N = 137) showed that an adapted brief version of prolonged exposure (PE) to the fearful memory of the event and situations, provided in the immediate aftermath after trauma (<72 h after a traumatic event), was effective in reducing early PTSD symptoms in rape victims. The aims of the present study were to adapt the brief PE protocol to a Swedish context and investigate its feasibility and delivery in 10 executive patients recruited at the Emergency Clinic for Rape Victims in Stockholm. Ten participants were provided with three sessions of early PE with overall successful results in terms of session attendance, home-work compliance, and also symptom reduction of PTSD and depressive symptoms. However, only a fraction of the screened patients at the Emergency Clinic (5.2%) were eligible to be included in the study, where the majority (40%) were excluded due to the time criteria of 72 h. In this article, we will present detailed results of the intervention and elaborate on how to increase feasibility of preventive interventions for rape victims. In the current form, providing PE with the strict time criteria was not feasible in the clinical setting that constitutes the Emergency Department for rape.Rape is the most common trauma leading to post-traumatic stress disorder (PTSD) among women, with a conditioned prevalence of up to 50%. PTSD is considered to be a lethal condition associated with increased risk of suicide, drug- and alcohol dependence, neurological- and vascular problems, as well as sick leave. Given the scope of this problem, novel and swiftly delivered interventions for this large vulnerable population are clearly warranted. One previous trial conducted in the United States ( Sexual assault, including sexual abuse, rape, intimate partner, and sexual violence, is a global public health concern affecting an estimated 12% of women around the world . In a reAmong women who have been exposed to rape, as much as 19–50% develop PTSD . Rape maN = 124) indicated that this brief video intervention could significantly reduce both marijuana misuse at 6 weeks follow-up (N = 140) by the same research group found that women with a pre-assault history of high marijuana use who were randomized to the video intervention had lower marijuana misuse scores from baseline to the 6-months follow-up (N = 164) where women randomized to the video condition had fewer anxiety symptoms at the 2-month follow-up but no main effects were found on PTSD symptoms (N = 154) compared to treatment as usual and an active control condition (pleasant imagery and relaxation instruction). Results showed that the video intervention had some efficacy in reducing substance use in rape victims with a history of sexual assault . Results showed that PE was especially effective in reducing symptoms of post-traumatic stress among the subsample of rape victims  with a medium effect size (d = 0.7) at 12 weeks. A more recent study did not replicate these promising findings when comparing the three session protocol against one session and assessment only . The clinic is one of the largest sexual assault centers in Europe with an intake of about 800 patients each year. Medical care, forensic examination, and psychological help are offered to patients in Stockholm county over 13 years who seek help within 1 month after the rape. Neither medical nor psychological help render any fee for the patient but is solely financed by the Stockholm county. The study was approved by the Regional Ethical Review Board in Stockholm, Sweden (ID:2016/2194–31).Eligible participants were patients of at least 18 years of age attending at the Emergency Clinic for Rape Victims at Stockholm South Hospital, Sweden, within 72 h  after experiencing a traumatic event meeting the DSM-5 criterion A for PTSD  and who had memories from the event. Exclusion criteria were (i) ongoing suicidal ideation or attempted suicide within the last 2 months, (ii) ongoing self-harm behavior, (iii) ongoing intoxication, (iv) other serious psychiatric comorbidity (ongoing psychotic symptoms or manic episode), (v) low cognitive capacity, (vi) not fluent in Swedish, and (vii) subjected to ongoing violence or threat.Recruitment was conducted in two steps. Each morning, a clinical psychologist or licensed psychotherapist did a pre-selection screening in the medical records of newly arrived patients. Eligible patients were asked to participate in the study at a routine appointment with either a clinical psychologist or a licensed psychotherapist. Patients who did not come to the routine appointment were instead reached by telephone. Patients signed informed consent at the assessment appointment and also completed the baseline battery consisting of Immediate Stress Reaction Checklist ISRC;  and BeckThe Posttraumatic Stress Disorder Checklist PCL-5; , a 20-itIn addition to the outcome measures above, we exploratory investigated if it is possible for rape victims to daily record the number of intrusive memories during treatment. This was done using a daily pen and paper diary where