Uncontrolled bleeding is a life‐threatening emergency that requires immediate intervention. Currently available on‐site bleeding interventions largely rely on the use of tourniquets, pressure dressing, and other topical hemostatic agents, which can only treat bleeding injuries that are known, accessible, and potentially compressible. Synthetic hemostats that are stable at room temperature, easy to carry, field‐usable, and able to stop internal bleeding at multiple or unknown sources, are still lacking. We recently developed a hemostatic agent via polymer peptide interfusion (HAPPI), which can selectively bind to activated platelets and injury sites after intravascular administration. Here we report that HAPPI is highly effective in treating multiple lethal traumatic bleeding conditions in normal as well as hemophilia models via either systemic administration or topical application. In a rat liver traumatic model, intravenous injection of HAPPI resulted in a significant decrease in blood loss and a four‐fold reduction in mortality rate within 2 h after injury. When applied topically on liver punch biopsy wounds in heparinized rats, HAPPI achieved a 73% of reduction in blood loss and a five‐fold increase in survival rate. HAPPI also exhibited hemostatic efficacy in hemophilia A mice by reducing blood loss. Further, HAPPI worked synergistically with rFVIIa to induce immediate hemostasis and 95% reduction in total blood loss compared to the saline‐treated group in hemophelia mice models. These results demonstrate that HAPPI is a promising field‐usable hemostatic agent for a broad range of different hemorrhagic conditions. Strategies include using substrates  with ligands that can target specific molecules in the hemostasis process, such as the exposed subendothelial matrix components,Previously, we reported a polymer‐based, systemic hemostatic agent, coined as hemostatic agent via polymer peptide interfusion (HAPPI).22.1−1 corresponding to O—H, C—H and C—O stretching vibrations, respectively. The C—H bending vibrations of HA in the range of 1404–1300 cm−1 in the HAPPI are also shown in the HAPPI spectrum. Characteristic peaks from peptides are also visible in the HAPPI spectrum but partly overlap with HA. Compared to HA, the most noticeable change is the presence of peptide bonds . The stretching vibrations of C—O in carboxylate anion from HA (in 1603 cm−1) are not visible after conjugation, suggesting that the carboxylic group was the reaction site, but the newly formed amide bond is overlapped with those from peptides.Given that vWF is overexpressed at the vascular injury sites as well as on activated platelets, and that fibrillar collagen is exposed on vascular injury sites, we chose these two molecules as our targeted moieties, i.e., markers of the injury. A CBP and a VBP were chosen to covalently attach to HA, leading to the formation of HAPPI. Following our original study, we used 1‐ethyl‐3‐(3‐dimethylaminopropyl)carbodiimide (EDC)/N‐hydroxysulfosuccinimide (sulfo‐NHS)‐mediated coupling reaction to conjugate CBP and VBP to HA. The successful conjugation of VBP and CBP was validated by nuclear magnetic resonance (NMR) spectroscopy Figure  and FourThis design of HAPPI with the multivalent presentation of VBPs and CBPs, targets them to the vascular injury sites, recruits activated platelets, and promotes their adhesion, thereby promoting the formation of a vascular plug and inducing hemostasis Figure . The hig2.2p = 0.0070) for similar liver resection degrees  or factor IX (hemophilia B), could be life‐threatening without prompt interventions.With the aim to reduce the cost of the final formulation, we further reduced the dose of rFVIIa by four times with the dose of HAPPI unchanged. As shown in Figure 2.5The cytocompatibility of HAPPI was evaluated in vitro using 3‐‐5(3‐carboxymethonyphenol)‐2‐(4‐sulfophenyl)‐2H‐tetrazolium (MTS) assay in human umbilical vein endothelial cells (HUVECs). HAPPI with concentrations ranging from 0.1 to 6.4 mg/mL was incubated with HUVECs for 24 h. No statistical difference in cell viability was observed between HAPPI treated groups and the untreated control group, although a modest decrease in percentage of viable cells was observed for HAPPI with concentration over 3.2 mg/mL Figure . Overall3Systemic hemostatic agents based on synthetic materials hold great promise in addressing the current challenge in the immediate management of internal, noncompressible bleeding,p = 0.0070) with a mortality rate decrease from 50% to 12.5% within the 2‐h monitoring period after injury. This nonsurgical hemostasis strategy for fatal hepatic hemorrhage is of clinical significance, given that liver is one of the most frequently injured organs in abdominal trauma,This mechanism of action makes HAPPI a promising agent for treating different hemorrhagic conditions, particularly for those benefiting from enhanced and accelerated primary hemostasis. For hemorrhage from minor injuries , intravenously administered HAPPI can staunch bleeding instantaneously without causing systemic thrombosis and toxicity.HAPPI has hemostatic potential for traumatic bleeding from external as well as accessible sources. The rationale here is that the primary action of HAPPI is to rapidly interact with activated platelets and subendothelial proteins at injury sites. Also, HAPPI can be stored and supplied as a dry solid, and only to be reconstituted at the site of injuries. Direct application of HAPPI solids for external or accessible wounds would be another efficient way for hemorrhage control. In a liver punch biopsy model in heparinized rats, HAPPI solids were applied locally at the injury site, yielding a 73% of reduction in blood loss and a significant increase in survival rate (from 20% in the untreated group to 100%). This could be attributed to the in‐situ dissolution of HAPPI at the wound site, concentrating the coagulation components, and the solvated HAPPI promotes hemostasis as in the case of systemic administration. It should be mentioned that HA used in synthesizing HAPPI is critical for the subsequent wound healing process,4Our results demonstrate that HAPPI is highly effective in treating lethal traumatic and hemophilic bleeding via either systemic administration or topical application. Intravenous injection of HAPPI resulted in a significant decrease in blood loss in both the rat liver traumatic model and tail‐vein laceration model in hemophilia A mice. Topical application of HAPPI on liver punch biopsy wounds in heparinized rats achieved a significant reduction in blood loss and an increase in survival rate, compared to the saline‐treated group. HAPPI represents a promising hemostatic agent for a broad range of different hemorrhagic conditions.55.17H, GenScript USA Inc., Piscataway, NJ) and VBP  solutions  were then added to the reaction solution for overnight reaction at room temperature. The conjugate products were purified by dialysis against a large excess amount of DMSO/DI water solution for 3 days  and pure DI water for another 4 days . The conjugates were then freeze‐dried and stored at −20°C until further use. For NMR characterization, the conjugates were dissolved in deuterated water, and 1H and 13C NMR spectra were obtained on Agilent DD2 600 MHz NMR Spectrometer  and Bruker 400‐MHz NMR Spectrometer , respectively with MNova 10.0.1  processing software. FT‐IR spectra were obtained using a Nicolet iS‐50 FT‐IR Spectrometer and analyzed using OPUS software.The synthesis and characterization of HAPPI followed our previous study.5.22 at 37°C. The cells were seeded in a 96‐well plate at a seeding density of 10,000 cells/well. Twenty‐four hours after seeding, media was removed, and the cells were treated with media containing HAPPI (concentrations ranging from 0.1 mg/mL to 6.4 mg/mL) versus media without HAPPI . The treated cells were incubated at normal culture conditions for 24 h. After 24 h, a standard MTS assay  was used to assess the metabolic viability of the cells. This assay is a colorimetric method for determining the number of viable cells in proliferation. Following 24 h of incubation with or without HAPPI, the treatments were removed, and MTS solution was applied to all wells. The cells were incubated at normal culture conditions for 2 h. After 2 h, absorbance was recorded at 490 nm using plate reader.EA.hy926 cells (ATCC) were grown in DMEM supplemented with 10% FBS. Cultures were maintained in a humidified atmosphere at 5% CO5.3All animal experiments were performed as per protocols approved by the Institutional Animal Care and Use Committee of Harvard University (IACUC_17–04–299‐1).5.3.12 inhalation. The abdomen was re‐opened, and blood loss was quantified using preweighed gauze.Sixteen Sprague–Dawley rats  were randomized into two groups (eight rats each): saline and HAPPI. HAPPI was formulated by dissolving it in sterile saline , and dosed at 12 mg/kg. Rats were anesthetized with isoflurane (2%–3%) and kept under isoflurane anesthesia for the entire surgery and monitoring period. Following the incision at the left inguinal, the left femoral vein was isolated from the connected tissue using a Size 3 suture and cannulated with a 22‐gauge catheter with prefilled saline. The catheter was flushed with 50 μL of saline to avoid blood coagulation inside the catheter. Subsequently, the abdomen was entered via a midline incision. The liver was exposed, and the left and right lateral lobes were resected at around 2.5 cm from the edge. Immediately following the resection, saline (1 mL/kg) or HAPPI (1 mL/kg) was infused through the femoral vein catheter. This was the time zero, and the rat was monitored for another 2 h or until death, whichever came first. The mortality was determined by the cessation of respiratory movements. Anesthetic depth was checked throughout the monitoring period. Survival time was recorded. Rats that survived for the 2‐h monitoring period were euthanized through CO5.3.2Seventeen Sprague–Dawley rats  were randomized into three groups: untreated, HAPPI, and TachoSil. Similar to the liver laceration model as described above, the left femoral vein was cannulated under isoflurane anesthesia. The liver was exposed after a midline incision. Right before liver injury, heparin solution (250 IU/kg) was infused via the catheter. The left lateral liver lobe was injured by a 5‐mm punch biopsy  with a depth of 5 mm. The liver core was removed, and rats in the untreated group was bleeding freely. For the HAPPI and TachoSil group, lyophilized HAPPI form and TachoSil patch were trimmed to approximately 1 × 1 cm and applied to the injury site immediately following the injury. Rats were monitored for 2 h or until death, whichever came first. The survival time was recorded, and blood loss was quantified, as described in the liver laceration model section.5.3.3F8tm1Kaz/J, Female, 8–10 weeks, Jackson Laboratory, stock #004424). The tail‐vein bleeding model in these mice was established based on a previously reported procedureTo evaluate the hemostatic efficacy of HAPPI in hemophilia models, mutant mice with severe factor VIII deficiency (less than 1% of wild‐type levels) were used : HAPPI and saline control. HAPPI  or saline was intravenously administered on Days 1, 30, 60, and 90. On Day 97, blood was withdrawn, and mice were sacrificed. Vital organs including the brain, heart, kidneys, liver, lungs, and spleen were harvested and fixed by formalin for histological analysis. Organ sections were stained with H&E. Blood samples were submitted to IDEXX Laboratories  for hematological and biochemistry analyses. For hematological analyses, whole blood was collected into a 0.5‐mL EDTA anticoagulant tube, mixed by gently inverting the tube to prevent clotting and hemolysis, and stored on ice. For biochemistry analyses, blood samples were collected into a 0.8‐mL serum gel tube, and serum was separated by centrifugation at 2000g for 10 min after 20 min of clotting at room temperature.Ten healthy BALB/c mice  were randomly allocated to two groups ; data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); writing – original draft (lead); writing – review and editing . Mayumi Ikeda‐Imafuku: Methodology (supporting). Zongmin Zhao: Formal analysis (supporting); methodology (supporting). Maithili Joshi: Formal analysis (supporting); methodology (supporting). Samir Mitragotri: Conceptualization ; funding acquisition (lead); writing – review and editing .S.M. and Y.G. are inventors on a patent application that covers some aspects of the technology reported in this manuscript (owned and managed by Harvard University).Data S1. Supporting Information.Click here for additional data file.


