or H‐MILE, accordingly.The preparation of dry LATP/poly(vinylidene fluoride‐trifluorethylene (PVDF‐TrFE) and LATP/PVDF‐TrFE/ILE films follows the same method already described in the previous studies.19, 32811 electrodes were prepared according to the previous study.811 slurries were prepared by intimately mixing NCM811, Super C65 and PVDF in N‐methyl‐2‐pyrrolidone (NMP) with a weight ratio of 92 : 4 : 4. A doctor blade technique was adopted to cast the slurries on aluminum foils. Next, the as‐prepared wet electrodes sheets were pre‐dried in an oven (60 °C) to remove NMP and then fully dried under vacuum . Prior to cell assembly, the porosity of NCM811 electrodes was filled with 5 μL of ILE (or MILE) with the aid of a vacuum step (15 min under a pressure of 10−3 mbar at 20 °C). The electrodes were wiped carefully afterwards to eliminate any free‐flowing liquid. The average NCM811 mass loading density was calculated to be ∼2.5±0.1 mg cm−2. All the specific capacity values are calculated based on the mass of NCM811.The PTNB@Li and NCM811 (or PTNB@Li|H‐MILE|NCM811), were assembled in two‐electrode pouch cells comprising NCM811 as the positive electrode, PTNB@Li as the negative electrode and H‐ILE (or H‐MILE) as the electrolyte.The ESW of both H‐ILE and H‐MILE was determined in two‐electrode Swagelok cells using a polymer‐coated lithium metal electrode (PTNB@Li) and an ion‐blocking stainless steel (SS) electrode . The ionic conductivity of H‐ILE and H‐MILE was measured in two‐electrode pouch cells by sandwiching a layer of H‐ILE or H‐MILE between two copper foil electrodes. Lithium stripping‐plating tests were conducted in two‐electrode pouch cells composed of two PTNB@Li electrodes separated by a layer of H‐ILE or H‐MILE as the electrolyte. Accordingly, the cells are named as PTNB@Li|H‐ILE|PTNB@Li or PTNB@Li|H‐MILE|PTNB@Li. Full cells, for example, PTNB@Li|H‐ILE|NCM−1 in an artificial air atmosphere. The gas flow rate ratio of N2 and O2 was fixed at 60 : 40.The density of ILE and MILE was measured by a density meter (Anton Paar DMA 5000M). The thermal stability of H‐ILE and H‐MILE hybrid electrolytes was examined by TGA . The samples were heated up to 600 °C at a heating rate of 3 °C min−2 and the time for each cycle is 2 h. For the long‐term cycling test, cells were always activated by one cycle at 0.05C and three cycles at 0.1C before subjecting to 200 cycles (PTNB@Li|H‐ILE|NCM811) or 500 cycles (PTNB@Li|H‐MILE|NCM811) at 0.5C. The rate capability was evaluated by cycling fresh cells at different (dis)charge rates ranging from 0.05C to 2C. The current density at the 1C rate corresponds to 200 mA g−1. The voltage range for full cell test is 3.0–4.3 V. The EIS  upon lithium stripping‐plating in symmetric cells and galvanostatic cycling in full cells were recorded using a multi‐channel potentiostat (VMP Biologic‐Science Instruments). The cell operation temperature was always fixed at 20 °C, controlled by a climatic chamber (Binder GmbH).All the electrochemical performance tests were performed using a battery tester (Maccor series 4000). The current density for lithium stripping‐plating test is fixed at 0.1 mA cm811 (or PTNB@Li|H‐MILE|NCM811) cells, the ILE or MILE was first removed by rinsing with dimethyl carbonate (DMC) solvent, and subsequently applying a vacuum drying step at 20 °C to remove residual DMC. For ex situ SEM/XPS measurements, samples were transferred using a home‐designed air‐tight transfer box to avoid any contact with moist air.To prepare ex situ samples, for example, cycled PTNB@Li recovered from symmetric cells and PTNB@Li|H‐ILE|NCMKα  X‐ray source and a Phoibos 150 XPS spectrometer (SPECS‐Surface concept) equipped with a micro‐channel plate and Delay Line Detector (DLD). The scans were acquired in a Fixed Analyzer Transmission mode with an X‐ray source power of 200 W (15 kV), 30 eV pass energy and 0.1 eV energy steps. The depth profiling was performed by a 5 keV Ar+ focused ion gun with an ion filter and sputtering rate of 0.8 nm min−1. The CasaXPS software was used for the spectra fitting, using a nonlinear Shirley‐type background and 70 % Gaussian and 30 % Lorentzian profile function.The morphology of cycled lithium foils recovered from symmetric and full cells was investigated by SEM (ZEISS EVO MA 10 microscope). The interphase on lithium metal was also examined by XPS analysis using a monochromatic AlThe authors declare no conflict of interest.1As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.Supporting InformationClick here for additional data file.


Fluid administration and resuscitation of patients with sepsis admitted through emergency departments (ED) remains a challenge, and evidence is sparse especially in sepsis patients without shock. We aimed to investigate emergency medicine physicians' and nurses' perceptions, self‐reported decision‐making and daily behavior, and challenges in fluid administration of ED sepsis patients.We developed and conducted a multicenter, web‐based, cross‐sectional survey focusing on fluid administration to ED patients with sepsis sent to all nurses and physicians from the five EDs in the Central Denmark Region. The survey consisted of three sections: (1) baseline information; (2) perceptions of fluid administration and daily practice; and (3) clinical scenarios about fluid administration. The survey was performed from February to June, 2021.In total, 138 of 246 physicians (56%) and 382 of 595 nurses (64%) responded to the survey. Of total, 94% of physicians and 97% of nurses regarded fluid as an important part of sepsis treatment. Of total, 80% of physicians and 61% of nurses faced challenges regarding fluid administration in the ED, and decisions were usually based on clinical judgment. The most common challenge was the lack of guidelines for fluid administration. Of total, 96% agreed that they would like to learn more about fluid administration, and 53% requested research in fluid administration of patients with sepsis. For a normotensive patient with sepsis, 46% of physicians and 44% of nurses administered 1000 ml fluid in the first hour. Of total, 95% of physicians and 89% of nurses preferred to administer ≥1000 ml within an hour if the patients' blood pressure was 95/60 at admission. There was marked variability in responses. Blood pressure was the most commonly used trigger for fluid administration. Respondents preferred to administer less fluid if the patient in the scenario had known renal impairment or heart failure. Normal saline was the preferred fluid.Fluid administration is regarded as an important but challenging aspect of sepsis management. Responses to scenarios revealed variability in fluid volumes. Blood pressure was the most used trigger. ED nurses and physicians request evidence‐based guidelines to improve fluid administration. Fluid resuscitation of sepsis patients is complex, involving integration of both perceived patient needs and alternatives for fluid therapy. This study assessed practitioner choices through responses to hypothetical scenarios. Variation among responses from practitioners was observed, as would be expected.1Sepsis is one of the most common diagnoses in emergency department (ED) patients, and sepsis remains a major cause of morbidity and mortality.In the context of the low‐quality evidence to guide fluid administration and to better understand practice variation, we conducted a survey about fluid administration in patients with sepsis in the ED. The survey had two objectives: (1) to describe ED physicians' and nurses' perceptions of fluids, and how they regard their knowledge and confidence in handling fluid administration to patients with sepsis, and (2) to describe their daily decision‐making and practice regarding the quantity, infusion rate, and type of fluid administered in sepsis patients with varying comorbidities as well as the use of triggers to guide in fluid administration.22.1We conducted a multicenter, web‐based, cross‐sectional survey. The survey was developed according to the COSMIN guidelines.2.2The survey consisted of three sections: (1) baseline information regarding the participants; (2) perceptions of fluid administration and daily practice; and (3) scenarios about fluid administration, with a total of 32 questions. There were four hypothetical scenarios of a patient with sepsis and suspected pneumonia arriving in the ED: with normal blood pressure (BP: 120/75), with lower BP (BP: 95/60), with chronic renal failure, and with known heart failure. The survey instrument included five‐point Likert‐scale options for answers ranging from “strongly disagree” to “strongly agree” and questions with the possibility to choose multiple answers. Attitude‐based questions included a “Don't know” option. The survey was in Danish. A translated version is provided in Data 2.3We identified all ED physicians and registered nurses employed in the five EDs in the Central Denmark Region in February 2021 from local mailing lists. The five EDs provide 24‐h care to a mixed rural–urban population of 1.3 million people. In the five EDs, patient contacts vary between 15.000 and 63.000 per year.The survey was structured and distributed through work emails using REDCap. Respondents activated the personal weblink and completed the survey instrument online. The survey was distributed from February to June 2021. To increase responses, non‐respondents received up to four email reminders.Participation was voluntary, and consent for participation was implied by completion of the survey. The survey was anonymous. The study conduct was approved by the department managements of the participating departments. No ethical approval was required. The survey is reported in accordance with the Checklist of Reporting of Survey Studies (CROSS) Data .182.4All data are presented as numbers and proportions for categorical variables. Missing responses were not imputed. The five‐point Likert scale responses were combined into three groups: “Agreed” (“Agreed” and “Strongly