the participant was instructed to tick a box for the day and corresponding time period (morning/afternoon/evening/night) or indicate zero in the absence of intrusive memories. The diary was translated into Swedish by the first author and has been used in previous trials testing early provided psychological interventions .Prolonged exposure is based on The PE treatment protocol was translated to Swedish by the first author in a previous trial after it was generously made available by the study authors of the The participants were provided with the first PE session within 72 h after the rape. During the first PE session, participants were provided with a rationale of the PE treatment and the function of avoidance behaviors as maintaining symptoms of post-traumatic stress. Subsequently imaginal exposure, where the patient was instructed to revisit the memory of the rape for 20–30 min  and recounting the rape in present tense, was conducted together with the therapist. In order to address erroneous trauma-related cognitions, open-ended questions were used after the imaginal exposure. A voice recorder was used to record the imaginal exposure on which the patient was instructed to listen to the recording each day as homework. A technique to decrease arousal symptoms in the patient’s daily life, breathing retraining, was also taught to the patient. Subsequently, two additional 60-min sessions were provided to the participants. The aim of these two sessions was to review the homework assignments and conduct additional therapist-led imaginal exposure.A total of 191 patients underwent the pre-selection screening for eligibility of which 118 (61.7%) were immediately excluded due to the following reasons: >72 h had passed since the rape , not fluent in Swedish , <18 years of age , and male gender .Seventy-three patients remained for the next phase of screening, using the digital medical records from the forensic examination. Nineteen patients  were not able to schedule an appointment with the therapist within the stipulated time frame for inclusion . Another nine patients  could not be scheduled within 72 h due to lack of available appointments in either the clinical psychologist or psychotherapists schedule. Ten additional patients  were excluded because they had no memory of the rape, and two patients (1%) were too physically ill and needed somatic care. Fourteen patients  had other serious psychiatric comorbidity and were thus excluded, and seven additional patients  did not want to be reached by the clinic personnel after the forensic examination. A total of 12 patients  were asked to participate in the study of which two patients declined further participation in the study.Mean time from the traumatic event (rape) to the start of PE was 52.1 h (SD = 11.1). Baseline characteristics for the included participants are presented in Three of the included participants dropped out from the study after the second PE session. No reason for dropping out was given in two cases. The third participant stated being exposed to another traumatic event as a reason. Seven participants were provided with the full treatment protocol. Adherence to the treatment protocol was excellent, and all PE sessions were provided according to treatment protocol. Homework compliance was high in these seven participants, ranging from 85% completion between session 2 and 3 and 100% between session 1 and 2.Data attrition was high at the 2-month follow-up; only six of the ten (60%) enrolled participants completed the CAPS-5 interview with the independent clinician. Data attrition was also high on the daily intrusion diary from session 1 to 3 where only three of ten (30%) participants had daily registrations the full treatment period.The mean score on the PCL-5 was 49.9 points (SD = 9.5) at post-treatment (end of session 3) which had reduced to 38.8 points (SD = 14.5) at the 2-month follow-up. The corresponding number for the BDI was 21.6 points (SD = 7.1) at post-treatment and 18.3 points (SD = 9.9) at the 2-month follow-up.The mean score on the CAPS-5 was 28.3 points (SD = 13.7) at the 2-month follow-up where three participants also received a diagnosis of PTSD and were offered standard PE treatment for PTSD. The mean ISI score was 12 points (SD = 4.48), the mean MSPSS point was 68 points (SD = 11.11), and the mean WHODAS score was 19.5 points (SD = 3.70) at the 2-month follow-up. No serious adverse events that could be attributed to the treatment were reported during the treatment or at the 2-month follow-up.In this study, we hypothesized that a brief PE intervention provided in the early aftermath of rape would be a feasible and acceptable early psychological treatment for PTSD in regular Swedish care. Our hypotheses were partly supported: Of the 10 included individuals, seven participants completed the full PE protocol, and these individuals had high degree of treatment adherence. Additionally, results indicated clinically meaningful reductions in PTSD symptoms in these individuals. However, it is important to point out