Although polygenic scores (PGS) for autism have been related to various psychiatric and medical conditions, most studies to date have been conducted in research ascertained populations. We aimed to identify the psychiatric and physical conditions associated with autism PGS in a health care setting.We computed PGS for 12,383 unrelated participants of African genetic ancestry (AF) and 65,363 unrelated participants of European genetic ancestry (EU) from Vanderbilt’s de-identified biobank. Next, we performed phenome wide association studies of the autism PGS within these two genetic ancestries.p = 0.05/1374 = 3.6 × 10−5) in the EU participants, including mood disorders (OR (95%CI) = 1.08(1.05 to 1.10), p = 1.0 × 10−10), autism (OR (95%CI) = 1.34(1.24 to 1.43), p = 1.2 × 10–9), and breast cancer (OR (95%CI) = 1.09(1.05 to 1.14), 2.6 × 10−5). There was no statistical evidence for PGS-phenotype associations in the AF participants. Conditioning on the diagnosis of autism or on median body mass index (BMI) did not impact the strength of the reported associations. Although we observed some sex differences in the pattern of associations, there was no significant interaction between sex and autism PGS. Finally, the associations between autism PGS and autism diagnosis were stronger in childhood and adolescence, while the associations with mood disorders and breast cancer were stronger in adulthood.Seven associations surpassed the Bonferroni adjusted threshold for statistical significance (Our findings indicate that autism PGS is not only related to autism diagnosis but may also be related to adult-onset conditions, including mood disorders and some cancers.Our study raises the hypothesis that genes associated with autism may also increase the risk for cancers later in life. Future studies are necessary to replicate and extend our findings.The online version contains supplementary material available at 10.1186/s11689-023-09485-x. Autism is a heterogeneous and complex neurodevelopmental diagnosis that is characterized by difficulty with certain social interactions, restricted interests and/or repetitive behaviors, as well as features that affect school and work performance and may impact other areas of life. While language delay or difficulty is often a feature of autism, there is marked variation, in language and cognitive development, as well as variation in co-occurring psychiatric and medical conditions .Co-occurring conditions are estimated to affect at least 70% of people with autism . A recenGenome-Wide Association studies (GWAS) demonstrate that autism is polygenic, i.e., it is associated with thousands of genetic variants of small effect . The effThe majority of studies using PGS, however, have only tested the associations between autism PGS and co-occurring conditions in volunteer or research ascertained settings, rather than in health care settings. For example, a study in 334,976 volunteer participants from the UK biobank tested the associations between autism PGS and a number of general health and mental health characteristics and found associations with eight general health related outcomes and one mental health related outcome . HoweverThe wealth of clinical information in Electronic Health Records (EHR) linked with genetic data provides the ability to conduct phenome-wide association scans (pheWAS) across a wide range of psychiatric and medical conditions. Using Vanderbilt’s biobank (BioVU) that is linked to de-identified medical records, we conducted a pheWAS to identify the physical and mental health conditions that are associated with autism PGS across genetic ancestries, sexes, and age-groups.The project was approved by Vanderbilt’s Institutional Review Board (IRB) (#190418). The phenotypic data originated from the synthetic derivative (SD), a de-identified clinical data repository of the EHR at VUMC, that includes records of over 3.2 million patients, and is updated regularly. The data includes billing codes from the International Classification of Diseases, 9th and 10th editions (ICD-9 and ICD-10), laboratory values, medication history, Current Procedural Terminology (CPT) codes, and clinical reports. BioVU is the associated biobank that accrues DNA samples from more than 280,000 of these patients  (OR (95%CI) = 1.09(1.05 to 1.14), p = 2.6 × 10−5) (Table −7).Seven associations surpassed the Bonferroni significance threshold ( = 1.341.4 to 1.43 = 1.341.4 to 1.435) Table . The assWhen BMI was added as a covariate in the PheWAS, all previously significant associations remained largely unchanged   (Table p = 9.3 × 10−10), depression (OR (95%CI) = 1.09 (1.06 to 1.12), p = 3.8 × 10−8), malignant neoplasm of female breast (OR (95%CI) = 1.10 (1.05 to 1.14), p = 2.23 × 10−5), and breast cancer (OR (95%CI) = 1.09 (1.05 to 1.14), p = 2.7 × 10−5). The sex-interaction test indicated that there was no statistical evidence of differences between the sexes once the baseline prevalence of the condition in the sample was taken into account  = 1.28(1.17 to 1.38), %CI = 1.2nt Table . Howeverp = 3.0 × 10−5) (Table Autism PGS was associated with autism diagnosis in this age group (OR (95%CI) = 1.28 (1.16 to 1.40),  p = 1.9 × 10−5)  = 1.36 (1.22 to 1.50),5) Table . No otheThere was no evidence for associations in this age group Table .p = 1.1 × 10−5), and depression (OR (95%CI) = 1.13 (1.07 to 1.19), p = 3.4 × 10−5) in this age group (Table Autism PGS was associated with mood disorders (OR (95%CI) = 1.22  and depression (OR (95%CI) = 1.09 (1.05 to 1.12), p = 2.8 × 10−6), while there was a negative association with electrolyte imbalance (OR (95%CI) = 0.93, 0.89 to 0.96), p = 2.8 × 10−5) (Table Autism PGS was associated with mood disorders (OR (95%CI) = 1.09  Table .There was no evidence for associations in this age group Table .Our study is the first to test the associations of autism PGS with psychiatric and medical conditions in an EHR context in a tertiary health care setting. There were seven medical diagnoses associated with autism PGS in the EU participants, including autism. These findings suggest that autism, as defined by ICD codes in the EHR, does reflect the genetic architecture of autism as defined in other independent samples. We did not estimate this association in the AF participants, as there were only 59 participants with an ICD diagnosis of autism in this sample (compared to 476 in the EU sample), and the minimum number we set for including case patients in the PheWAS is 100.Autism PGS was also associated with mood disorders in the EHR, an association that is not surprising given that autism is both phenotypically and genetically correlated with depressive symptoms and major depression , 21. OurAutism PGS was associated with a slight increase in breast cancer risk in our study. While epidemiologically there is limited evidence of an association between autism and cancer , there iOne limitation of our study is the low sample size of the AF participants that likely affected our power. Similarly, sample size difference across age groups may also have reduced power and increased the type II error rate. Another limitation of our study is that our data comes from a single medical center, Vanderbilt University Medical Center, located in Nashville, Tennessee. Thus, the data included in our study is unlikely to be representative of the entire population. However, it should be noted that our study included a large and diverse sample of patients due to the broad catchment of the Vanderbilt Affiliated Health Network (VHAN) which includes outpatient clinics in Tennessee and eight bordering states. As with all research collections, there is a sampling bias. In this case, the sampling bias includes individuals who seek medical care at one or more Vanderbilt affiliated clinics. For example, individuals with reliable transportation, close proximity to VHAN clinics, and resources to cover the cost of medical care are more likely to be represented in the data. Importantly, the VHAN child and adolescent clinics accept both public and private health insurance which reduces the impact of insurance status on entry into the medical system for children. Nevertheless, there are limits to the generalizability of our findings because of the sampling strategy. Collider bias could result from selection of people into a health care setting. However, this is only likely to have a significant impact on the results shown here if autism polygenic scores are also correlated with the sample selection. To the best of our knowledge, our biobank is not enriched for individuals with a family history of autism, mood disorders and/or breast cancer, nor are individuals with these conditions more likely to participate in the biobank. Additionally, autism polygenic scores are not clinically assessed, which reduces the potential for PGS to influence health care, thus reducing the potential for collider bias. Lastly, with regards to ascertainment bias, it is possible that individuals with autism are more likely to be given certain additional diagnoses. For instance, individuals with autism are more likely to be coded with developmental delays and may be more likely to come to clinical attention for other behavioral or psychiatric conditions that are often diagnosed and documented in the EHR. However, it should be noted that we did not specifically restrict our sample to individuals with autism. Instead, we examined the autism polygenic score across the entire biobank, which again reduces the potential for ascertainment bias as the only ascertained variables included entry into the EHR and the biobank. Lastly, we also adjusted for potential confounding factors such as age, sex, and the first 10 principal components of genetic ancestry. Finally, future studies in other cohorts/datasets are needed to replicate our findings.Our study is the first to examine the associations between autism PGS and psychiatric and medical conditions in the EHR of a tertiary care center. We replicated the association between autism PGS and autism, and also identified associations with mood disorders and breast cancer. Our findings indicate that genes involved in autism may also increase risk for breast cancer later in life. Future studies are essential to replicate our findings.Additional file 1:Supplementary Table S1. PheWAS results of the African American genetic ancestry population. Supplementary Table S2. PheWAS results of the European genetic ancestry population. Supplementary Table S3. PheWAS results conditional on BMI in the European genetic ancestry population. Supplementary Table S4. PheWAS results conditioning on autism diagnosis. Supplementary Table S5. PheWAS results in males of European genetic ancestry. Supplementary Table S6. PheWAS in females within the European genetic ancestry population. Supplementary Table S7. Results of sex-interaction analysis in the European genetic ancestry population. Supplementary Table S8. PheWAS results in the 0 to 11 age group (European genetic ancestry population). Supplementary Table S9. PheWAS results in the 12 to 18 age group (European genetic ancestry population). Supplementary Table S10. PheWAS results in the 19 to 25 age group (European genetic ancestry population). Supplementary Table S11. PheWAS results in the 26 to 40 age group (European genetic ancestry population). Supplementary Table S12. PheWAS results in the 41 to 60 age group (European genetic ancestry population). Supplementary Table S13. PheWAS results in the 61 to 100 age group (European genetic ancestry population).