agreed”), “Neither/Or,” and “Disagreed” (“Disagreed” and “Strongly disagreed”) for purposes of data presentation in the manuscript but is shown in its full in tables. The data for all respondents were also described according to whether respondents were physicians or nurses. No sample size calculation was conducted a priori since the intent of the survey was to be descriptive and all physicians and nurses were contacted. We performed all analyses using Stata version 17 (StataCorp LP).33.1The survey was distributed to 841 individuals employed in the EDs in Central Denmark Region: 246 physicians and 595 nurses. In total, 138 physicians (56%) and 382 nurses (64%) for a total of 520 (62%) responded to the survey. Characteristics of respondents are shown in Tables 3.287% of physicians and 82% of nurses agreed that they regarded fluid as medication with potential side effects. Of total, 94% of physicians and 97% of nurses indicated that fluid is an important part of the treatment of sepsis. The majority agreed that they felt confident in managing fluid treatment of patients with sepsis  while fewer felt confident in managing fluid treatment of patients in septic shock  , temperature (63%), patient history (39%), and arterial/venous blood gas analyses including lactate measurements (35%) Figures . The mos3.4In a clinical scenario describing an otherwise healthy 55‐year‐old woman (70 kg) with sepsis due to suspected pneumonia, the most common choice (physicians: 46% and nurses: 44%) was to administer 1000 ml intravenous fluids within 1 h. Of total, 37% of physicians and 38% of nurses would prefer to administer 500 ml in 1 h. When the same patient was admitted with lower blood pressure (95/60 mmHg), the preferred fluid volumes in general increased, and 95% of physicians and 89% of nurses preferred to administer ≥1000 ml within an hour of admission to this patient , overall fluid administration decreased. The respondent proportion preferring to give no fluids increased , as well as the respondent proportion who did not know  Table . This waMost respondents would administer normal saline to the patients  or “Initially NaCl followed by Ringers” . The decisions on fluid administration in the scenarios were primarily based on a clinical judgment  , which may reflect insecurity toward the sickest patients. However, the finding is in contrast to a similar survey from Denmark investigating fluid management in the prehospital setting.Blood pressure was the most frequently used trigger for fluid initiation and further fluid administration, in accordance with self‐perceived practice in the prehospital setting.Respondents were prone to administer less fluid to a hypothetical patient with known renal impairment compared to one without comorbidity. We did not further investigate the reasons for this decision. In a survey investigating de‐resuscitation practice, they found respondents to be less likely to administer diuretics to a patient in acute renal failure.For patients with known heart failure, respondents also administered less fluids. Accordingly, a survey found that respondents would administer less fluid in patients in septic shock with heart failure with reduced ejection fraction <40%, compared to those without heart failure, before deeming the patient volume non‐responsive.Normal saline was the preferred fluid of choice. This is in accordance with the findings in other surveys in emergency medicine,Ongoing studies investigating fluid administration strategies in patients with sepsis‐associated hypotension and septic shock in EDs and ICUs are awaited.4.1This survey has not been validated as a measurement instrument; however, the survey was conducted in accordance with guidelines from COSMIN and the purpose was to reflect the daily clinical practice of handling fluids in Danish EDs. The generalizability to other countries in general is uncertain. Emergency medicine is a new specialty in Denmark. As such, administering for example vasopressors for now still requires assistance from anesthesiologists and usually transfer to an intensive care unit. This may have caused a tendency to administer more fluids instead of considering early vasopressors. The thoughts on especially triggers are probably universal. The main limitation of this survey is the low response rate, which raises concerns that the sample does not represent the entire ED staff population. The survey was distributed during the COVID‐19 pandemic, which could have affected the response rate. It is possible that respondents are those with more interest in the topic and potentially more polarized views. The questions on fluid administration were asked using hypothetical clinical scenarios in early sepsis, reflecting self‐reported clinician behaviors, which may not reflect true practice management. Also, the scenarios only described the early treatment of sepsis not covering later stages of sepsis during hospital admission.In conclusion, fluid administration is regarded an important but difficult aspect of sepsis treatment. Nurses and physicians primarily base their decisions on clinical judgment, and they request evidence‐based guidelines to improve fluid management. Responses to scenarios revealed variability in fluid volumes, with no consensus on appropriate indications, volumes, or rates, although blood pressure was the most frequently used trigger. These findings support further investigations within the field to provide specific recommendations in guidelines.Marie Kristine Jessen, Birgitte Y. Simonsen, and Marie‐Louise H. Thomsen developed and tested the survey. Lars W. Andersen, Jens A. K. Petersen, and Hans Kirkegaard tested the survey and provided expert knowledge in the conduction process. Marie Kristine Jessen drafted the manuscript. All authors approved the submission of the manuscript.The authors declare no conflict of interest.Data S1 Supporting InformationClick here for additional data file.


Allowing selected patients with few distant metastases to undergo potentially curative local ablation, the designation “oligometastatic” has become a widely popular concept in oncology. However, accumulating evidence suggests that many of these patients harbour an unrecognised microscopic disease, leading either to the continuous development of new metastases or to an overt polymetastatic state and questioning thus an indiscriminate use of potentially harmful local ablation. In this paper, reviewing data on oligometastatic disease, we advocate the importance of identifying a true oligometastatic disease, characterised by a slow speed of development, instead of relying solely on a low number of lesions as the term “oligometastatic” implies. This is particularly relevant in clinical practice, where terminology has been shown to influence decision making. To define a true oligometastatic disease in the context of its still elusive biology and interaction with the immune system, we propose using clinical criteria. As discussed further in the paper, these criteria can be classified into three categories involving a low probability of occult metastases, low tumour growth rate and low tumour burden. Such cases with slow tumour-cell shedding and slow proliferation leave a sufficiently broad window-of-opportunity to detect and treat accessible lesions, increasing thus the odds of a cure. In 1995, Hellman and Weichselbaum summarised available evidence on local ablation of distant metastases, deducing the concept of oligometastases as an intermediate state between a localised tumour and a widespread metastatic disease . EmerginP = 0.11), no difference was observed in the rates of polymetastatic progression (55% versus 61%) . Th. Th26]. P = 0.006) [Imaging plays a decisive role in assessing growth kinetics, based on a chronological series of examinations, tumour burden, defined by size (volume) and number of lesions and number of organ sites, and location of lesions within a given organ site. All three parameters are inherently connected and determine the technical feasibility and safety of local treatment. Notably, tumour doubling time varies both on a case-by-case basis and in the same patient. According to volumetric analyses, it ranges from less than one week to more than 1 year, albeit usually being in the order of several months . At an i= 0.006) . Hence, Laboratory methods comprise both traditional tumour marker tests which have been validated in some malignancies, such as prostate-specific antigen (PSA) in prostate cancer, and presently still investigational liquid biopsies based on detection of different elements such as cell-free circulating tumour DNA, circulating tumour cells, microRNA or exosomes in body fluids, typically in the peripheral blood . AccordiOur hypothetical model has several limitations some of which have already been addressed, especially a lack of prospective validation. Besides that, a restricted insight into tumour biology prevents us from integrating the multifaceted effects of heterogeneous behaviour of the primary tumour and its different metastases in terms of cancer cell shedding and proliferation and those of an outstanding phenomenon known as a dormant state allowing cancer cells to preserve their tumour-generating capacity and reawaken several years later . AccumulIn conclusion, when employing local treatments in patients with few metastases, tumour dynamics seems to be the major denominator of therapeutic success. Cancers with slow tumour-cell shedding and slow proliferation leave a sufficiently broad window-of-opportunity to detect and treat accessible lesions. In case sporadic micronodules later develop in overt metastases, the indolent behaviour of such tardily appearing “argometastases” gives us another fair opportunity to eradicate them. A scientific terminology is a mighty tool that may eventually steer our decision making, not only in daily practice but also when dealing with a rare and sometimes over-diagnosed entity as a true oligometastatic disease probably is.