that the treated sample consisted of only a fraction (5.2%) of the total intake at the clinic during the study period.As much as 40% of the screened sample was excluded due to the time criteria of 72 h. One well-reported factor in the literature is that only a small proportion of rape victims seek help from mental health professionals in the early aftermath of trauma  and therTen percent of the screened sample was not fluent in Swedish and therefore excluded. Translator assisted trauma focused CBT for PTSD has proven to be effective , and oneAbout one-quarter of the screened sample was excluded due to the age criteria (at least 18 years old). Previous research indicate female adolescents as victims of sexual assault in a higher, disproportionately degree . AdolescThe Emergency Clinic for Rape Victims did only provide care to female rape victims during the main execution phase of this study which unfortunately excluded an additional 10% of the screened sample. About 4–10% of all reported rapes include a male victim , and stuTwelve (6.2%) patients were finally asked to participate in the study, and two of these individuals declined participation. Of the 10 included participants, three participants dropped out of treatment. Homework assignment completion rate was high among the seven participants that underwent the full treatment, between 85 and 100%. One important challenge for future research is how to overcome the degree of data loss found in this study (40% of the included participants did not show up at the 2-month follow-up assessment). As previously discussed, digital innovations  may provide solutions to some of these problems. Several studies have shown promising results of smart phone assessments for several mental health problems  as well The study comes with limitations. One major limitation is that the lack of a control group to control for spontaneous fluctuations. Although the treated participants on average had a clinical meaningful decline in PTSD symptoms, this symptom reduction may as well be explained by natural recovery. However, it is important to stress that the aim of this study was merely to assess feasibility and delivery of the PE protocol. Future studies should consequently use a parallel group design to investigate efficacy of this treatment with extended time criteria. Another limitation in this study was that the independent assessor was not blinded to the aim study hypothesis and time-point, and thus, the mean scores on the CAPS-5 should be interpreted with caution. We also used a “supervision on demand” approach which means that we did not make a systematic control for treatment fidelity of the intervention. Future research may want to investigate this issue in more detail and to investigate possible therapist drifts when providing early PE interventions. The low rate of eligible patients to recruit also poses a major limitation. Comparing these figures with previous studies on early interventions after trauma shows that a low rate of eligibility and recruitment is unfortunately common stressing the need for the field overall of finding ways around it .To summarize, early provided PE seems to be an acceptable and deliverable intervention for rape victims. However, of the 191 screened patients, we were only able to include 10 participants (5.2% of the screened sample) in the study. The main reason for this was an inability to recruit participants in the stipulated time window for this study of 72 h. We suggest an alternative approach which includes offering the first intervention session when the patients present irrespective of time since rape and also to develop remotely delivered interventions. Complementary interventions, delivered through other formats , that are easily accessible – irrespective of gender, language barriers, and geographical distances – are clearly needed in order to swiftly reach the millions of rape victims around the globe.The raw data supporting the conclusions of this article will be made available by the authors upon request given that the request comply with Swedish and EU laws regulating protection of identifiable data.The studies involving human participants were reviewed and approved by the Regional Ethical Review Board in Stockholm, Sweden (ID:2016/2194–31). The patients/participants provided their written informed consent to participate in this study.KL and LN conducted the data collection. MB and EA performed the data analysis and interpretation, and first draft of the article. All authors contributed to the study design and writing of the article, and read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The purpose of the present review article is, therefore, to describe the various stages of exogenous CoQ10 metabolism, from its first ingestion, stomach transit, absorption from the small intestine into the lymphatic system, transport in blood, and access into cells. In particular, the importance of CoQ10 crystal dispersion in the initial formulation is emphasised, the absence of which reduces bioavailability by 