To describe inpatient resource use in the 2 years following infantile epileptic spasms syndrome (IESS) diagnosis, examine the association between clinical/demographic variables and incidence of readmission, and identify risk factors/reasons for frequent readmissions.Retrospective cohort analysis of readmissions (scheduled/unscheduled) within the first 2 years following IESS diagnosis, details of readmissions , demographic/clinical variables, and reasons for readmissions were collected. Negative binomial regression analysis evaluated associations between incidence of readmissions  and demographic/clinical factors. Logistic regression assessed the risk of having recurrent readmissions (≥5 readmissions).P = 0.006), technology assistance (P ≤ 0.001), and multispecialty care (P = 0.01); seizure freedom (P = 0.015) and known etiology (P = 0.011) lowered the incidence of readmissions. Examining unscheduled readmissions separately, increased readmissions occurred with public insurance (P = 0.013), technology use (P ≤ 0.0.001), and multispecialty care (P = 0.013); seizure freedom decreased unscheduled readmissions (P = 0.006). Technology assistance  increased the odds (P = 0.007) for recurrent readmissions. Reasons for readmissions included EEG monitoring  (51%), acute medical issues (21%), and seizure exacerbation (15%). Protocol‐driven readmissions declined an estimated 52% following protocol modification during the study.Among 93  new‐onset IESS patients, there were 394 readmissions (56% scheduled and 44% unscheduled) within 2‐years following IESS diagnosis. Mean length of stay was 3.5 days (SD: 5.9). Readmissions occurred in 82 patients (88%) and 37 (40%) experienced ≥5 readmissions. On multivariate regression analysis, readmissions were increased with use of multiple first‐line treatments for IESS . Since readmissions are increased by intrinsic patient characteristics such as medical complexity  or epilepsy‐related issues, the preventability of readmissions is uncertain, except for protocol‐driven ones. In the 2 years following IESS diagnosis, there is substantial inpatient resource use, with nearly 40% experiencing ≥5 readmissions.Most readmissions are epilepsy related and scheduled readmissions (56%) are more frequent than unscheduled (44%) ones.Medical complexity/technology assistance increased frequency of readmissions; public insurance use increased unplanned readmissions only.Readmissions included EEG monitoring (planned and unplanned) (51%), acute medical issues (21%), and seizure exacerbation (15%).Protocol‐driven inpatient diagnostic admissions decreased by an estimated 52% with protocol modification.1West syndrome, the triad of (a) epileptic spasms, (b) hypsarrhythmia on EEG, and (c) developmental stagnation or regression,Following IESS diagnosis, infants are at risk for multiple hospitalizations. During a 2‐year follow‐up of Medicaid beneficiaries with new‐onset IESS, 77% had at least one emergency department (ED) visit or hospitalization.Epilepsy patients utilize more health resources than those with other chronic health conditions.While some studies have recognized the magnitude of IESS medical burden,We conducted a retrospective study of hospitalizations in IESS patients with goals to (a) examine inpatient resource use (readmissions and length of stay) in the 2 years following IESS diagnosis, (b) examine factors that lead to increased frequency of readmissions, including identification of risk factors for frequent readmissions (stratified by readmissions frequency), and (c) evaluate reasons for readmissions.2This is an IRB‐approved retrospective cohort analysis of readmissions in the first 2 years following the diagnosis of IESS (index admission).2.1Children born between January 2014 and June 2019 and had new‐onset IESS, diagnosed between 2 months and 2 years of age with a minimum follow‐up of 2 years, at Boston Children's Hospital (BCH) were included. Patients were excluded if they lacked 2‐year follow‐up, were deceased, or were referred to BCH for a second opinion.IESS patients were identified from the institutional data warehouse for IESS‐specific diagnosis codes . We verified this list against BCH clinical note search utility (“Hound Dog”) searching for “infantile spasms,” “ACTH,” “prednisolone,” and “vigabatrin” through notes within the study's date range (2014‐2019).2.2Hospital readmission  was the main outcome measure. We included all‐cause readmissions (scheduled/unscheduled) affiliated with any medical or surgical service within the first 2 years following index admission. Data collected included number of readmissions, time between readmissions, and total length of stay.The primary reason for readmission was determined from chart review (TM). Two reviewers (CH and AS) independently reviewed 50% of charts verifying reasons for readmissions; discrepancies were resolved through discussion. Reasons for readmissions were categorized as scheduled or unscheduled readmissions.Scheduled readmissions primarily included presurgical evaluations, elective procedures, ketogenic diet initiation, protocol‐driven overnight EEG monitoring for verifying electroclinical remission of IESS, and EEG monitoring for surveillance. Prior to November 2017, our protocol suggested inpatient overnight EEG monitoring to confirm electroclinical remission for patients who were spasm free within 2 weeks following first‐line IESS therapy. This practice is similar to other academic institutions across the United States and in the proposed protocol for treatment of IESS.Reasons for unscheduled readmissions included: acute medical issues, seizure exacerbation, and other unanticipated hospitalizations. Seizure exacerbation needs urgent attention to prevent seizure escalation/status epilepticus. The unpredictable nature/urgency makes this type of readmission an unplanned one.EEG monitoring for characterization of events (epileptic/nonepileptic) was categorized either as scheduled (admitted via treating neurologist) or unscheduled (admitted via ED). Admission via neurologist or ED depended upon level of clinical concern by the clinician and/or family. When multiple reasons for readmission were noted, the dominant symptom necessitating admission/treatment was chosen as the reason for readmission.2.3We collected demographic data  and IESS‐related information , and number of first‐line treatments tried). Further data collection included: use of the ketogenic diet, seizure status at last follow‐up (seizure free for 1 year vs continuing seizures), use of technology assistance (described below), and involvement of multispecialty care .First‐line therapies included ACTH, prednisolone, and vigabatrin.2.4P‐values. An IRR of <1 indicated that the incident rate was lower in one group compared to another group, and vice versa. For example, an IRR of 0.88 would indicate a 12% reduction in rate of hospitalization when comparing the group in the numerator to that of the denominator. The number of protocolized readmissions pre‐/postprotocol change was compared using a Chi‐square test.Descriptive statistical analysis , univariate, and multivariate generalized linear regression were performed. Negative binomial regression analysis  in multivariate negative binomial and logistic regression analysis without adjusting for multiple comparisons. SAS version 9.4  was used for analyses.Following IESS diagnosis, mean emergency room visit/hospitalization rates of 1.6‐3.5 per person per year3Of 125 IESS patients identified, 93  were eligible for the study : 26 used gastrostomies; two used nasogastric tubes; one used ventriculoperitoneal (VP) shunt; and one tracheostomy. Technology use was noted prior to or around IESS onset in 15 of these 30 patients; of these, 12 had a g‐tube and one patient each had an NG‐tube (g‐tube placed later), VP shunt, and tracheostomy with g‐tube. In the remaining 14 patients, g‐tube was placed at a median 7.5 months  from IESS diagnosis, but many of these patients had history of aspiration and/or malnutrition for several months before g‐tube placement. Multispecialty care was seen in 68% of patients. Insurance carrier was public in 35 patients (38%), private/dual in 52 patients (56%), and unknown in six patients (6%).3.1In the 2 years following the index admission, 11 did not have any readmissions, 24 had scheduled readmissions only (six of the 24 had exclusively protocol‐driven ones), and 58 had both scheduled and unscheduled readmissions. Overall, 82 of 93 patients (88.2%) experienced at least one further readmission and 37 of 93 (39.8%) experienced ≥5 readmissions.P ≤ 0.001; Figure In the first 2 years following IESS diagnosis, there were a total of 394 readmissions, with a mean of 4.2 (SD 3.8) readmissions per patient, 220 (56%) were scheduled, and 174 (44%) were unscheduled. Readmissions occurred throughout the study period, most frequently in the first 6 months after IESS diagnosis Figures . SchedulP = 0.025).The subset of patients who had fewer than five readmissions had 101 readmissions, with scheduled readmissions at 66%; in patients with ≥5 readmissions (n = 293 admissions), around half (52.5%) were scheduled. Unscheduled readmissions  were more common in patients with ≥5 admissions vs those with <5 readmissions  with mean length of stay of 3.5 days (SD: 5.9). Median time between readmissions was longer for those with <5 hospitalizations  versus those with ≥5 readmissions .P ≤ 0.001), use of ketogenic diet (P = 0.011), use of technology assistance (P ≤ 0.001), and multispecialty care (P ≤ 0.001). Seizure freedom at last follow‐up (P ≤ 0.001) was associated with decreased frequency of rehospitalizations. On multivariate regression, multiple first‐line treatments , use of technology assistance , and multispecialty care  increased incidence of readmissions; seizure freedom at last follow‐up , and known etiology  lowered the incidence of hospital readmissions. No other variables affected the incidence of rehospitalizations .In the 2 years following IESS diagnosis, univariate analysis of all readmissions , use of ketogenic diet (P = 0.010), technology assistance (P ≤ 0.001), multispecialty care (P ≤ 0.001), and public insurance (P = 0.035) increased unscheduled readmissions; seizure freedom at last follow‐up reduced the frequency of readmissions (P ≤ 0.001). On multivariate analysis, technology assistance , multispecialty care , and use of public insurance  increased the incidence of unscheduled readmissions, and seizure freedom at last follow‐up reduced the incidence of unscheduled readmissions .When examining unscheduled readmissions separately  for having ≥5 admissions (Table Comparing a binary outcome (categorized as having 4 or less vs ≥5 readmissions), technology assistance increased the odds , acute medical issues , seizure exacerbation , procedures , ketogenic diet initiation , and other reasons .3.3The majority of scheduled readmissions (76%) were for EEG monitoring: including protocol‐driven verification of