Analysis of ascitic fluid can offer useful information in developing andsupporting a differential diagnosis. As one of the most prevalent complicationsin patients with cirrhosis, ascitic fluid aids in differentiating a benigncondition from malignancy. Both the gross appearance of the ascitic fluid, alongwith fluid analysis, play a major role in diagnosis. Here, we discuss a patientwith liver cirrhosis, esophageal varices, hepatitis C, and alcohol abuse, whohad a paracentesis performed, which revealed a turbid, viscous, orange-coloredascitic fluid that has not been documented in literature. Ascitic fluid isroutinely analyzed based on gross appearance, cell count, and serum ascitesalbumin gradient (SAAG) score. An appearance of turbidity or cloudiness hascommonly suggested an inflammatory process. In our case, fluid analysis revealeda red blood cell count of 24 250/mcL, further suggesting inflammation. However,it also revealed an insignificant number of inflammatory cells, with a totalnucleated cell count of 14/mcL. This rich-orange color has posed a challenge inclassification and diagnosis of the underlying cause of ascites, with oneclassification system suggesting inflammation, while another suggesting portalhypertension. Furthermore, we have traditionally relied on the SAAG score to aidin determining portal hypertension as an underlying cause of ascites. With a96.7% accuracy rate, the SAAG score incorrectly diagnosed portal hypertension inthis patient. In this article, we aim to explore how this rare, orange-coloredascitic fluid has challenged the traditional classification system ofascites. Performing abdominalparacentesis for ascitic fluid analysis is an essential factor in determining theunderlying cause of ascites.2 Various properties are used to evaluate ascitic fluid suchas gross appearance, cell count, and differential, serum ascites albumin gradient(SAAG), enzymes, macromolecules, culture and gram stain, and tumormarkers.3 Both laboratory data and clinical findings are used inconjunction to establish a diagnosis to provide treatment and disease prognosis.Traditionally, once all these parameters are analyzed, the underlying cause ofascites usually becomes apparent, helping direct medical therapy.Abdominal ascites is a pathologic collection of peritoneal fluid seen in a variety ofdiseases including hepatic disease, heart failure, kidney disease, malignancy,infection, and more.A 56-year-old Hispanic woman with a history of alcoholic cirrhosis, grade 3esophageal varices status-post banding in 2017, and untreated hepatitis C, presentedwith abdominal distension and fatigue for 1 month. She presented multiple times tothe emergency room for abdominal discomfort due to ascites, requiring paracentesis.In addition, she was noted to have anemia on these occasions and required bloodtransfusions. She never had been diagnosed with spontaneous bacterial peritonitis(SBP), nor did she have any recent gastrointestinal (GI) bleeding. She presentedwith abdominal pain secondary to ascitic fluid accumulation. On physicalexamination, the abdomen was distended with mild tenderness to palpation diffusely.Shifting dullness, caput medusa, and spider angiomata were noted. Her skin wasjaundiced with associated scleral icterus. She had bilateral lower extremity pittingedema up to the knees. Laboratory investigations at that time revealed: hemoglobinof 6.0g/dL, platelets of 115 000/mcl, serum albumin 1.8 g/dL, direct bilirubin of1.8 mg/dL, total bilirubin of 4.8 mg/dL, and total protein of 8.3g/dL. Coagulationstudies showed prothrombin time (PT) of 20.6 seconds, partial thromboplastin time(PTT) of 2.1 seconds, and an international normalized ratio (INR) of 1.81. Vitamin Dlevel was 125 ng/mL and vitamin B12 level was 2451 pg/mL. Chest X-ray andelectrocardiogram were unremarkable. On computed tomography scan of the pelvis, amoderate amount of ascites was associated with stigmata of cirrhosis, and portalvenous hypertension was manifested by severe splenomegaly.3. Serum ascites albumin gradient scorewas calculated as 1.1 with 96.7% accuracy of correctly predicting that the asciteswas due to portal hypertension. Her Child-Pugh score was 12, categorizing her asChild Class C with a life expectancy of 1 to 3 years with abdominal surgery andperioperative mortality of 82%. Her Model for End-Stage Liver Disease (MELD) scorewas 23 points, with a 19.6% estimated 3-month mortality.Body fluid culture with gramstain was negative. Hepatocellular carcinoma was ruled out, as evidenced by hernormal alpha-fetoprotein (AFP) level and lack of imaging findings. She wassubsequently started on intravenous pantoprazole, octreotide, furosemide,spironolactone, and lactulose therapy.An abdominal paracentesis was performed and revealed a unique bright orange-colored,turbid, and viscous ascites  with totGiven the above analyses, ascites cytology opens a discussion about the possibilityof a non-inflammatory, hemorrhagic process underlying her disease. The clinicalpicture of this patient and high protein count pointed us toward an inflammatorypathology initially; however, this does not appear to be the case, as evidenced byher cellular profile showing minimal PMNs. The significantly elevated RBC count doesnot appear to be from an acute process, but more likely a long-term process. Thiscan be supported by the increased total protein concentration, which will often beelevated in a long-term process. On one hand, we have an SAAG score that pointed ustoward the diagnosis of portal hypertension. On the other hand, we have asignificantly elevated protein count that cannot be explained by portal hypertensionalone, and therefore, must be attributed to another underlying process such as aspontaneous hemorrhagic, noninflammatory, yet exudative pathology.4 This can occur due to numerous pathologies including, but notlimited to, portal hypertension, hypoalbuminemia, and peritoneal disease. We willfocus our discussion on the development of portal hypertension in the setting ofcirrhosis, given that our patient presented with alcoholic cirrhosis. In thesepatients, portal hypertension is a prerequisite for the development of cirrhoticascites, serving as a landmark in the natural history of cirrhosis with a poorprognosis of 50% mortality within 3 years.1 In other words, ascites doesnot occur in cirrhotic patients without portal hypertension. The mechanism of portalhypertension continues to be studied. It begins with arterial splanchnicvasodilation, which can be attributed to increased nitric oxide production. Anothercontributing factor is the degree of structural changes within the hepatocytes dueto fibrosis, nodule formation, and vascular occlusion (depending on the degree ofcirrhosis).5 In turn, the body undergoes changes in hemodynamics to maintainblood flow by the renin-angiotensin-aldosterone system (RAAS) activation,anti-diuretic hormone (ADH) secretion, sympathetic nervous system regulation, andincreased production of endogenous vasoconstrictors. Vasoconstrictors contribute tothe dynamic changes seen in the liver by activating the contraction of hepaticstellate cells and myofibroblasts. Renal sodium excretion becomes impaired, leadingto a positive sodium balance and water retention. Hepatic vascular resistance thenincreases due to expansion of extracellular fluid, causing hydrostatic forces withinhepatic sinusoids, typically greater than 12 mm Hg. These hydrostatic forces causefluid to transude into the peritoneal space, beginning the cycle of portalhypertension. As portal hypertension worsens, there is a continued increase in localrelease of splanchnic vasodilators leading to systemic hypotension, plasma volumeexpansion, and increased cardiac output.6Ascites is defined as the disruption in intravascular and extravascular fluid spaces,resulting in pathologic accumulation of >25 mL of fluid in the peritonealcavity.7 Pathogenic growth of gutbacteria in patients with cirrhosis occurs due to slowed mucosal blood flow, therebypromoting bacterial overgrowth and translocation. An ascites PMN count≥250/mm3 indicates peritonitis that may be due to SBP or secondarycauses. Risk factors include upper GI bleed and low ascitic proteinconcentration.2One of the most common complications of portal hypertension is spontaneous bacterialperitonitis. Although the pathophysiology is still being investigated, it isproposed that SBP occurs due to interaction between changes in gut microbiome,intestinal permeability, and immune dysfunction.3 Gross appearance of ascites fluid can be useful in providingpreliminary signs related to the cause of an underlying disease. Clear orstraw-colored ascitic fluid is often associated with uncomplicated ascites in thesetting of cirrhosis. Turbid or cloudy ascites is associated with infected fluid asseen in bacterial infection or peritonitis (SBP). Milky or chylous ascites isdefined as a fluid rich in triglycerides and proteins, which is often seen inmalignancy, tuberculosis, or parasitic disease. Hemorrhagic or bloody ascites, whichis defined as ascitic fluid RBC count >10 000/μL, may indicate a “traumatic tap”during paracentesis, or malignancy such as hepatocellular carcinoma.Ascites is routinely analyzed via fluid samples based on appearance of the fluid,total protein concentration, SAAG score, cell count, and total proteinconcentration.8 This causes us to move on toinvestigating the cell count and differential, which notably has the greatestdiagnostic power for ascitic fluid.We begin our classification of ascites in this patient by examining the grossappearance of fluid extracted by abdominal paracentesis. Our patient presented withan orange-colored, turbid ascites, featuring an unfamiliar appearance that is notdocumented in literature. The color and fluid properties of the ascites is sounique, it does not fit into the traditional classification system that hascategorized ascites by its gross appearance. Fluid analysis revealed an RBC count of24 250/mcL, which suggests an inflammatory process is at play here. However,appearance as a predictor of underlying disease is challenged by the cell count anddifferential in the ascitic fluid. Furthermore, ascitic fluid RBC count, in additionto physical exam findings and bloody ascites strongly suggest hemorrhagic ascites.Hepatocellular carcinoma would be the strongest differential diagnosis given herfindings; however, even this suspicion was minimized due to an AFP level of 6.5ng/mL and negative imaging studies. AFP levels have a specificity of 80% indetecting hepatocellular carcinoma.3 Total proteinconcentration has since been widely replaced with SAAG but can still be valuable indifferentiating pathologies of high-protein ascites and low-protein ascites. Ourteam originally believed the diagnosis was portal hypertension, which wascontradicted by the high protein count we found in our fluid. The typical finding incirrhosis, and what we had expected to find, is