75%. In addition, evidence comparing the relative bioavailability and efficacy of ubiquinone and ubiquinol forms of CoQ10 has been reviewed.A lack of understanding of the processes determining the absorption and subsequent metabolism of coenzyme Q CoQ10 has an essential role supplying all cells with energy [10 shuttles electrons from complexes I and II to complex III of the mitochondrial respiratory chain. As such, it is an energy transfer molecule in the electron transport chain, which produces ATP chemical energy. CoQ10 has an important role as an antioxidant that protects lipid-soluble cell membranes [10 is a major lipid-soluble antioxidant, protecting cell membranes and circulating lipoproteins from oxidative damage by free radicals [10 is the only lipid-soluble antioxidant manufactured within the body; in addition to preventing lipid peroxidation, CoQ10 is able to regenerate vitamins C and E back to their active fully reduced forms. The antioxidant function of CoQ10 is attributed to its reduced ubiquinol form, which must be constantly regenerated from its ubiquinone oxidised form. Moreover, a gene expression profiling study demonstrated that CoQ10 influences the expression of several hundred human genes [10 has been shown to reduce the activity of inflammatory markers, suggesting that CoQ10 may have anti-inflammatory action via gene expression modification, most notably in elderly individuals [Coenzyme Qan genes . In cellividuals ,4.10 is produced within the body. Some CoQ10 is ingested from food , requiring a number of amino acids, vitamins, and trace element precursors and cofactors, deficiency of any of which can adversely affect normal CoQ10 production [10 therefore provides a mechanism to maintain adequate levels within the body. As people mature into adulthood and begin to increase in age, the ability of the body to synthesise its own CoQereafter . In addi disease . The synoduction . Nutriti10 has led some manufacturers to make incorrect claims relating to the bioavailability of their CoQ10 supplements; this in turn is of potential relevance to the use of such supplements in clinical studies. The purpose of this review article is, therefore, to describe the bioavailability process of supplemental CoQ10, from its first ingestion all the way through to its uptake and utilisation within the cells, in order to clarify these misleading marketing claims.A lack of understanding of the processes determining the bioavailability of CoQ10 was originally purified from bovine heart tissue in the 1950s by Professor Frederick Crane [10 is now manufactured via a yeast fermentation method. The Kaneka corporation of Japan is now a major supplier of CoQ10 (produced via a fermentation process) to supplement manufacturers. The manufacturers then incorporate the CoQ10 into their respective products. CoQ10 produced via yeast fermentation produces the trans-form (as opposed to the cis-form) of CoQ10. This form is chemically identical to the form of CoQ10 that is bio-synthesised within the body. The CoQ10 raw material comes in the structural form of polymorphic crystals [10 in polymorphic crystalline forms will not be absorbed in the gastro-intestinal tract. CoQ10 can be absorbed only as individual molecules, as described in 10 crystals must therefore be dissociated first into individual CoQ10 molecules. These CoQ10 molecules should remain fully dissociated throughout the shelf life of the CoQ10 preparation. Not all CoQ10 manufacturers have demonstrated an ability to achieve this dissociation of the crystals to single molecules [Although CoQ10 exists within the body in the oxidised form (ubiquinone) and the reduced form (ubiquinol); these two redox forms are continually inter-converted within cells as part of the normal function of CoQ10. In the blood, CoQ10 is transported in ubiquinol form (bound to low-density lipoprotein- and very low-density lipoprotein- cholesterol), irrespective of the initial dietary form (ubiquinone or ubiquinol) [10 into intestinal absorption cells was thought to be enhanced by the reduction of ubiquinone to ubiquinol [10 in the ubiquinol form will facilitate the absorption process. However, given that ubiquinol is, by its very nature, inherently unstable and will oxidise to ubiquinone, until relatively recently, CoQ10 supplements could only be produced using the ubiquinone form. Recent technical improvements in encapsulation methods have made the production of CoQ10 supplements in the ubiquinol form possible. On the basis of the above rationale, some manufacturers of reduced CoQ10 supplements have claimed that the reduced form of CoQ10 is more readily absorbed from the gastro-intestinal tract, even though recent absorption and bio-availability studies have brought the issue of ubiquinol’s claimed superior absorption into question, as explained in the sections that follow. CoQiquinol) . Becausebiquinol , some st10 supplements differing in formulation  were administered in a single 100 mg dose to the same series of 14 healthy individuals, using a crossover/washout