electroclinical remission of IESS (n = 67), EEG surveillance for reasons other than IESS remission (n = 30), characterization of events (n = 59), and presurgical monitoring (n = 12). Elective surgery  accounted for 11.3% of scheduled admissions, ketogenic diet (n = 15) for 6.8%, and other reasons (n = 12) for 5.5%.EEG surveillance was performed for a variety of reasons other than IESS posttreatment monitoring . EEG surveillance also occurred in cases where historically, seizures were unrecognized by family to assess seizure burden, among other reasons. Characterization of events was related to new or changing character of events or for spasms‐like spells. Other reasons included initiation of ACTH therapy, trial of BiPAP support, monitoring following postprocedural sedation, hypoxia noted during sleep study, etc.3.4Acute medical issues (n = 83) and seizure exacerbation (n = 57) accounted for 80% of unscheduled readmissions. Unscheduled EEG monitoring (n = 31) due to worsening spells or new spells with vital sign changes (sometimes with acute illness) constituted 18% of unscheduled visits. Other reasons (n = 3) included an unscheduled admission for ACTH initiation and two readmissions for unplanned procedures. Acute medical issues included respiratory illness, gastrointestinal illness, neurological symptoms , dehydration, electrolyte imbalance, and hypertension, among others.3.5P = 0.002) and an estimated 52% decline in protocolized readmissions.Our protocol for verifying IESS remission was changed in November 2017 to use prolonged outpatient EEGs over inpatient monitoring. Prior to the change, 56 patients were diagnosed with IESS and 37 were diagnosed after. Prior to protocol change, 62.5% (35 of the 56) patients had at least one protocolized readmission and 30% (11 of the 37) had at least one protocolized readmission after the change. This represents a significant difference in readmissions before and after the protocol modification . This is an underestimate of the disease morbidity as we have not explored ambulatory care and emergency visits incurred by these patients. We found scheduled readmissions (56%) were more frequent than unscheduled ones (44%), especially in the first 6 months following IESS diagnosis. Our data emphasize that focusing exclusively on unscheduled readmissions would overlook many scheduled readmissions and obscure a potentially important indicator of total inpatient care delivered. Our study identified a subset at risk for frequent readmissions, namely patients with medical fragility and poor seizure control. One‐third of our patients needed technology assistance , a key attribute specifically for intensive resource use.The frequency of readmissions  in our patients was affected by IESS/epilepsy‐related factors  and by medical complexities . A separate analysis of unscheduled readmissions showed similar results. Additionally, public insurance use increased unscheduled readmissions in our patients, which was concordant with multicenter data involving children/infants, where public insurance use was a risk factor for readmissions.Within 2 years of diagnosis, our patients experienced an intense cluster of readmissions (40% had ≥5 readmissions within 2 years), which may be intrinsic to the expected progression of this disorder, as ~50% of IESS patients have IESS and other seizure types occur in 30‐78% within 12‐18 months after IESS onset.In our patients, the majority of readmissions were epilepsy related  similar to pediatric neurology/epilepsy readmissions, where seizures were the principal cause of unplanned or all‐cause readmissions.Nearly 60% of our post‐IESS diagnosis readmissions were scheduled. The majority of scheduled readmissions were for EEG monitoring, which may be inevitable when indicated for disease‐specific reasons, such as for patients with a history of subtle spasms/spasms‐like spells,Regardless of these challenges, we found a decline in the protocol‐driven readmissions for verification of IESS remission following protocol change. This implies that standardization of practice has the potential to prevent at least some readmissions. We have subsequently demonstrated that this modified approach along with clinical assessment was sufficient to detect treatment failure for IESS.Our study was not designed to address preventability of readmissions. Nevertheless, our study findings could rationalize other approaches to contain hospitalizations. Although not available at our hospital, prolonged outpatient EEG (4‐12 hours) has been utilized to characterize frequent spells successfully and can potentially prevent some of the scheduled readmissions.4.1Smaller sample sizes from a single institution, retrospective data, and lack of a control group (infantile nonspasms epilepsy) are limitations. There may be unknown confounders. Certain attributes of our study question the generalizability and strength of our findings; single‐institution studies may be influenced by center‐specific care practices, availability of extended outpatient EEG, scope of practice (epilepsy surgery and ketogenic diet), candidate selection for technology assistance, timing of procedures, etc. Variations in readmission rates occur across adult hospitals potentially due to area differences in tendency to hospitalize or availability of hospital beds.Despite these limitations, this study adds to our body of knowledge by providing a detailed analysis of hospital resource use in new‐onset IESS. If our study findings are verified in large administrative databases, we can then investigate potential strategies to reduce hospitalizations, with the understanding that preventing readmissions is complex and may be influenced not only by demographic/patient characteristics but also by caregiver perception of discharge readiness.5Our findings are helpful to counsel families regarding hospital resource use of infants with new‐onset IESS. Infants with IESS are at risk for recurrent hospitalizations within 2 years following diagnosis of IESS, mainly related to epilepsy care . The subset of IESS patients with technology dependence and poorly controlled epilepsy are at risk for frequent readmissions. We suggest that, among the spectrum of childhood epilepsy, infants with newly diagnosed IESS should be considered “at risk” for intense healthcare utilization, especially in the first 2 years following IESS diagnosis. Since readmissions are increased by intrinsic patient characteristics, such as medical complexity or epilepsy‐related issues, the preventability of readmissions in new‐onset IESS is uncertain for most readmissions, except for protocol‐driven readmissions, which are modifiable to certain extent.Harini: Conceptualization; data acquisition, analysis, and interpretation; and writing—original draft and review/editing; Yuskaitis: data acquisition, analysis, and interpretation and critical revision of manuscript; Singh: data acquisition, analysis, and interpretation and critical revision of manuscript; McHugh: data acquisition, analysis, and interpretation and critical revision of manuscript; Liu: data acquisition and critical revision of manuscript; DeLeo: data analysis and critical revision of manuscript; Gupta: data acquisition and critical revision of manuscript; Marti: data acquisition, analysis and interpretation; and critical revision of manuscript; Zhang: data analysis and interpretation and critical revision of manuscript; Libenson: data acquisition and analysis and critical revision of manuscript; Berry: Conceptualization; data acquisition, analysis, and interpretation; and critical revision of manuscript;None of the authors has any conflict of interest to disclose. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.We confirm that we have read the Journal‘s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.


Dear Editor,Colorectal cancer (CRC) is the third most common cancer and the second most common cause of cancer‐related death globally.Transfer RNA (tRNA), as an important regulatory noncoding RNA, can generate a variety of tRNA‐derived fragments (tRFs). Available evidences indicated that tRFs were involved in the development of various diseases, including CRC.Firstly, a cross‐sectional study was conducted, in which five pairs of age‐ and gender‐matched AA and CRC patients were enrolled. High‐throughput sequencing was employed to detect levels of tRFs in tissues and then to establish the pool of potential tRFs. Nine tRF subtypes were identified, among which tRF‐1, tRF‐3a, and tRF‐5c accounted for majority Figure , and theAfterwards, an enlarged cohort including 40 pairs of AA and CRC patients was established by RT‐PCR. The results indicated that tRF‐Tyr‐GTA‐081 was significantly downregulated and tRF‐Ala‐AGC‐060 was significantly upregulated in the AA and CRC groups, while others did not show any difference Figures  and S1D.Eventually, we designed a third cohort including 37 AA patients, 30 CRC patients, and 91 healthy controls (HCs) to testify above results by RT‐PCR in blood samples. Then, two steps were arranged. To be specific, an exploratory dataset consisting of 10 AA patients, 10 CRC patients, and 10 HCs was initially set to preliminarily assess the diagnostic performance, and then a validation dataset with an enlarged sample size  was set to further demonstrate the results. In the exploratory dataset, similar to tissue samples, tRF‐Tyr‐GTA‐081 was downregulated, while tRF‐Ala‐AGC‐060 was upregulated Figure . And theThereafter, the diagnostic performance of tRFs combination in AA or CRC was analyzed. In AA, the tRFs combination generated an AUC value of 0.777 with sensitivity of 67.57% and specificity of 81.11% Figure . And in To explore the potential regulatory mechanism, we identified probable targeted mRNAs and built tRF‐mRNA networks Figure . The potIn conclusion, we found an innovative tRFs combination (tRF‐Tyr‐GTA‐081 and tRF‐Ala‐AGC‐060) with satisfactory diagnostic performance and sensitivity in CRC. The performance of tRFs combination would be further enhanced when evaluated together with CEA. Moreover, tRFs combination presented promising performance for early‐stage CRC screening as well. Therefore, this scheme would provide a new choice for early‐stage CRC screening and benefit CRC management.G.J., Y.X., and Y.D. conceived, designed, and supervised the study. Y.D., L.D., and Y.X. collected samples. Y.D., L.D., W.Z., and J.X. performed the experiments and analyzed the data. Y.D., W.Z., and G.J. wrote the paper and edited and revised the paper. All authors reviewed and approved the final manuscript.The authors declare no conflicts of interest.Shanghai Rising‐Star Program (21QA1409000); Shanghai Frontier Research Base of Disease and Syndrome Biology of Inflammatory cancer transformation (2021KJ03‐12).This study was approved by the Ethics Committee of Longhua Hospital (2019LCSY020), and informed consent was obtained from all participants.Supporting InformationClick here for additional data file.