a low-protein ascitic fluid.High-protein ascites, as in our patient, is usually explained by other processessuch as congestive heart failure, constrictive pericarditis, peritonealcarcinomatosis, tuberculosis (TB), and Budd-Chiari syndrome, all of which wereexcluded upon further evaluation.3 At this point, we seek to findanother source for the elevated total protein count found in our ascitic fluid,which we can now definitively state was not a product of portal hypertension butlikely some other underlying pathophysiology that has not revealed itself.We next consider the white blood cell (WBC) and PMN leukocyte count to exclude SBPand investigate other potential causes of ascitic fluid. Based on the unique cellprofile found in our orange-colored ascites of low WBC, high protein, and high RBC,we are faced with a challenge in further classifying this fluid. The total proteinconcentration was formerly used to classify ascitic fluid as exudative ortransudative, with a diagnostic criterion of >2.5g/dL to be exudative.3 Prior to the SAAG score, ascitic fluid was classified intotransudate versus exudate based on ascitic fluid total protein (AFTP). The AFTPclassified ascites correctly only 55.6% of the time, and thus was replaced with theSAAG score due to greater diagnostic accuracy.9 A SAAG score >1.1 g/dLindicates that a patient has portal hypertension, most notably with an accuracy of96.7%.1 In our patient, serum albumin level is 1.8 mg/dL and the ascitesfluid albumin level is 0.7 mg/dL resulting in an SAAG score of 1.1, which isnondiagnostic for portal hypertension as the cause of ascites. Depending on thesource, the SAAG score parameters can have differing cutoff values. Based on theAnnals of Internal Medicine, an SAAG score of ≥1.1 is indicative of portalhypertension, whereas other sources designate >1.1 or <1.1 as their diagnosticparameters.9 The inconsistent definitions lend to the variability ofapplication of the SAAG score.To further classify the ascitic fluid, the SAAG score was calculated. The SAAG scoreis a diagnostic calculation tool used in the evaluation of ascites. It is based onthe difference between the ascitic fluid albumin level and serum albumin level,reflecting the degree of portal hypertension, as well as prognosis in a patient withcirrhosis.10In contrast to the inconclusive SAAG score results, computed tomography of the pelvisand abdomen  in this 11 Up to 50% of decompensated cirrhotic patients develop ascitesas an associated complication.12 Approximately 3% of thepopulation with ascites fluid in 2015 would estimate up to 9499 patients who areunable to be evaluated by the SAAG score. Our case is an example of this fact, whereusing the SAAG score suggested causes of ascites fluid that contradicted ourpatient’s laboratory findings. Thus, in clinical settings that do not conform withtraditional diagnostic tools, physicians cannot rely on these measures to help guidediagnoses. Our team suggests that there needs to be more research into quick scorecalculations used to classify ascitic fluid.Physicians have traditionally used the SAAG score to help differentiate betweenabdominal ascites being due to portal hypertension versus inflammatory processeswith 96.7% accuracy. Our case highlights the misdiagnosed 3% of patients withascites. The prevalence of liver cirrhosis in the United States was 633323 adults in2015.


We propose a framework for jointly modelling tumour size at diagnosis and time todistant metastatic spread, from diagnosis, based on latent dynamic sub-models ofgrowth of the primary tumour and of distant metastatic detection. The frameworkalso includes a sub-model for screening sensitivity as a function of latenttumour size. Our approach connects post-diagnosis events to the natural historyof cancer and, once refined, may prove useful for evaluating new interventions,such as personalised screening regimes. We evaluate our model-fitting procedureusing Monte Carlo simulation, showing that the estimation algorithm can retrievethe correct model parameters, that key patterns in the data can be captured bythe model even with misspecification of some structural assumptions, and that,still, with enough data it should be possible to detect strongmisspecifications. Furthermore, we fit our model to observational data from anextension of a case-control study of post-menopausal breast cancer in Sweden,providing model-based estimates of the probability of being free from detecteddistant metastasis as a function of tumour size, mode of detection (of theprimary tumour), and screening history. For women with screen-detected cancerand two previous negative screens, the probabilities of being free from detecteddistant metastases 5 years after detection and removal of the primary tumour are0.97, 0.89 and 0.59 for tumours of diameter 5, 15 and 35 mm, respectively. Wealso study the probability of having latent/dormant metastases at detection ofthe primary tumour, estimating that 33% of patients in our study had suchmetastases. The most basic multi-state Markov model, used in this context, is athree-states model  and another example is the five-statesmodel that sub-divides asymptomatic and symptomatic states by lymph nodes involvement. Markov models can be easily extended, but model complexity grows quickly.These models have other limitations too, which have been discussed elsewhere. Biologically-inspired (continuous) tumour growth models are being usedincreasingly often as an alternative to multi-state Markov models, due to theirflexibility and parsimony.310These models combine a growthmodel of breast cancer with a continuous function of screening sensitivity(depending on e.g. tumour size or mammographic density) and accommodate randomeffects to account for individual variations in tumour growth. From fitting suchmodels to observational data it has been shown that Markov models may assume toolittle individual variation in cancer progression. These types of continuous growth models have so far been developed only tomodel data collected up until the time of diagnosis. In this paper, we explore anapproach that models events occurring after diagnosis but incorporates the tumour’snatural history. Here, we focus specifically on distant metastatic spread: wedescribe a framework for modelling tumour characteristics at diagnosis and time todistant metastasis (counted from diagnosis), which is based on specifying a dynamicprocess of metastatic spread from the onset of the primary tumour. The framework wedescribe could potentially be extended to other events (such as tumour relapse ordeath) and can be useful, e.g., for studying personalised screening and treatmenteffects.Data collected through cancer screening trials or via population-based studiescarried out in the presence of screening programmes enable the estimation ofunderlying cancer natural history processes. The natural history of breast cancerhas traditionally been modelled using either discrete multi-state (semi-) Markovmodels or continuous tumour growth models, based on using data on tumourcharacteristics that are collected at diagnosis. Such models have been used tostudy, e.g., screening effectiveness and over-diagnosis.12Arvelo et al. divide the pathogenesis of metastasis into 10 sequential steps. In the earlysteps, tumour cells detach from the primary tumour and travel through the vascularsystem; experimental data show that this first stage is very efficient.12 For cancercells to extravasate to a distant organ and to establish metastases they need tothen evade multiple mechanisms which are designed to prevent the formation ofmetastases. Few cells (compared to the large number of cells that shed from theprimary tumour) manage to successfully form a metastasis.11 From observational data, itis not possible to distinguish these two stages; in the current paper, we modelsuccessful metastases directly. Finally, in the third stage, newly formed metastasesgrow until they become clinically detectable.The (metastatic) process of cancer cells disseminating from the primary tumour to adistant organ is complex.16; for somecancers  local tumours can affect vital organs.In breast cancer, all death will, in essence, be attributable to distant metastases.Patients with breast cancer that initially present with distant metastases arediagnosed with Stage IV disease, which is nearly always incurable. It has also beenestimated that among women without detectable metastases at the time of diagnosis(and surgery), and initially diagnosed with early-stage disease, around 30% willultimately develop metastases months or even years later. Some of the most common sites of distant metastatic involvement are lung,bone, liver and pleura. Refined surgical techniques and primary local and systemic therapies are allaimed at preventing metastatic spread and growth. However, metastatic cells become selected, experience high mutation rates andbecome resistant to treatments. The growth of these (therapy-resistant) metastasescolonies is responsible for the vast majority of cancer-related deaths. Hosseini et al. have estimated, based on molecular studies, that at least 80% of breastcancer metastases are derived from early disseminated cells (well before diagnosisof the primary tumour) and this had been offered as a reason why early detection andtreatment often fail to prevent cancer deaths. A better understanding of themetastatic process is fundamental to understanding approaches aimed at reducingcancer mortality.Over the years, several publications have reported that around 90% of cancer deathscan be attributed to metastatic spread of the primary tumourThis paper is organised as follows. In Section 2, we first describe the modellingframework for tumour growth (upon which we build); we then extend it to incorporatethe distant metastatic spread process. A model in the simple setting of no screeningis described in Section 2.3, which is then extended to incorporate data collected inthe presence of screening (Section 2.4). We show via Monte Carlo simulation that ourestimation procedure retrieves the correct model parameters (Section 3). In Section4, we illustrate our methodology using data from a Swedish population-basedcase-control study of postmenopausal breast cancer. In Section 5, we assess therobustness of the model (in the absence of screening) to violation of some of thestructural assumptions. Finally, we conclude the manuscript with a discussion(Section 6).The model developed in this paper is aimed at being used on breast cancer incidentcases, collected in a population of women in which screening is offered. Beforedealing with screening, we start by introducing the model in the context of noscreening.The growth of the primary tumour is assumed to follow an exponential functionsuch that, for a tumour growing according to an inverse growth rate Individual variation in growth rates is accounted for by assuming that inversegrowth rates