protocol. Bioavailability of the different formulations was quantified as the area under the curve (AUC) at 48 h. Of particular note in this regard is the clinical study by Lopez-Lluch et al. . The bio10 studies, the AUC of the plasma CoQ10 concentration is used to report bioavailability because the AUC represents the proportion of the dose that has entered the blood circulation.In CoQ10 concentration following the initial administration of a dose and prior to any subsequent administration.Cmax is the peak plasma CoQ10 in identical ubiquinone form, in a soy carrier oil with identical excipient content and capsule specification; sample 01  had been subject to a patented thermal crystal dispersion process whilst sample 02 (from the same manufacturer) had not undergone this procedure. The respective mean AUC and Cmax values in the Lopez-Lluch study were 28.0 mg/L/48 h and 1.07 mg/L for sample 01, and 6.89 mg/L/48 h and 0.33 mg/L for sample 2. Failure to subject crystalline CoQ10 to crystal dispersion therefore reduced bioavailability by approximately 75%. The first point of note is the difference in bioavailability between samples 01 and 02 of the study. Both samples contained 100 mg CoQ10. For the ubiquinol form of CoQ10 (sample 05), the AUC and Cmax values were 14.8 mg/L/48 h and 0.49 mg/L, respectively. Thus, the AUC for CoQ10 in ubiquinol form was approximately twice of that for ubiquinone, which had not been subjected to thermal crystal dispersion (sample 02), but was only 52% of that for ubiquinone that had been subjected to thermal crystal dispersion (sample 01). It should be noted that it is not possible to subject ubiquinol to the same crystal dispersion process as that described for sample 01; this is because of the differential stability of ubiquinone and ubiquinol. These data therefore demonstrate (i) the importance of CoQ10 crystal dispersion, the absence of which reduces bioavailability by some 75%; (ii) the fact that the relative bioavailability of ubiquinone and ubiquinol forms of CoQ10 depends on CoQ10 crystal dispersion status and carrier oil/excipient composition. A second point relates to the relative bioavailability of the ubiquinone and ubiquinol forms of CoQ10 formulations over a 6-week period, ubiquinone (150 mg), ubiquinol (150 mg), and a liposome ubiquinone formulation. There was a significant inter-subject variation in the baseline plasma CoQ10 levels, which persisted after administration of the test formulations. In addition, it was noted that the bioavailability of CoQ10 varied significantly between individuals, irrespective of the CoQ10 form (ubiquinone/ubiquinol) administered. There was no significant difference in plasma CoQ10 levels following administration of ubiquinone or ubiquinol formulations. The study therefore concluded that intestinal absorption of CoQ10 is highly variable among individuals, and is independent of the form of CoQ10 (ubiquinone/ubiquinol) administered. This is because the body is limited in how much CoQ10 it can absorb at a given time, and thus if an individual takes several 100 mg capsules together, some of the CoQ10 tends to be wasted [Several previous clinical studies have reported no significant difference in the relative bioavailability of ubiquinone and ubiquinol supplements. Thus, a cross-over study by Vitetta et al.  in 11 heiquinone 0 mg, ubi10 levels (determined as Cmax and AUC).Similarly in a 2002 study by Miles et al. , nine he10 levels compared to ubiquinone but did not significantly increase ubiquinol levels compared to ubiquinone. In a 2014 study by Langsjoen and Langsjoen [10 levels with ubiquinol. Langsjoen did not report the increases in plasma ubiquinol levels. It should be noted that in all of these studies, the apparently superior bioavailabilty of ubiquinol was determined against supplemental ubiquinone samples that had not been subject to thermal dispersion, as described above.In contrast to the above, several studies have reported significantly improved bioavailability of ubiquinol versus ubiquinone supplements, particularly in older subjects. For example, a 2009 study by Evans et al.  supplemeangsjoen , supplem10 bioavailability using a variety of agents; examples include polyethylene glycol [A number of studies have been carried out with the objective of improving CoQe glycol , phosphoe glycol , polyoxae glycol , and hyde glycol . Howevere glycol  above.In summary, on the basis of the evidence reviewed above, the concept of the superior bioavailability of supplemental ubiquinol compared to ubiquinone would appear to be mistaken. This concept, widely disseminated on the Internet, seems in part to have originated from an inappropriate comparison between bioavailability data from studies by Shults et al. in 1998  and Hoso10 is a lipid soluble substance; therefore, the highest quality CoQ10 preparations have the CoQ10 dissolved in a well-suited carrier lipid . Typically, they are encapsulated in bovine gelatine capsules. The capsule material does not seem to be an important factor for absorption. The gelatine capsules readily dissolve within minutes inside the stomach and release the oil-dissolved CoQ10. The stomach transit time varies among individuals and varies according to the type of food eaten. The complete transit of the CoQ10 from the stomach through the small intestines, and the lymph to the blood typically takes between 5 and 8 h [10 enters the duodenum as part of the chyme. Although the CoQ10 is in transit from the stomach into the duodenum, any CoQ10 in the reduced form, the ubiquinol form, will be oxidised to the ubiquinone form; in studies of CoQ10 absorption under conditions that simulate gastric conditions, this process usually takes about 90 min . 