Glioblastoma (GBM) is notorious for malignant neovascularization that contributes to undesirable outcome. However, its mechanisms remain unclear. This study aimed to identify prognostic angiogenesis‐related genes and the potential regulatory mechanisms in GBM. RNA‐sequencing data of 173 GBM patients were obtained from the Cancer Genome Atlas (TCGA) database for screening differentially expressed genes (DEGs), differentially transcription factors (DETFs), and reverse phase protein array (RPPA) chips. Differentially expressed genes from angiogenesis‐related gene set were extracted for univariate Cox regression analysis to identify prognostic differentially expressed angiogenesis‐related genes (PDEARGs). A risk predicting model was constructed based on 9 PDEARGs, namely MARK1, ITGA5, NMD3, HEY1, COL6A1, DKK3, SERPINA5, NRP1, PLK2, ANXA1, SLIT2, and PDPN. Glioblastoma patients were stratified into high‐risk and low‐risk groups according to their risk scores. GSEA and GSVA were applied to explore the possible underlying GBM angiogenesis‐related pathways. CIBERSORT was employed to identify immune infiltrates in GBM. The Pearson's correlation analysis was performed to evaluate the correlations among DETFs, PDEARGs, immune cells/functions, RPPA chips, and pathways. A regulatory network centered by three PDEARGs  was constructed to show the potential regulatory mechanisms. External cohort of 95 GBM patients by immunohistochemistry (IHC) assay demonstrated that ANXA1, COL6A1, and PDPN were significantly upregulated in tumor tissues of high‐risk GBM patients. Single‐cell RNA sequencing also validated malignant cells expressed high levels of the ANXA1, COL6A1, PDPN, and key DETF (WWTR1). Our PDEARG‐based risk prediction model and regulatory network identified prognostic biomarkers and provided valuable insight into future studies on angiogenesis in GBM. In this study, we have constructed a regulatory network based on angiogenesis‐related gene signature and identified a signaling pathway, WWTR1 (TAZ)‐ANXA1, COL6A1, and PDPN‐EMT, which may provide new therapeutic strategies by utilizing the signaling axis in tumor angiogenesis we proposed for GBM patients. High metabolic demands of GBM create hypoxic areas and require newly generated vessels to contain oxygen and nutrients to meet the need of GBM tumor growth and facilitate the migration of GBM cells. Most importantly, more than 90% of glioma recurrences are found within 2 cm of the focal area and prone to malignant progression,Here, transcriptome sequencing data of GBM patients were evaluated, and the correlation of expression of angiogenesis‐related genes and clinical outcomes was explored. Single‐cell RNA sequencing data and immunohistochemistry (IHC) was used to validate the expression of core angiogenesis‐related genes. Connectivity map database was employed to select potential chemical agents targeting angiogenesis‐related genes.22.1https://portal.gdc.cancer.gov/). R packages of “affy” and “impute” in R/Bioconductor software were utilized for RNA‐seq data processing. Elaborate sample information of demographic feature , clinical stages, survival end point  even histopathology were also retrieved. Samples with imprecise clinical information was excluded. 318 tumor‐flag TFs and its expression profiles were collected from Cistrome database (http://cistrome.org/).immport.org/home) database provided us with immune‐correlated genes and immune cells.BioRender.com (https://biorender.com/).RNA sequencing (RNA‐seq) data and 173 samples obtained from patients with GBM were collected from TCGA , and we conducted the research in strict accordance with its regulations. The inclusion criteria of our study were set to cover candidates as: (1) patients were aged at 18 or over, diagnosed with GBM pathologically, and have been surgical treated in our center; (2) patients' clinical information for this study were completed. The exclusion criteria were set to be precise as: (1) patients were diagnosed with other malignancies; (2) patients' pathological sections were missing; and (3) patients were untraceable to follow‐up.2.22 (fold change [FC]), false discovery rate (FDR), t value and p value of each detected gene between 168 GBM tumor samples and 5 para‐tumor tissue samples were calculated by R package “limma.” Differentially expressed genes were defined from genes of absolute value log2 (FC) > 1 and p < 0.05. Angiogenesis‐related genes (ARGs) info were obtained from Molecular Signature Database  and members from angiogenesis‐related gene set. Likewise, differentially expressed ARGs (DEARGs) were determined using “limma” package, based on the thresholds of |log2 (FC)| > 1.0 and p < 0.05. Gene set enrichment analysis (GSEA) and over‐representation analysis (ORA) were conducted based on MSigDB and Gene Ontology (GO) database, to identify potential biological processes involving the DEARGs. The biological function of the overlap DEARGs was interpreted. There was statistical significance in the p value (<0.05).To analyze the expression profile, the log2.3p value <0.05 were adopted as prognostic DEARGs (PDEARGs). Forest plot was generated to describe hazard ratio (HR) and p value of each PDEARGs.To ascertain the prognostic indicators from DEARGs, univariate Cox regression analysis was conducted by the expression profiles and clinical outcomes of each sufferer with GBM. Specifically, DEARGs with 2.41 expression *β1 + gene2 expression *β2 + … + genen expression *βn (β:regression coefficient of the genes in the model).We then set a loop by R programming language where the GBM patients were partitioned into train and test cohort by ratio of 3:2 randomly. In each cycle, by using R package “glmnet”,p value <0.01 and AUC >0.85 in the train cohort, along with p < 0.05 and AUC >0.80 in the test cohort.Risk score of each GBM patient was obtained, and directly displayed by vascular content in Hematoxylin–Eosin (HE) staining and Ki‐67 in clinicopathological histochemistry. According to the cutoff value of RS, these GBM patients were separated into the group of low‐ or high‐risk. Package “survminer” was utilized to determine the optimal cutoff for risk‐grouping of different cohorts, respectively. The survival of each individual was displayed by risk curve and scatter plot. Differences in survival were identified by Kaplan–Meier survival analysis between low‐ and high‐risk group. Receiver operating characteristic (ROC) curve, along with its area under the curve (AUC), both were investigated respectively to make sure the accuracy of the risk‐predicting model by package “survivalROC.” Moreover, this loop would break when http://software.broadinstitute.org/gsea/index.jsp)Univariate and multivariate analyses of Cox regression were conducted to distinguish whether RS were an independent factor along with other clinical features including age, race, and gender. Furthermore, to reveal the function of PDEARGs, we made gene set enrichment analysis  of PDEARGs was calculated and the loci of these genes on chromosomes were visualized. The spearman correlation analysis was adopted to analyze the relationship between the tumor mutation burden (TMB) and the risk score. Data concerning the number of mutated genes in GBM samples were retrieved and the waterfall plot was created to describe the top 20 mutated genes in low‐ and high‐risk groups.2.62 (fold change) >1 and in the meantime p < 0.05. Pearson's correlation analysis were conducted to confirm co‐expression of DETFs and PDEARGs. Only correlation meets Pearson's correlation coefficient (R) >0.60 and p < 0.05 were adopted for subsequent analyses.To explore the DETFs, we further applied the “limma” packages in R to process TFs' expression profiles between 168 GBM tumor samples and five adjacent normal tissue samples. TFs were regarded as DETFs with its absolute value of log2.7https://cibersort.stanford.edu/). After removing samples without statistical significance, we constructed bar plots to reveal the distribution and classification of these cell subtypes in GBM patients within the both risk groups. Single‐sample gene‐set enrichment analysis (shorthand as “ssGSEA”) sooner was carried out aiming at ascertaining the immune function score per each sample based on their expression patterns by using 29 immune datasets, namely immune cell subtypes, immune‐related activities, and pathways.R > 0.20 and p < 0.05 and PDEARG‐immune function pairs with R > 0.48 and p value <0.05 were considered closely correlated.Immune cell, contributing to angiogenesis in patients with GBM, were identified by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm.2.8http://tide.dfci.harvard.edu/login/)A total of 7202 GBM samples and their profiling in terms of immune dysfunction and exclusion (TIDE), Dysfunction, DKK3, SLIT2, SERPINA5, Merck18, PLK2, and NRP1 were retrieved from the TIDE database , absolute value of R > 0.60 and p < 0.05 (in PDEARG‐DETF pairs), absolute value of R > 0.48 and p < 0.05 (in PDEARG‐immunity function pairs), absolute value of R > 0.50 and p < 0.05 (in PDEARG‐RPPA chip pairs), absolute value of R > 0.20 and p value <0.05 (in PDEARG‐immunity cell pairs) were accounted as pivotal genes in the regulatory network. Based on the abovementioned correlation pairs, a regulatory network