follow a Gamma distribution with shape 810 we assume that, in theabsence of screening, the rate of symptomatic detection at time We assume that tumours are detectable with non-zero probability from a givenvolume  for more details.For an unscreened population, the density for tumour volume at symptomaticdetection can, from the above assumptions, be calculated asfollows: We will first formulate a model for successful distant metastaticseeding; then, we will formulate a model for time to detectability of eachmetastasis given detection of the primary tumour. A successful seeding can alsobe thought of here as a colony that will not be eradicated by treatment atdiagnosis. The time to detectability of distant metastases will be related tothe growth rate of the primary tumour.The model for distant metastatic spread is inspired by previous models onlocalised spread to the lymph nodes.The model for successful distant metastatic seeding assumes that themetastatic process is proportional to the average number of mutations in thecancer cells and the rate of cancer cell division. We use this model form asit has previously been shown to fit well to observed data on the lymph nodesspread. We note however that our methodology can be easily adapted to othermodels of spread, e.g. one which specifies the rate of spread to beproportional to the current volume of the primary tumour; it can be seenhowever that such a model would provide a poorer fit to empirical data (thanthe one we use), as we will later discuss. the exponent genomic instability.Under the model considered here, assuming a constant rate of mutation duringcell division, the average number of mutations is proportional to the numberof times the tumour cells have divided. Specifically, metastatic seeding isassumed to follow an inhomogeneous Poisson process with intensityfunctionThe number of times that cancer cells have divided can be calculated from thefollowing relation:ction ish(t|R=r)A model for distant metastatic spread needs to be stated in terms ofobservable quantities (seedings are not observable), such as the presence ofdistant metastases at diagnosis and the times of distant metastases fromdiagnosis of the primary tumour. We therefore define a random variable The time from metastatic seeding to detection of a givenmetastasis takes the individual-specific time We assume that breast cancers are only diagnosed through theprimary tumour, and not by the clinical manifestation ofmetastases.We make an exception to the above for metastases growing to adetectable size before the diagnosis of the primary tumour:these metastases will be visible at diagnosis of the primarytumour, but not detected beforehand.For an individual with a tumour that reaches size Equations  to 15)  15) are We now introduce additional assumptions: we assume that the metastaticseeding completely stops at diagnosis of the primary tumour and that alreadyseeded, successful colonies are not affected. These assumptions constrainthe density and hazard functions –  and 15)15) – to  Three types of observations contribute to the likelihood function:We describe the joint likelihood of tumour size and time to first distantmetastasis, for a sample of incident cases from a stable disease population.Observed events, i.e. metastases that are diagnosed after diagnosisof the primary tumour , can be written asoptim in R.Standard errors for the model parameters can be obtained using, e.g. theobserved information matrix at the optimum, or the bootstrap. Using Monte Carlosimulation, we show, in the supplementary material available online, that we canmaximise the likelihood function of this model and retrieve the correct modelparameters when simulating from a cohort in the absence of screening. We notethat the simulation was carried out treating tumour size as a continuousvariable; for reasons described later, when we implement our procedure for datacollected in the presence of screening (in Section 2.4), we discretise tumoursize.To calculate the likelihood we need to approximate the integrals appearing in thethree quantities described above (which do not have a closed-form); to do thiswe use numerical integration. Once the full likelihood function is formulated,it can be maximised using any general-purpose optimiser such asstable disease assumptions: specifically, that (1)the rate of births in the population, (2) the distribution of age at tumouronset, and (3) the distribution of time to symptomatic detection are constantacross calendar years. Stable disease assumptions are discussed in more detailin Isheden and Humphreys.We note again that this likelihood function is valid for samples of incidentcases under So far, we have described how to estimate our model for samples of cases that aredetected symptomatically in the absence of screening: we now extend our approachto allow the analysis of data from incident cases collected in the presence ofscreening. In Sweden, where our study is based (Section 4), mammographyscreening was introduced in 1974, and nationwide coverage was achieved in 1997.Nowadays, all women in Sweden between the ages of 40 and 74 years old are calledto screening every 2 years .First, we specify a model for screening sensitivity. We assume that theprobability of a positive screening test, given the presence of a tumour in thebreast, follows a logistic function:The contribution to the likelihood function is formulated as a conditional jointprobability: the joint probability of tumour size and the distant metastaticoutcome, given mode of detection (screen-detected versus symptomatic) and thehistory of previous (negative) screens. We rely on stable disease assumptions toformulate the likelihood, and we use a procedure (and notation) similar to thatdescribed in Isheden and Humphreys that was developed for modelling tumour sizewith data collected in the presence of screening.SD) tumourscan be written asscreening history, i.e. The likelihood contribution for screen-detected   w<0) cgrals in  to (21) SYM) cases with a history of negativescreens, the likelihood can be written asscreening historyFor symptomatic ,,21), witThe likelihood functions described in Sections 2.4.2 and 2.4.3 containintegrals that have no closed form. We approximate them numerically using aprocedure similar to that described in Section 2.3 in the absence ofscreening. Once the full likelihood function is formulated, it can, again,be maximised using any general-purpose optimiser with standard errorscalculated using, e.g. the observed information matrix at the optimum or thebootstrap.The vast majority of breast cancer patients do not have a detected distantmetastasis at diagnosis: for these, we can estimate conditional survivalprobabilities using the model described in Section 2.4. These model-basedpredictions still rely on assumptions that were previously outlined in Table 1;the calculations implicitly assume removal of the primary tumour at the time ofdiagnosis, which fully stops the process of metastatic seeding.For example, for symptomatically-detected tumours, survival probabilitypredictions at a given time fined in . More prequation . To evalequation ) and theThe denominator is calculated as the probability of having no detected metastasesat detection of the primary tumour, which is independent of We designed a simulation study aimed at assessing the ability to retrieve thetrue data-generating parameters of the model for data collected in the presenceof screening, i.e. the model described in Section 2.4. In other words, we aim totest that our implementation can correctly estimate parameter values when themodel is correctly specified.We simulated independent datasets, each with 1500 individuals. We assumed thattumours originate from a single spherical cell of volume 810 Next, wesimulated the volumes of the tumours at symptomatic detection based on equation would introduce minimal biases inestimates of growth rate distributions, see Isheden et al. Tumours were detected via screening with probabilities corresponding toequation  to each independently simulateddata set. Standard errors of the model parameters were computed by inverting theHessian matrix at the optimum, which we calculated using Richardsonextrapolation as implemented in the R packageWe considered the following performance measures:Bias, for quantifying whether the estimation procedure can retrievethe true data-generating parameters, on average. We also reportaverage and median estimated values of each estimand.Empirical and model-based standard errors, to quantify the precisionof the point estimates.Coverage probability, quantifying the probability that a confidenceinterval contains the true value of each estimand.Further details on each performance measure are described elsewhere; for completeness, we also report their Monte Carlo standard errors whichquantify the uncertainty in their estimation.To estimate the key performance measure (bias) with a given precision, we firstran 10 replications of the simulation study. Allowing a Monte Carlo standarderror for bias of 0.01, we would require rsimsum package.28The likelihood functionwas optimised using the optim function in R and thelimited-memory modification of the Broyden-Fletcher-Goldfarb-Shanno (BFGS)quasi-Newton method.This simulation study was coded and run using R version 4.0.2 and analysed usingthe There were no serious convergence issues with the estimation algorithm.Convergence was achieved for 293 (97.67%) of the 300 repetitions. This showed that poor coverage can be attributed to bias, asbias-eliminated coverage was otherwise good  was linked to data from the Swedish Cancer Registry andthe Stockholm-Gotland Breast Cancer Registry. The data on distant metastases hasbeen used before to study the association of mammographic density and the risk ofdistant spread. Mammographic images and screening histories were collected from mammographyscreening units and radiology departments, in an extension of the originalcase-control study; the collection of this data has also been describedpreviously.33 The data analysed here is from 1614 breast cancer patients, ofwhom 1035 (64.13%) were detected via screening; the remaining 579 (35.87%) womendetected their tumour symptomatically. Among all participants, the median tumoursize at detection (as diameter) was 15 mm .We used our modelling approach to analyse data from a case-control study ofpostmenopausal breast cancer in Sweden . was 5.49 years (IQI: 5.41–5.58 years). As shown in Four (0.25%) women had metastases at diagnosis; none of these women had small tumours. From the women that were free of detected metastases atdetection of the primary tumour, 293 (18.20%) were diagnosed with metastases duringfollow-up .Median ). We also fitted the model assuming different integer values of We fitted our model (Section 2.4), first fixing 810 we focus hereon novel quantities that we can obtain by modelling future events (distantmetastases).Since examples of how continuous growth models can be used to estimate aspects ofscreening sensitivity and growth of the primary tumour (in CAHRES data) have beenpresented elsewhere,n = 161), 12.50–17.50  mm(n = 326) and 32.50–37.50  mm (n = 40), respectively, andplotted their Kaplan-Meier curve estimates (The (hazard) rate of detection of first distant metastasis is defined in equationas a fun16.399) . In Figu16.399) , over tin = 159, or symptomatically with a negative screen inthe last 2 years, n = 75).Finally, based on our model, we estimated (1) the probability of a patient havingdormant/latent metastases at diagnosis and (2) the probability of detecting distantmetastases in a patient at diagnosis of the primary tumour, both as a function oftumour size at detection . Once agIn this section, we discuss the robustness of the proposed modelling framework toviolations of some of the underlying, structural modelling assumptions; we assessthis using Monte Carlo simulation. For studying robustness to misspecifiedassumptions it will not matter whether we focus on a screened or unscreenedpopulation. Therefore, for simplicity, we based our simulations on theimplementation of our model in the absence of screening (Sections 2.2 and 2.3).the primary tumour and seeded metastases grow at the same inverse growthrate Growth rates follow a Gamma distribution (equation );All individuals share the same spread parameter equation ). RecallWe relax (i) by simulating correlated growth rates from a bivariate Gaussiancopula with equally distributed Gamma margins and a correlation parameter The assumptions that we assess (in turn) are the following: We assessed the performance of the model when its assumptions are violated bycomparing model-based predictions with empirical probabilities estimated on a largesimulated dataset. We did this in terms of clinically relevant, model-basedpredictions; we assessed median tumour doubling times, probabilities of detectedmetastases and latent metastases at diagnosis of the primary tumours, fitteddistributions of tumour volume at symptomatic detection, and conditional survivalprobabilities (for being free from detected distant metastases), conditional onbeing free from metastases and on having a particular tumour volume at diagnosis ofthe primary tumour. Here we present results only for the latter; results for theother prediction types are presented in Supplemental Material Appendix D.Predicted conditional survival probabilities , under the six simulation scenarios and for threedistinct values of tumour size at detection, are included in 310As far as we are aware, themodel we introduce in this paper, that includes a component for events (distantmetastases) that occur after the diagnosis of the primary tumour, is the first ofits kind, although a similar model, but without random effects  has been proposed by Szczurek et al. Several models for events occurring after diagnosis have, of course, beendeveloped, e.g., for survival after breast cancer surgery,38 breast cancerrelapse,3943 metastasis after breastconservation treatment, susceptibility to breast cancer, major adverse cardiovascular events in breast cancer patients.However, these models condition on patients’ characteristics that are observedat diagnosis, i.e., do not incorporate the natural history of the disease. The modelintroduced here incorporates the unobserved natural history of the tumour. Itrelates the underlying biology to future outcomes, whilst at the same time providinginsights into the latent growth and spread processes of the tumour. According to arecent review on prognostic models for breast cancer survival and recurrence, themost common model inputs are nodal status, tumour size, tumour grade, age atdiagnosis, and oestrogen receptor status. In our model, we directly model tumour size and include it as a solecovariate in the screening sensitivity function, but it would be straightforward toaccommodate other covariates in this and other parts of the model.Continuous natural history growth models with random effects provide a usefulalternative to multi-state (semi-) Markov models for studying the unobserved historyof breast cancer. Models that have been introduced include those for tumour growth,time to symptomatic detection, screening sensitivity and local spread to the lymphnodes.). Blumenson and Bross derived a model for the probability of a distant metastasis that is notremoved by surgical treatment at diagnosis; they further extended their model toaccommodate two distinct disease subgroups, with distinct model parameters. Slack etal. introduced another two-disease model describing the course of breast cancer andimplications for clinical practice arising from the two-disease hypothesis. Jorcyk et al. described an animal model and a mathematical model for breastcancer progression, with the latter being solved numerically and showing goodagreement with laboratory data. Models developed by Koscielny et al. and by Szczurek et al. are perhaps most relevant to our work. Koscielny et al. estimated that metastatic bottleneck.As mentioned above, a major limitation of Szczurek et al.’s model is that it assumesa common rate of tumour growth (and therefore spread rate). In our work we assumethat it can vary between individuals; previous estimates of tumour growth ratevariation have shown substantial heterogeneity between subjects.There exist several mathematical models for breast cancer distant metastasis,including models for the initiation of metastatic growth, and models to assess theefficacy of surgery and adjuvant therapy on metastatic spread  that most metastases are seeded early. There are of course even other assumptions implicit in our approach, that arenot mentioned above. In reality, seeding of cancer metastases may in some cases be complex. For example, implicitly we assume that metastases do not cross-seed newmetastases; cross-seeding may contribute to faster, or increase heterogeneity in,metastatic growth.The model we describe in this paper has, of course, its limitations. It relies on aset of assumptions . In future work, we plan to incorporate both components to fit with theconventional model of breast cancer progression.The conventional, sequential model of breast cancer progression states that cancerpasses through several stages disseminating first to the lymph nodes and then todistant organs. However, genomic analyses suggest that there is minimal seeding fromaxillary lymph nodes.Even in its current form, our model adapts to biological principles of breast cancergrowth and spread providing building blocks for future extensions that couldaccommodate more complexity and processes within a single, unified and efficientframework for modelling latent tumour growth and future outcomes. Models and, moreimportantly, frameworks of this kind are fundamentally important for studying novelinterventions  and treatments, e.g., usingmicrosimulation, or for studying risk factors behind tumour growth and spread.Click here for additional data file.Supplemental material, sj-pdf-2-smm-10.1177_09622802211072496 for Estimatinglatent, dynamic processes of breast cancer tumour growth and distant metastaticspread from mammography screening data by Alessandro Gasparini and KeithHumphreys in Statistical Methods in Medical ResearchClick here for additional data file.Supplemental material, sj-pdf-3-smm-10.1177_09622802211072496 for Estimatinglatent, dynamic processes of breast cancer tumour growth and distant metastaticspread from mammography screening data by Alessandro Gasparini and KeithHumphreys in Statistical Methods in Medical Research


Using analytical results for viscous dissipation in phononic crystals, we calculate the decay coefficient of a sound wave propagating at low frequencies through a two-dimensional phononic crystal with a viscous fluid background. It is demonstrated that the effective acoustic viscosity of the phononic crystal may exceed by two to four orders of magnitude the natural hydrodynamic viscosity of the background fluid. Moreover, the decay coefficient exhibits dependence on the direction of propagation; that is, a homogenized phononic crystal behaves like an anisotropic viscous fluid. Strong dependence on the filling fraction of solid scatterers offers the possibility of tuning the dissipative decay length of sound, which is an important characteristic of any acoustic device.This article is part of the theme issue ‘Wave generation and transmission in multi-scale complex media and structured metamaterials (part 2)’. In a homogeneous fluid of density  η and ξ . For fre γ0=ω24η/+ξ/(2ρc3)In the case of a periodic distribution of scatterers, dissipation becomes a dominant factor that defines the efficiency of any acoustic device –14. In pIn some cases, viscous losses are desirable in devices that reduce external noise. Modern sound absorbers use innovative designs based on metamaterials. Artificial acoustic metamaterials can be used as structures to increase sound absorption to extents not achieved in natural materials ,19. In pIn the presence of solid scatterers, the energy of a sound wave propagating in a solid–fluid structure is dissipated mainly within the narrow boundary layer uid; see .A sound wave with wavelength see e.g. ,23. If tylinders . This quEquation  for the In this article, we explore the analytical theory of homogenization developed in  for two-nd holes ,18,20,21. 2Derivation of the analytical formula for the decay coefficient us fluid ,2.1ΔE=1It is assumed in equation  that souΔE/(a0E) :2.3γph(lated in  and is e. 3Using the equations –2.7), w, w2.7), In figures The effective speed of sound in equation  can be rd k^ik^k . The decIt is known that in a crystal lattice possessing a third- or higher-order rotational axis of symmetry, a second-rank symmetric tensor is reduced to a scalar. The structures depicted in figures Inset (ii) shows the region of small filling fractions. For all the structures considered in this work, the decay coefficient grows as equation . In figuFor less symmetric structures, the effective parameters Finally, in The effects of enhanced acoustic dissipation in narrow slit cavities have been recently studied in ,14. For An alternative way to analyse the anisotropic behaviour is depicted in . 4In conclusion, we have presented a possible way of tuning the decay coefficient of sound in a phononic crystal with a viscous background. A periodic set of solid rods in a viscous fluid homogenizes in the low-frequency limit and behaves for sound waves like an anisotropic viscous fluid. The level of anisotropy of the decay coefficient can be tuned to the desired value by selecting appropriately the crystal lattice, cross-section of the rods and filling fraction of the rods. We predict strong enhancement of sound decay along the directions where sound propagates through narrow slits formed by neighbouring scatterers.