10 is a consequence of its inherent chemical structure—technically, it is due to the presence of an isoprenoid chain within the CoQ10 molecule. CoQ10 is not at all water soluble, and, contrary to some manufacturers’ claims, cannot be made water-soluble [10 molecule in an attempt to increase water solubility means that the molecule is no longer CoQ10. Similarly, the presence of two additional hydrogen atoms within the chemical structure of ubiquinol (compared to ubiquinone) has a negligible effect on potential water solubility [The lipid solubility of CoQ-soluble . Thus, alubility .10 molecules are subject to the process of micellisation, the formation of mixed micelles, through which various lipid-soluble substances, such as monoglycerides, fatty acids, and fat-soluble vitamins, are prepared for intestinal absorption. Bile from the gall bladder is secreted into the duodenum. Substances present in the bile interact with CoQ10 molecules to form characteristic micellular spherical structures with a diameter of up to 20 nanometers. Each individual micelle may contain as many as thousands of CoQ10 molecules. Micelles are constantly breaking apart and then reforming. The micelles are small enough to diffuse between intestinal villi before they break apart. In this way, the micelles are transporting the CoQ10 molecules to the surface of the intestinal absorption cells preliminary to absorption. It is important to note that the micelles themselves are not absorbed; only single CoQ10 molecules that have been released from micelles can be absorbed by intestinal absorption cells [In the duodenum, CoQon cells .10 via a process of “passive facilitated diffusion”. This process is described as passive because the process does not require energy. Carrier molecules facilitate the diffusion of the CoQ10 molecules through the cell membranes and into the enterocyte cells. Researchers have not definitively identified the carrier molecules for the CoQ10; however, some researchers have suggested the cholesterol transporter NPC1L1 (Niemann-Pick C1 Like 1) as a possible candidate [The enterocyte cells that form the lining of the small intestines’ villi absorb CoQandidate ,29. 10 absorption, Singh et al. [10 levels in a group of 60 healthy adults. In the Singh study, a 200 mg CoQ10 dose yielded a larger increase in serum levels than a 100 mg dose. However, divided dosages of 2 × 100mg of CoQ10 yielded a larger increase in serum levels than a single 200 mg dose. The Singh data show that the human digestive system has a finite capacity to absorb CoQ10 in a single dose. This discovery of a limited capacity to absorb CoQ10 follows logically from the concept of the requirement for a CoQ10 transporter for access into enterocytes as outlined above.In an attempt to understand CoQh et al.  investig10 molecules are incorporated into chylomicrons. Chylomicrons are lipoprotein-based particles that carry lipid-soluble substances and CoQ10 in the lymph and the blood circulation. From the basal surface of the enterocytes, chylomicrons are released by the active process of exocytosis. Chylomicrons are substances that are too large to enter the blood circulation via the process of capillary absorption within villi. For this reason, the chylomicrons transit into the lymph circulation within villi. The chylomicrons enter the distal abdominal lymph duct. The chylomicrons with their CoQ10 content then eventually enter the subclavian vein through the proximal abdominal and thoracic lymph ducts and continue in the blood circulation. Once inside the enterocytes, the CoQ10 molecules in the blood circulation are found predominantly in the reduced form. Accordingly, at some point, dietary CoQ10 in the oxidized ubiquinone form must therefore be reduced to the ubiquinol form; this occurs most probably in the lymph. Some CoQ10 supplement manufacturers are selling supplemental CoQ10 in the ubiquinol form because of the supposed superior bioavailability of the ubiquinol form of CoQ10. They are doing so because the absorption of CoQ10 into the enterocytes has been thought to require the reduction of ubiquinone to ubiquinol, an assumption that is based in part on work in which ubiquinone was reduced to ubiquinol during uptake by cultured Caco-2 human epithelial intestinal absorption cells [10 in the ubiquinol form was thought to facilitate the process of CoQ10 absorption, especially in