composed of DETFs, PDEARGs, signaling pathways, immune cell types, immune functions, and RPPA chips was established, and the regulatory network was visualized by using Cytoscape 3.6.Gene set variation analysis (namely “GSVA”)2.10To predetermine the small molecular drugs which could attenuate or reverse the effect brought by identified PDEARGs and DETFs, the Connectivity map (CMap) databaseSimilarity of the query to each of the gene signatures were represented by connectivity score (CS) of each agent, ranging from −1 to 1, was calculated. Specifically, negative CS indicated that the drug may reverse input results. Subsequently, we identified small molecule drugs with negative CSs, which demonstrated the therapeutic potent for GBM.2.11n = 95) was divided into a train set (n = 60) and test set (n = 35) at a ratio of 3:2 randomly. We conducted Univariate LASSO regression, multiple Cox regression analysis, and Cox regression analysis to evaluate the clinical application value of PDEARGs in predicting the overall survival (OS) of the patients, and to rebuild a risk prediction model. Based on the median value of the risk score, all GBM patients in the train set were divided into low‐ and high‐risk groups, respectively. K–M curves, used for OS evaluation of the two groups were constructed to assess the accuracy of the PDEARG signature in the internal train group. We further verified the results in the internal test group.We further accessed the PDEARG signature in the independent validation cohort collected from our own institution. Only five GBM patients in the primary cohort were excluded due to loss to follow‐up. The whole cohort , anti‐COL6A1 (ab6588), and PDPN (ab10288) at 4°C for 12 h. Then, the sections were incubated using the secondary antibody at 37°C for 1 h. Next, we use 3,3‐diaminobenzidine solution to stain those sections for 3 min and counterstained using hematoxylin. Eventually, the slices were observed, analyzed, and photographed by microscope.2.15After dewaxing, the tissue sections were stained with Hematoxylin and Eosin solution . Then, sections were subjected to dehydrate. Finally, we observed their morphology, malignancy degree, and vascular content with microscope, captured images and conducted analysis when the GBM sections stained successfully.2.16https://gdc.cancer.gov/about‐data/publications/ATACseq‐AWG) and visualized the result through using R package of Gviz.Assay for Transposase‐Accessible Chromatin sequencing (ATAC‐seq) was used for studying the chromatin‐accessibility of hub genes.2.17https://www.ebi.ac.uk/gxa/sc/experiments/E‐GEOD‐84465/results.As single‐cell RNA sequencing (scRNA‐seq) assays continues to develop and data availability increases to expand, scRNA‐seq datasets have been widely used. Furthermore, the scRNA‐seq on 3589 cells in a cohort of four GBM patientsFirst, we generated a Single‐Cell‐Experiment object by loading 10× single‐cell raw matrix into R statistical programming software (v4.0.2) through Seurat package (v4.0.1). The matrix of data was then normalized, rescaled and filtered via quality control (QC). Second, the top 20 components of principal components analysis (PCA), as well as supplementary t‐SNE analysis, were conducted (perplexity = 50). We conduct clustering of similar single cell groups on the two‐dimensional (2D) t‐distributed stochastic neighbor embedding (tSNE) space with k‐means and by R package “stats.” Feature plot was utilized to visualize specific gene expressions from single‐cell data.2.18http://biocc.hrbmu.edu.cn/CancerSEA/). The threshold of the aforementioned genes related to each cancer functional status was established as a threshold value of |r| > 0.3 and a discrimination significance (p < 0.05).We further investigated the correlations of gene expression levels of the identified differentially expressed angiogenesis‐related genes with 14 functional states in multiple cancer types, especially in GBM, including apoptosis, angiogenesis, differentiation, cell cycle, DNA repair, DNA damage, EMT, inflammation, hypoxia, proliferation, invasion, metastasis, stemness, and quiescence, by utilizing the CancerSEA database (2.19www.cgga.org.cn) database. Because of incomplete information, 100 GBM patients were excluded from this study, and we only investigated the data of the remaining 289 GBM patients. Kaplan–Meier curves were constructed to validate the potential relationship between the OS of GBM patients and the expression level of the identified differentially expressed angiogenesis‐related genes.The data of 389 GBM patients were obtained from the CGGA  as follows: not significant RE ns; p < 0.05 RE *; p < 0.01 RE **, and p < 0.001 RE ***.All the data were visualized and analyzed using R software  involving packages “limma,” “clusterProfiler,” “msigdbr,” etc.  and test set . Second, to obtain PDEARGs that were closely correlated with prognosis of patients with GBM, univariate Cox regression analysis were conducted in the TCGA‐GBM train set. The results showed that 27 of these DEARGs were closely related to the overall survival (OS) of GBM patients, which were defined as PDEARGs  and the mortality rate in the high‐risk group was higher than that in the low‐risk group  and multivariate Cox regression analysis , p < 0.001) . Expression level difference of 12 PDEARGs between high‐ and low‐risk groups was demonstrated in box plots Figure . All setp Figure . The AUCp Figure . Additio) Figure .Moreover, the scatter plot and risk curve displayed the notable differences in survival status and risk score of GBM patients between low‐ and high‐risk groups Figure . The hea3.4R = −0.21, p = 0.009)  frequency of 12 genes was explored and PDPN showed the highest frequency of gene gain and loss Figure . The spe) Figure . Additio) Figure , which ip < 0.05), Macrophages M2 (p < 0.05), and Mast cells resting (p < 0.05) were indicated to be increased obviously in low‐risk group. Based on immune cell type fraction, the ssGSEA scores of immune functions in each sample were calculated , Dysfunction (p < 0.001), DKK3 (p < 0.001), SLIT2 (p < 0.001), SERPINA5 (p < 0.001), Merck18 (p < 0.01), PLK2 (p < 0.001) and NRP1 (p < 0.001)  effect and the greater the possibility of immune evasion. Violin plots revealed the significant difference in biological processes in terms of TIDE  Figure . Additio) Figure . The AUC) Figure . TIDE coIn specific, TIS is referred to as an 18‐gene signature , which was related to the response to ICIs in various cancers.3.6p value <0.05, and enrichment score <0). Within these small molecule drugs, phenazone, nortriptyline, foliosidine, trimethylcolchicinic acid, and loracarbef showed closely higher negative correlation, which exhibited a therapeutic potent against GBM  algorithm was adopted. The absolute value of signaling pathways in 173 GBM samples was quantified and displayed in heatmap plot Figure . HallmarM Figure .We further evaluated the accessible chromatin landscape  representing potential regulatory regions, likely involved in the mechanisms underlying key PDEARG‐driven GBM angiogenesis, using the ATAC‐seq profiles of GBM patients' biopsies from GEO cohort. Over the whole genome across samples, ANXA1, COL6A1 and PDPN were all in the presence of substantial changes in chromatin accessibility widely presenting Figure .3.7p < 0.001). We randomly divided the internal group into an internal train group and test group. Subsequently, LASSO regression was employed, and cross‐validation was performed in the internal training group , test set (p = 0.002), and all set (p < 0.001) . The risk prediction model could effectively differentiate between the low‐risk group and high‐risk group, and the PDEARG signature was highly stable and effective in different situations.The mRNA expression of the 3 PDEARGs in GBM patients was shown in Figure p Figure . The inip Figure . Surviva) Figure . Besidesp < 0.001) and multivariate Cox regression analysis .We conducted univariate and multivariate Cox regression analyses to identify prognosis‐related factors for GBM patients. Forest maps Figure  indicate3.8The basic information of 95 GBM patients collected from our center is summarized in Table To explore the correlation between ANXA1, COL6A1 and PDPN at protein level and the prognoses of these GBM patients, we conducted large cohort retrospective analysis with IHC assay. The protein expression levels of ANXA1, COL6A1, and PDPN were negatively correlated to the prognosis of GBM patients Figure . The K‐MIn addition, gender, age, Ki‐67 positive rate, HE staining score, and ANXA1, COL6A1, and PDPN staining positivity rates (PC), H‐S, immune‐related risk score (IRS), postoperative survival time , and survival status was summarized in Table 3.9R = 0.27, p < 0.01). Moreover, based on the operative specimens from five GBM patients , ANXA1 may promote metastasis  and inflammation  in GBM, which may lead to a poor prognosis in GBM patients. Further, it showed significantly strong positive correlations between ANXA1 and metastasis , DNA repair , hypoxia .ANXA1 expression was related to the 14 functional states in nine cancer types Figure . Then, wR = 0.24, p < 0.001). Furthermore, based on the operative specimens from five GBM patients , we identified significantly strong positive