This study revealed a novel mechanism through which aconite exerts antitumor effects, not merely through apoptosis induction pathways, providing more sound evidence that aconite has the potential to be developed into an effective anti-HCC agent.Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and, in its advanced stages, has a 5-year survival rate of only 3% to 5%. Despite novel mechanisms and treatments being uncovered over the past few years, effective strategies for HCC are currently limited. Previous studies have proven that aconite can suppress tumor growth and progression and prevent the recurrence and metastasis of multiple cancers, but the underlying molecular mechanisms are largely unknown. In this study, different doses of aconite were applied to mice bearing subcutaneous HCC tumors. It was found that aconite had a therapeutic effect on H22 tumor-bearing mice in a dose-dependent manner by reducing tumor volumes and prolonging survival times, which could be attributed to the immunoregulatory effect of aconite. Furthermore, results showed that high-dose administration of aconite could enhance adaptive immunity and natural killer (NK) cell-mediated immunity by regulating the secretion of interferon- Curr surgery . NeverthSeveral immunotherapy schemes have been promoted, including tumor vaccines, immune modulators, adoptive immune cell transfers, and immune checkpoint inhibitors. High rates of drug resistance and severe immune-related adverse events remain the major obstacles to the development of such immune-targeting therapies. The efficacy of immunotherapy in liver cancer is relatively low compared with other cancers due to the peculiar immunotolerance characteristics of the liver . ServingAconitum carmichaeli Debx. (aconite) has been shown to have clinical efficacy in treating advanced HCC [In TCM theory, the immunodeficient condition of patients with advanced HCC can be recognized as “Yang-deficiency and Yin-excessiveness,” which can be characterized by symptoms including drowsiness, fatigue, edema, chilliness in the abdomen and limbs, and diarrhea. One of the principles of TCM treatment is “Yang-warming.” As a typical herb used in Yang-warming methods, nced HCC . Studiesnced HCC , 9. Thesnced HCC , 11. NevThis study was designed to evaluate the effect of aconite on the immune status of mice with subcutaneous HCC tumors. Our results showed that aconite had a therapeutic effect on H22 tumor-bearing mice that could be attributed to its immunoregulatory effects, involving adaptive and NK cell-mediated immunity. This study revealed a novel mechanism through which aconite exerts an antitumor effect, not merely through apoptosis induction pathways, providing evidence that aconite has the potential to be developed into an effective anti-HCC agent.All animal studies were conducted in accordance with protocols from the Animal Care and Use Committee of the Naval Medical University. Also, all possible steps were taken to avoid animal suffering at each stage of the experiment.Aconite herbs were purchased from the Pharmacy of TCM, Changhai Hospital, affiliated with the Naval Medical University, Shanghai, China. The original solution of aconite decoction was prepared as follows: (1) 100 g of crude aconite was immersed and boiled in 1000 mL of diluted water for 2 h and then filtered; (2) this was repeated again; and (3) the two decoctions were mixed and concentrated to 100 mL. Aliquots of the original solution (1 g/mL) were stored at 4°C for further experiments.7 cells/mL) were intraperitoneally injected into BALB/c mice. After 5 days, the ascitic fluid was drawn from the abdominal cavity and intraperitoneally reinjected into another healthy BALB/c mice. When the mice exhibited abdominal bulging, 5 mL of ascites (flaxen-colored) were collected and diluted with saline solution to a concentration of 1 × 107 cells/mL suspension.In total, 50 4-week-old male BALB/c mice were purchased from SLAC Co., Ltd. . The mice were housed in a specific pathogen-free animal laboratory of Changhai Hospital, with a 12 h light/dark cycle lighting condition, 15×/h air ventilation, and 21 ± 1°C controlled temperature. During the experimental period, all mice had free access to standard fodder and distilled water, which were provided by SLAC Co., Ltd. and the animal laboratory. H22 cells  were cultured for three passages and confirmed to be uncontaminated by a mycoplasma test. Then, 1 mL of H22 cells . This was followed by a 3-week treatment, starting the day after H22 inoculation, with saline solution or aconite in different concentrations ). The mice were then divided into five groups (10 mice/group): blank , subcutaneous tumor-bearing model mice , low dose , medium dose , and high dose . During the administration period, the general condition and body weight dynamics of the mice were recorded daily. Mice were sacrificed after 3 consecutive weeks of aconite administration, and the survival times of mice in each group were documented. After all the mice were sacrificed, the tumors in each group were excised and weighed. The body mass changes of mice in each group were calculated as weight at sacrifice/natural death with tumor)−(weight of mice before modeling).d) and maximum vertical diameter (vd) of the tumors. The following formula was used to calculate the tumor size: volume = 1/2 × d × vd2. Three weeks after inoculation, the mice were sacrificed by cervical dislocation, and the tumors were removed, cleaned with distilled water, dried with absorbent paper, and weighed. The average tumor weights in the model (M) and experimental (E) groups were calculated; tumor inhibition rates (IRs) in each group were calculated as IR (%) = (M−E)/M × 100%.Tumor volumes were measured every 2 days for 3 weeks by recording the longest diameter  and eosin  staining at room temperature.Tumor tissues were fixed in 10% formalin for 24 h and embedded in paraffin. The tissues were then cut into 3 μm; Thermo Fisher Scientific). The mobile phase flow rate was 1 mL/min, and the column temperature was maintained at 45°C.Liquid chromatography mass spectrometry was used to analyze the main components of aconite. To control the quality of aconite, the fingerprint spectrum was established by the ultra-performance liquid chromatography high-resolution mass spectrometry (UHPLC-HRMS) method. These analyses were performed with a Thermo Q Exactive  equipped with a quaternary gradient pump, an autosampler, and an orbitrap mass spectrometer detector. The components were eluted with a gradient system consisting of acetonitrile (I) and aqueous 0.1% formic acid (II) . The mass spectrometer scan range was set in the range of 120–1200 m/z. The chromatographic column was a Thermo Syncronis C18 .Flow cytometry was used to detect the number of NK cells and the expression of the NK cell receptors  in peripheral blood and spleen samples, the expression of CD107a in the spleen, and the number of B and T lymphocytes in peripheral blood from each group. Briefly, freshly isolated splenocytes and peripheral blood samples were prepared. Red blood cell lysate was added to cell suspensions for lysis, and samples were centrifuged at 400 × α, interleukin (IL)-1β, and interferon (IFN)-γ in serum. Briefly, the wells of ELISA plates were coated with samples at an appropriate concentration and then incubated in the dark at 37°C for 90 min. Next, the solution was diluted with double-distilled water, gently shaken for 30 s, and then repeated cleaned three times. Then, 100 μL of biotinylated secondary antibody working solution and incubated with the samples in the dark at 37°C for 1 h. Then, 100 μL of streptomycin HRP working solution was added and incubated in the dark at 37°C for 30 min. Next, 100 μL of chromogen was added and incubated at 37°C for 30 min. Finally, the termination solution was added when there was an obvious color gradient. The absorbance at 450 nm was detected with a Bio-Rad model 680 microplate reader .ELISA was used to detect the expression levels of tumor necrosis factor (TNF)-t-test. A two-way analysis of