patients with malabsorption disorders for whom the process of CoQ10 reduction process may be compromised, as well as in elderly individuals. The CoQon cells . Thus, t10. The chylomicrons in the blood carry the CoQ10 to the liver, where it is primarily loaded into LDL and VLDL lipoprotein particles. A much smaller quantity of CoQ10 is packaged into high-density lipoprotein cholesterol. Moreover, the platelets and leucocytes in the blood contain CoQ10. The erythrocytes contain a quite small amount of CoQ10 . Normal plasma CoQ10 levels vary from person to person and are typically in the range 0.5 to 1.5 μg/mL [10 has been reported to raise plasma CoQ10 levels from 0.90 to 3.25 mcg/mL, that is, to the level required for therapeutic efficacy in cardiovascular disease [The highest plasma concentration (Cmax) of the ingested CoQ.5 μg/mL . Twice d disease ,31.10 seems to protect against atherosclerosis. There is a relatively low concentration of CoQ10 (0.5–0.8 mol/LDL particle) in LDL cholesterol compared to the content of alpha tocopherol (8–15 mol/LDL particle) in LDL cholesterol. Even so, CoQ10 has been shown to protect human low-density lipoprotein against lipid peroxidation better than alpha tocopherol [10 to LDL cholesterol has not been widely used as a bio-marker; however, there is evidence that the ratio of CoQ10 to LDL cholesterol may be more important in the prevention of atherosclerosis than is the HDL/LDL cholesterol ratio [By inhibiting the oxidation of LDL cholesterol, CoQcopherol . The ratol ratio .10 is found in all cells. The relative tissue levels reflect the extent of metabolic activity and energy demand in various tissues and organs; accordingly, the highest CoQ10 levels are found in heart muscle tissue. Given its lipid nature, CoQ10 is assumed to enter the cell membranes by diffusion from the lipoprotein carriers in the blood; to date, there has been no known transporter of CoQ10 from blood into cells identified. Diffusion relies upon the movement of the CoQ10 molecules from areas of high CoQ10 concentration to areas of CoQ10 lower concentration; this need for a higher CoQ10 concentration in the blood than in the tissue cells is the rationale in clinical studies of heart failure patients for the use of CoQ10 dosages of 300 mg/day (and for plasma CoQ10 levels of at least 3 μg/mL). In this way, the movement of CoQ10 from the blood into tissues such as cardiac muscle will be facilitated [CoQilitated . 10 levels following supplementation is manifest as increased CoQ10 levels within tissues. Thus, a 2002 study by Rosenfeldt et al. in 24 older adults supplemented with 300 mg/day of CoQ10 or placebo for at least 7 days prior to cardiac surgery found that the CoQ10 level of atrial tissue was significantly increased in those taking CoQ10, especially in patients greater than 70 years of age [10 for 4 weeks before cardiac surgery increased CoQ10 concentrations in the heart but not in skeletal muscle [10, heme A, and selenoproteins, thereby impairing mitochondrial ATP production [There is some evidence that increased blood CoQs of age . In a 20l muscle . In 2015oduction . 10 in ubiquinol form have been identified. For example, cytochrome b5 reductase maintains CoQ10 in its reduced form [Once within cells, several enzyme systems capable of maintaining CoQced form . Moreoveced form . In addiced form . Recent studies by Bersuker et al.  and Doll10 has been shown to cross the blood–brain barrier; however, researchers have not been able to definitively show its ability to cross the blood–brain barrier in humans. The difficulty of getting supplemental CoQ10 into the brain with physiological dosages may explain the disappointing outcomes of some randomized controlled clinical trials supplementing CoQ10 in patients diagnosed with neurodegenerative disorders [In animal models with Gram-magnitude dosages, CoQisorders . 10. CoQ10 researchers do not fully understand the relative contribution of cellular bio-synthesis of CoQ10 and dietary derived CoQ10 to cellular requirements. CoQ10 molecules are found primarily within the mitochondria  but are also found in many other types of sub-cellular organelles such as the endoplasmic reticulum, the Golgi apparatus, lysosomes, and peroxisomes [10, whether endogenous or exogenous, a means of transport is required between these various sub-cellular organelles. This means of transport between and among organelles inside the cells is thought to involve a vesicle-mediated process that involves the Golgi apparatus. Although this transport process is not completely understood at present, it may involve saposin B as a presumptive binding and transfer protein for CoQ10 [10 elsewhere within the cell also needs to be taken into account. All cells except red blood cells have the capacity to manufacture CoQoxisomes . Regardlor CoQ10 . It shou10 compared to ubiquinone. Because ubiquinol and ubiquinone are continually inter-converted within the body, the concept that ubiquinol supplements may somehow be more efficacious than supplemental ubiquinone is incorrect, particularly