correlations between COL6A1 and hypoxia .COL6A1 expression was associated with the 14 functional states in 15 cancer types Figure . We nextR = 0.38, p < 0.001), EMT . Furthermore, based on the operative specimens from five GBM patients , we identified significantly strong positive correlations between PDPN and DNA repair , invasion , stemness , metastasis .PDPN expression was correlated with the 14 functional states in 10 cancer types Figure . We next3.10To further validate the prognostic value of the identified prognostic differentially expressed angiogenesis‐related genes for GBM, we downloaded and analyzed the sequencing data and clinical information of 389 GBM patients from the CGGA database. Because of missing information, a total of 290 eligible patients were ultimately enrolled in the prognostic analysis. Interestingly, consistent with our previous findings, GBM patients with high ITGA5, COL6A1, SERPINA5, NRP1, PLK2, ANXA1, and PDPN expression had worse survival outcomes, whereas GBM patients with low MAPK1 and HEY1 showed worse survival outcomes Figure .3.11n = 500) were selected to generate a cell‐to‐cell dissimilarity matrix. Subsequently, tSNE was conducted on the distance matrix to construct a feature plot of these single cells. Eventually, k‐means clustering was used on the feature plot, and 14 different cell types in distinct clusters were identified  were members of immune cell clusters, with the residual single cells annotated as the oligodendrocyte precursor cell (OPC) cluster , vascular cell clusters , and the oligodendrocyte cluster , and neuronal cell cluster . It is noteworthy that, the mature astrocyte cluster was the only cluster without any cell found in the malignant tissue. The interaction between these cell types in microenvironment of the tumor may contribute to tumor chemoresistance and progression.To characterize GBM infiltrating tumor cells, high‐depth scRNA‐seq data on a cohort containing four GBM patients  were collected and analyzed, namely BT S1, BT S2, BT S4, and BT S6. Almost all single cells collected from dissociated GBM tumor core attributed to the neoplastic section, instead of other neuronal, glial, vascular, and immune cell subtypes. The cellular identities of the resulting clusters were inferred through identifying significantly over‐expressed genes of the corresponding cell cluster Figure . Moreoved Figure . Importa3.12Specifically neoplastic clusters expressed high levels of the 3 PDEARGs  and key DETF (WWTR1). Therefore, scRNA‐seq revealed co‐localization of these key biomarkers in malignant cells, and indicated that the interaction between these PDEARGs and DETF may contribute to malignant behavior of GBM cells Figure . MoreoveWe also identified discrete differences of gene expression between malignant cells and myeloid cells. Therefore, we found more pro‐inflammatory genes were mainly expressed in within periphery tissue and more pro‐angiogenic or anti‐inflammatory gene markers were expressed in the tumor core Figure . For ins4Here, we employed a set of bioinformatics methods to construct an angiogenesis‐related risk prediction model for patients with GBM. Core genes and relevant upstream and downstream components were discovered to establish a network regulating angiogenesis in GBM. Notably, 3 PDEARGs were found to be intensively correlated to WWTR1 (a TF) and hallmark epithelial mesenchymal transition , the binding strength between WWTR1 and 3 PDEARGs was initially confirmed by ATAC‐seq analysis.Annexin A1 (ANXA1) is known for its role in immune response and inflammation.Collagen type VI alpha 1 (COL6A1) is a part of a family of collagen and is widely distributed in various tissues including skeletal muscles, skin and blood vessels.Podoplanin (PDPN) is known to be upregulated in tumor cells, tumor‐associated fibroblasts and inflammatory macrophages.In a word, pro‐angiogenesis and protumor effect of ANXA1, COL6A1 and PDPN are confirmed by previous and the present literature. Nevertheless, finding out how these three key genes interact with one another and whether transcriptional regulators mediating these PDEARGs in GBM angiogenesis turn to be seductive. To our knowledge, few studies have reported the potential impact of ANXA1, COL6A1, and PDPN on the prognosis of GBM patients. Using the risk score, related to degree of proliferation (Ki‐67) and angiogenesis (HE), and IHC to further validate protein expression levels of these genes in our GBM brain tissue samples, we confirmed that ANXA1, COL6A1 and PDPN are not only significantly upregulated in tumor tissues of high‐risk GBM patients, but are closely related to worsened prognosis, indicating their contributions to the pathogenesis of GBM. Furthermore, comprehensive analyses have shown that ANXA1, COL6A1 and PDPN appeared to be promising candidates as prognostic predictors and therapeutic drug targets.We identified that WWTR1 is closely correlated with the three PDEARGs, which has attracted our interest. WWTR1 is also known as TAZ, which is a transcriptional co‐activator with a PDZ binding motif. YAZ and its paralog the Yes‐associated protein (YAP) are both initially recognized as downstream effectors of the Hippo signaling pathways. In the context of various human tumors, TAZ/TAP have been activated and translocated into the nucleus to regulate target genes expression, enhancing malignant cell proliferation, maintaining a pro‐neoplasm microenvironment and promoting cancer stemness.The regulatory interaction between WWTR1 and 3 PDEARGs was initially confirmed by ATAC‐seq analysis in the present study. Furthermore, using scRNA‐seq data of 3588 cells in a cohort of 4 GBM patients, single infiltrating tumor cells within peripheral tumor core of GBM were clustered and characterized. Despite the fact that significant heterogeneity exists across neoplastic cells, we identified that infiltrating tumor cells share a consistent gene expression pattern between patients, indicating a common infiltration mechanism. As expected, we found that the value of 3 PDEARGs  and key DETF (WWTR1) were co‐expressed significantly higher in the tumor core than in the peripheral of GBM tissue, specifically in the neoplastic cells. Additionally, we identified that GBM infiltrating immune cells play an important role in promoting tumor cells' growth, survival, and dissemination through inhibition of inflammation, activation of angiogenesis and remodeling of ECM.Here, we applied function enrichment analysis in search of underlying downstream biological process related to PDEARGs. Among the hallmark events, epithelial mesenchymal transition (EMT) was highly correlated with PDEARGs. EMT enables solid tumor cells to acquire the potential to evade from primary tumor and disseminate through the body by equipping the cells with capabilities to undergo morphological transitions such as the loss of cell–cell adhesion, the loss of cell polarity and the gain of mesenchymal markers.Up to now, the hypothesis chain regarding the regulatory network in our study has been completed, that is, transcriptional regulator WWTR1 up‐regulates the expression level of 3 PDEARGs, which may promote the excretion of TGF‐β, finally resulting in EMT‐mediated angiogenesis in GBM.Additionally, on the immune cell level, we have found that regulatory T cell (Treg) has the strongest correlation coefficient with the three PDEARGs, indicating that upregulation of PDEARGs may promote the infiltration of Treg, mediating angiogenesis and undesirable outcomes in GBM patients. Abundant studies have confirmed that Treg enhances the establishment of VEGFA‐rich cancer microenvironment and promotes angiogenesis in ovarian cancer,Among candidate agents with high specificity identified in this study, Nortriptyline is a potent autophagy inhibitor and has been repurposed as a medication to treat brain cancers such as GBM owing to its ability to permeate brain–blood barrier (BBB) and tumor cells toxicity.5In the present study, we have constructed a regulatory network based on angiogenesis‐related gene signature and identified a signaling pathway, WWTR1 (TAZ)‐ANXA1, COL6A1 and PDPN‐EMT, which may provide new therapeutic strategies by utilizing the signaling axis in tumor angiogenesis we proposed for GBM patients.Zhiping Wan: Conceptualization (lead). Xiaokun Zuo: Conceptualization ; formal analysis ; methodology . Siqiao Wang: Formal analysis ; investigation ; writing – review and editing . Lei Zhou: Methodology . Xiaojing Wen: Data curation ; investigation . Ying Yao: Investigation ; methodology . Jiefang Song: Data curation . Juan Gu: Resources ; software ; visualization . Zhimin Wang: Data curation ; funding acquisition ; investigation . Ran Liu: Investigation ; writing – original draft ; writing – review and editing . Chun Luo: Data curation ; funding acquisition .This study was funded by Natural Science Foundation of China (grant No.82171832), Shanghai science and Technology Innovation Fund (grant No.20Z11900105).The authors have no conflict of interest.We state that our study was granted by the Ethics Committee of Tongji Hospital affiliated to Tongji University (No. SBKT‐2022‐053), and we conducted the research in strict accordance with its regulations.Data S1.Click here for additional data file.