variance was applied to analyze significant differences between two groups affected by time. P < 0.05 was considered to represent a significant difference.Statistical analyses were performed using SPSS software . The data are expressed as mean ± standard deviation. Significant differences between groups were analyzed by one-way analysis of variance, or the Student's UHPLC-HRMS results showed the chemical composition of aconite extract. Several compounds, including isotalatizidine, songorine, and hokbusine B, were identified from aconite extract. The compounds and corresponding retention times are shown in M group, mice in the MD and HD groups (P < 0.05 for MD and P < 0.01 for HD) gained significant weight . The IR in each group was 13.44% (LD), 28.63% (MD), and 42.17% (HD). P < 0.05 for MD vs. M and P < 0.01 for HD vs. M). Hematoxylin and eosin staining of tumor issues (400×) showed that, compared with the M group, poorer blood supply and greater tumor necrosis were present in the aconite groups, with the morphology of tumor tissues visibly destroyed (P=0.005).After 3 weeks of treatment, the mice were euthanized, and tumor tissues were resected and weighed. As shown in estroyed . As showM, the spleen index of aconite-treated mice presented a dose-dependent decrease, while the thymus index steadily increased  and NKG2D levels were significantly reduced in tumor-bearing mice, while aconite had no effect on either . TIGIT eIn peripheral blood, NKp46, NKG2D, and TIGIT expression were significantly upregulated in tumor-bearing mice, while high doses of aconite treatment downregulated the expression of all three receptors .c from mitochondria and the activation of apoptosis [γ significantly upregulated. Strangely, TNF-α and IL-1β are extensively regarded as immunosuppressive cytokines and both were unexpectedly upregulated following aconite treatment, which may be attributed to M2-polarized macrophages. This unexpected phenomenon requires further experimental validation.Several studies have shown that aconite and its main components can suppress the progression of HCC through multiple pathways –9, 11. Tpoptosis . Neverthα [α signaling in HCC progression remains controversial. For one thing, TNF-α can inhibit tumors via activating programmed death cascades in targeted cells, with devastating effects on the function of lysosomal enzymes and/or lysosomal membrane integrity. TNF-α signaling can also be used to induce B7-H1 and PD-L1 expression on CD8+ T cells via several pathways, thereby causing immune resistance [α levels in the serum of tumor-bearing mice. The activation and proliferation of NK cells mediated by aconite treatment may be responsible for the release of TNF-α.NK cells are a significant component of the nonspecific immune response that exert antitumor effects by both direct cytolytic activity and the secretion of immunostimulatory cytokines –16. Studα ; howeversistance –27. We fγ may also be related to this change. To our surprise, TIGIT was upregulated in the spleen following aconite treatment. This may be related to the increased expression of CD155, the shared ligand of TIGIT and CD226 [The functions of NK cells depend on the regulation of activated receptors and inhibitory receptors expressed on their surfaces. The former are represented by the NCR family , NKG2D, NKG2C, CD226 (DNAM-1), 2B4, and CD16, while the latter mainly includes TIGIT, KIRs, CD96, and CD94/NKG2A , 29. NKpnd CD226 , 34. The+/CD8+ T cells and stimulates related cytokines, including IL-4, IL-10, and IFN-γ in patients with septicopyemia [+/CD8+ T cells and NK cells [Aconite and its components have been found to conduct bidirectional regulation of immune status in diseases, including chronic glomerulonephritis, rheumatic arthritis, osteoarthritis, and allergic rhinitis –37. Thiscopyemia . As for NK cells . On the NK cells , 40. OurThe results of our study showed that aconite had a therapeutic effect on HCC subcutaneous tumor-bearing mice, which could be attributed to the immunoregulatory effect of aconite, including adaptive immunity and NK-mediated immunity. This study revealed a novel mechanism through which aconite exerts an antitumor effect, not only through apoptosis induction pathways. Considering the high incidence and poor prognosis of HCC patients, our findings could be of valuable clinical significance in the treatment of HCC. However, the bidirectional regulation of adaptive immunity mediated by aconite as well as the deep mechanism through which aconite activates NK cells still require further investigation. These studies may provide more sound evidence before the introduction of this treatment into routine clinical practice.


S. maltophilia has become a major cause of hospital-associated pneumonia. Aztreonam (ATM) is a monobactam stable to hydrolysis by metallo-β-lactamases (MBLs), including those intrinsically produced by S. maltophilia. Avibactam (AVI) is a non-β-lactam β-lactamase inhibitor that inhibits serine β-lactamases such as ESBLs, KPCs, AmpCs, and some OXAs. ATM-AVI is being developed for treatment of serious infections caused by Gram-negative bacteria, including MBL producers. We evaluated the activity of ATM-AVI against S. maltophilia and B. cepacia from US hospitals.S. maltophilia and 219 B. cepacia were consecutively collected (1/patient) in 77 US medical centers in 2016-2021 and susceptibility tested by CLSI broth microdilution method. Only isolates determined to be the probable cause of infection were included. CLSI/US FDA breakpoints were applied when available.1,565 S. maltophilia, with MIC50/90s of 2/4 mg/L and 98.3% of isolates inhibited at ≤8 mg/L, including 100.0% of isolates from BSI and 97.9% of isolates from pneumonia. Trimethoprim-sulfamethoxazole  and minocycline  also were very active against S. maltophilia, while ceftazidime and levofloxacin were active against 21.3% and 76.2% of isolates per CLSI criteria, respectively (Table). ATM-AVI was also very active against B. cepacia, with MIC50/90s of 4/16 mg/L and 88.1% inhibited at ≤8 mg/L, including 93.3% and 87.3% of isolates from BSI and pneumonia, respectively. The most active comparators tested against B. cepacia were ceftazidime (84.9%S), meropenem (84.5%S), TMP-SMX (83.5%S), and minocycline (80.0%S).The isolate collection were recovered mainly from patients with pneumonia (73.3%) and bloodstream infection . ATM-AVI was very active against in vitro activity against S. maltophilia and B. cepacia from US hospitals and may represent a valuable option to treat infections caused by these organisms. Clinical studies are urgently warranted to evaluate the efficacy of ATM-AVI as well as reevaluate the susceptibility breakpoints for antibiotics currently used to treat infections caused by these organisms.ATM-AVI demonstrated potent Helio S. Sader, MD, PhD, AbbVie: Grant/Research Support|Cidara: Grant/Research Support|Melinta: Grant/Research Support|Nabriva Therapeutics: Grant/Research Support|Pfizer: Grant/Research Support Dee Shortridge, PhD, AbbVie: Grant/Research Support|JMI Laboratory: Employee|Melinta: Grant/Research Support|Menarini: Grant/Research Support|Shionogi: Grant/Research Support SJ Ryan Arends, PhD, AbbVie: Grant/Research Support|GSK: Grant/Research Support|Nabriva Therapeutics: Grant/Research Support|Shionogi: Grant/Research Support Cecilia G. Carvalhaes, MD, PhD, AbbVie: Grant/Research Support|Cidara: Grant/Research Support|Melinta: Grant/Research Support|Pfizer: Grant/Research Support Rodrigo E. Mendes, PhD, AbbVie: Grant/Research Support|Cidara: Grant/Research Support|GSK: Grant/Research Support|Melinta: Grant/Research Support|Nabriva Therapeutics: Grant/Research Support|Office for Assistant Secretary of Defense for Health Affairs: Grant/Research Support|Pfizer: Grant/Research Support|Shionogi: Grant/Research Support|Spero Therapeutics: Grant/Research Support Mariana Castanheira, PhD, AbbVie: Grant/Research Support|Cidara: Grant/Research Support|GSK: Grant/Research Support|Melinta: Grant/Research Support|Pfizer: Grant/Research Support|Shionogi: Grant/Research Support.