This study aimed to explore potential risk factors for the occurrence of multiple organ dysfunction syndrome (MODS) in patients with multiple injuries by evaluating neutrophil‐to‐lymphocyte ratio (NLR)‐associated trauma severity.d‐dimer level, activated partial thromboplastin time (APTT), complicated shock within 24 h of admission (CS‐24h), length of stay, as well as prognostic outcome was systematically analyzed. According to MODS occurrence, patients were divided into a MODS group (n = 27) and a non‐MODS group (n = 68). The risk factors affecting patients with multiple injuries complicated by MODS were identified using univariate and multivariate logistic regression analyses. Candidate risk factors were further analyzed using receiver operating characteristic (ROC) curves.This retrospective case–control study included 95 patients with multiple injuries, who were admitted to our hospital (between January 2018 and December 2020). Clinical data including gender, age, underlying disease, number of injury sites (NIS), injury severity score (ISS), hemoglobin level within 24 h of admission (HL‐24h), neutrophil count (NC), white blood cell count, platelet count (PC), NLR, d‐dimer level, CS‐24h, NLR, NC, prognostic outcome, and other indicators (p < .05). Multivariate logistic regression analysis showed that d‐dimer levels within 24 h of admission and ISS, NLR, and CS‐24h were significantly associated with multiple injuries complicated by MODS. Compared with the non‐MODS controls, the NLR in the MODS group showed a much higher level and tended to rise with the increase in ISS score, indicating a significant intergroup difference (p < .05). The ROC curve analysis results suggested that the NLR had good sensitivity and specificity for predicting the prognosis of patients with MODS with multiple injuries.Univariate analysis revealed a significant difference between the MODS and non‐MODA groups in terms of NIS, ISS, HL‐24h, PC, APTT, d‐dimer level, ISS, NLR, and CS‐24h are important risk factors for MODS in patients with multiple injuries. Notably, NLR expression may be a good indicator of injury severity and predictor of the occurrence of MODS in patients with multiple injuries. Therefore, assessment of injury severity and coagulation function, active resuscitation, as well as prevention of infection should be emphasized during treatment of multiple injuries, to reduce and prevent the risk of MODS in patients with multiple injuries. Therefore, we retrospectively reviewed the clinical data of patients with multiple injuries treated at our hospital between January 2018 and December 2021. Risk factors for MODS in patients with multiple injuries were systematically analyzed. Notably, the possibility of using the NLR to predict MODS occurrence in patients with multiple injuries was also explored in detail.22.1n = 39) and a non‐MODS group (n = 125), respectively. The MODS group consisted of 25 males and six females (average age: 45.3 ± 3.56 years). The non‐MODS group included 50 males and 14 females (average age: 46.3 ± 2.75 years). The injury types included 57 cases of car accident injury , 22 cases of fall from height injury , and 12 cases of knife injury . The injury sites included 65 cases of cranial injury, 37 of chest injury, 24 of hepatic blunt trauma, 47 of spinal fractures, and 37 of pelvic fractures. Simultaneously, 50 healthy people who underwent physical examinations at our hospital health center were selected as the control group, according to the patients' basic information such as sex and age.Data from 95 patients with multiple injuries who were admitted to the emergency department of our hospital (between January 2018 and December 2020) were retrospectively analyzed. Of all patients, 75 were males, aged 11–74 years (average: 47.2 ± 4.56 years), and 20 were females, aged 9–67 years (average: 39.7 ± 3.74 years). According to the occurrence of MODS after injury, patients were divided into a MODS group (p > .05).As shown in Table 2.2Inclusion criteria were as follows: (1) after examination, all diagnosis met the criteria of multiple injuries2.32/FiO2) is <300 mmHg (PaO2/FiO2 < 300 mmHg). (3) Diagnosis of central nervous system dysfunction: (a) apathy, restlessness, lethargy, shallow, and deep coma; (b) Glasgow Coma Score ≤ 14; a diagnosis can be made if one of items (a and b) is available. (4) Diagnostic criteria for coagulation dysfunction: (a) platelet count (PC) < 100 × 109/L; (b) prolonged or shortened activated partial thromboplastin time (APTT), coagulation time, and prothrombin time; 3P test was positive; a diagnosis could be made if one of items (a and b) was available. (5) Diagnosis of liver dysfunction: (a) total bilirubin > 20.5 μmol/L; (b) blood albumin < 28 g/L; a diagnosis can be made if one of items (a and b) is available. (6) Diagnostic criteria for renal dysfunction: (a) serum creatinine > 123.76 μmol/L; (b) urine volume < 500 mL/24 h; a diagnosis can be made if one of items (a and b) is available. (7) Diagnostic criteria for gastrointestinal dysfunction: (a) weakened or absent intestinal sounds; (b) positive hematemesis, black stool, and fecal occult blood test; a diagnosis can be made if one of the items (a and b) is available.Diagnostic criteria for multiple injuries2.4Exclusion criteria were as follows: (1) no clear medical history; (2) the presence of chronic cardiopulmonary encephalopathy, liver disease, or tumor disease before the injury; (3) concomitant mental disorders; and (4) death occurring within 7 days after injury.2.5d‐dimer level, APTT, presence of complicated shock within 24 h of admission (CS‐24h), length of stay, and prognostic outcome. In addition, identified risk factors were further analyzed by receiver operating characteristic (ROC) curves to determine their diagnostic potential and the optimal threshold for predicting complicated MODS. The NLR levels between the different groups in the first 7 days after admission were also compared. Spearman's correlation analysis was conducted to determine the correlation between NLR and ISS scores.The patients included in the study were divided into MODS and non‐MODS groups according to the MODS criteria. Univariate and multivariate analyses were subsequently performed on the following clinical indicators: sex, age, presence of underlying diseases, number of injury sites, ISS, hemoglobin level within 24 h of admission (HL‐24h), NC, white blood cell (WBC) count, PC, NLR, 2.6t test with at least two independent samples. Non‐normally distributed measurement data are expressed as M  using a nonparametric test (Wilconxon rank‐sum test). Enumeration data were expressed as the number of cases using the chi‐square test. Binary logistic regression analysis was used for the multivariate analysis. ROC curve analysis was applied to discriminate risk factors for predicting complicated MODS in patients with multiple injuries, and the maximum value of the Youden index (sensitivity + 1 − specificity) was set as the best threshold. The diagnostic value was evaluated using the area under the curve (AUC). In general, diagnostic value was considered to be low , or high (AUC > 0.9). Correlation analysis was performed using the Spearman's rank test. The NLR value on Day 2 after admission in patients with multiple injuries was set as the x axis and the corresponding ISS score was set as the y axis. ROC curves were produced for NLR evaluation of prognosis and AUCs were calculated with 95% confidence interval (CI). Two‐sided values (p <  0.05) were considered significant.Statistical software SPSS  and the SPSSAU project (ver. 22.0) were used for the statistical analysis in this study. All measurement data are presented as mean ± SD using Student's 33.1As illustrated in Figure 3.2d‐dimer level, presence of CS‐24h, NLR, NC, prognostic outcome, and other indicators (p < .05). In contrast, no statistically significant differences were observed between the two groups in terms of sex, age, presence of underlying diseases, WBC count within 24 h of admission, length of hospital stay, or other indicators (p > .05).In the univariate analysis, as shown in Table 3.3d‐dimer level, presence of CS‐24h, NLR, NC, and prognostic outcome (p < .05) were included as independent variables in the multivariate logistic regression analysis, whereas the occurrence of MODS was included as a dependent variable. Logistic regression analysis results showed that the d‐dimer level within 24 h of admission, ISS score, NLR, and presence of CS‐24h were all significantly associated with multiple injuries complicated by MODS (p < .05).According to the univariate analysis results, as shown in Table 3.4d‐dimer level, and NLR in patients with multiple injuries complicated by MODS. For ISS, the AUC of the ROC was AZ = 0.726 (95% CI: 0.667–0.784), which is considered a moderate diagnostic value for MODS prediction in patients with multiple injuries. For d‐dimer, the AUC of the ROC was AZ = 0.638 (95% CI: 0.571–0.706), which was evaluated as having low diagnostic value for MODS prediction in patients with multiple injuries. For the NLR, the AUC of the ROC was AZ = 0.735 (95% CI: 0.671–0.795), which was evaluated as a moderate diagnostic value for MODS prediction in patients with multiple injuries. As shown in Figure d‐dimer was 21.84 mg/L with a sensitivity of 58.5% and a specificity of 66.3%. Multiple injuries complicated by MODS were set as predictive targets.ROC curve analysis was performed to analyze ISS, 4d‐dimer level, shock within 24 h of admission, NLR, NC, and prognosis.In terms of whether multiple injuries are associated with MODS, previous studies have proposed that in time correction of shock, ISS, acute physiological, and chronic health assessment II scores are independent risk factors for MODS predictions in patients with multiple injuries.An imbalance between the inflammatory and anti‐inflammatory responses is considered the underlying cause of MODS.NLR refers to the ratio of the absolute value of neutrophils to lymphocytes in the peripheral blood. Zahorec et al.In addition, the NLR has been applied to predict poor prognosis in patients undergoing abdominal surgery, severe trauma, and acute renal injury.Traumatic shock is not only one of the major early complications in multiple trauma patients, but also one of the main causes of early death.p < .05) Multivariate analysis indicated that ISS was an independent risk factor for MODS in patients with multiple injuries. In addition, ROC curve analysis showed that the ISS had moderate diagnostic value in predicting multiple injuries complicated by MODS.The ISS is the most commonly used index for evaluating the severity of injury in patients with multiple injuries. Studies have shown that ISS can not only assess the severity of multiple injuries but also predict the prognosis of multiple injuries.d‐dimer is a fibrin degradation product produced by cross‐linking fibrin in the presence of fibrinolytic enzymes. Increased d‐dimer levels cause an increase in secondary fibrinolytic activity and can therefore be used as a marker to indicate hypercoagulability and fibrinolytic function in vivo.d‐dimer is not only associated with the severity of multiple injuries but also with prognosis.d‐dimer level is also a risk factor for multiple injuries complicated by MODS. However, the diagnostic value, sensitivity, and specificity of d‐dimer in predicting multiple injuries in MODS remain low.Coagulation dysfunction plays an important role in MODS.d‐dimer levels > 21.83 mg/L within 24 h of admission; second, we should actively correct shock, supplement coagulation factors, correct coagulation disorders, as well as prevent and control infection.During the treatment of multiple injuries, to prevent and reduce the risk of MODS complications in patients with multiple injuries, first, assess the condition of patients with multiple injuries comprehensively, especially ISS > 32 and 5d‐dimer level, ISS, NLR, and CS‐24h are important risk factors for MODS in patients with multiple injuries. Notably, NLR expression may be a good indicator of injury severity and predictor of the occurrence of MODS in patients with multiple injuries. Therefore, assessment of injury severity and coagulation function, active resuscitation, as well as prevention of infection should be emphasized during treatment of multiple injuries, to reduce and prevent the risk of MODS in patients with multiple injuries. This study also has some limitations, such as its relatively small sample size, which may have statistical bias and requires a larger sample size for further confirmation.Collectively, Qiu‐Hong Fu, Xing Liu, and Yong‐Ming He conceived the idea and conceptualized the study. Si‐Yi Zhong and Qiu‐Hong Fu collected the data. Yong‐Ming He and Si‐Yi Zhong analyzed the data. Xing Liu and Qiu‐Hong Fu drafted the manuscript, then Xing Liu and Si‐Yi Zhong reviewed the manuscript. All authors read and approved the final draft.The authors declare no conflict of interest.The study was conducted in accordance with the Declaration of Helsinki (as was revised in 2013). The study was approved by Ethics Committee of the Shenzhen Longhua District Center Hospital (No.2023‐045‐01). Written informed consent was obtained from all participants.