in view of evidence reviewed above regarding relative bioavailability. However, it is correct that most randomised controlled clinical trials to date have been carried out using supplemental CoQ10 in ubiquinone form. This is particularly the case in relation to cardiovascular disease, where supplemental ubiquinone has been shown to be especially effective in two studies, Q-SYMBIO and KISEL-10. Contrary to some manufacturers’ claims, ubiquinol is not the active form of CoQ10 supplementation (3 × 100 mg/day for 2 years) on symptoms and biomarker status  was assessed, as an adjuvant to conventional medication (angiotensin-converting enzyme (ACE) inhibitors, beta blockers). Assessment included clinical examination, echocardiography, and NT-pro-brain natriuretic peptide (NT-proBNP) status. The primary long-term endpoint was the time to first major adverse cardiovascular event (MACE), which included unplanned hospitalisation due to worsening heart failure and cardiovascular death. supplementation with CoQ10 significantly reduced the relative risk of MACE by 42%, with a reduction in both cardiac related deaths (43%) and all-cause mortality (42%). There was no significant difference in adverse events between the CoQ10-treated and placebo groups over the duration of the study [10 supplementation in the European cohort (231 of 420 patients) of the Q-SYMBIO study has since been undertaken [10, resulting in a consistently higher plasma CoQ10 level for the duration of the study: 3.4 μg/mL at 3 months and 3.6 μg/mL at 2 years, compared to 3.0 μg/mL at 3 months and 2.1 μg/mL at 2 years in the full cohort. The Q-SYMBIO multi-national (nine country) study was carried out in 420 patients with chronic heart failure (NYHA class III and IV). The effect of CoQhe study . Sub-grodertaken . In thes10 together with 200 μg of an organic high-selenium yeast preparation or matching placebos daily for 4 years [The KISEL-10 study was a clinical trial that enrolled 443 senior citizens living in south-eastern Sweden. The study was designed as a randomized, double-blind, placebo-controlled clinical trial. The elderly study participants—average age of 78 years—took either 200 milligrams of coenzyme Q 4 years .Professor Urban Alehagen and the team of researchers from Linköping University and Karolinska Institute reported significantly reduced risk of death from heart disease in the active treatment group. The relative risk reduction was 53%. The researchers also reported significantly improved heart function as shown on echocardiograms and significantly reduced levels of the N-terminal-proBNP bio-marker for heart muscle dysfunction in the active treatment group compared to the placebo group . 10 and selenium was associated with a significant decrease in the number of days in the hospital and with significantly moderated deterioration in the senior citizens’ health-related quality of life [Moreover, the researchers quantified health-related quality of life outcomes. They used the Short Form-36, Cardiac Health Profile, and Overall Quality of Life survey questionnaires. The active treatment with a combination of CoQ of life .Sub-study analysis of the data from the KiSel-10 study showed significant reductions in the blood levels of known bio-markers for inflammation—C-reactive protein and sP-selectin—and in known bio-markers for oxidative stress—copeptin and adrenomedullin ,50.10 and selenium endure for several years beyond the period of intervention. A follow-up analysis showed that the significantly reduced risk of death from heart disease still held 12 years after the intervention [10 and selenium supplementation on the risk of heart disease in community living senior citizens [The cardio-protective effect of the combined daily supplementation with CoQrvention . Professcitizens ,52.There are unsubstantiated manufacturers’ claims circulating on the internet that individuals over 40 years of age may be progressively less able to absorb supplemental ubiquinone. However, the two studies above provide evidence that older individuals  are able to absorb and subsequently benefit from a properly formulated ubiquinone supplement .10 absorption is complex. It is not surprising that the absorption and bioavailability of CoQ10 supplements can vary widely and does indeed do so. This variability depends primarily on the formulation of the preparation. As there is considerable inter-individual variability in the uptake of CoQ10, the absorption and bioavailability of CoQ10 also depends on the capacity of a person to absorb a preparation with a given formulation. Thus, studies by both Wahlqvist et al. [10 were demonstrated as large inter-individual variations in the 36 h AUC and 48 h AUC, respectively, following the CoQ10 administration. As detailed above, the process of CoQt et al.  and Lopet et al.  demonstr10, there now seems to be evidence that there are multi-functional systems in place to convert ubiquinone to ubiquinol, involving as many as five different enzymes. Studies by Nordman et al. [10 in reduced form to maximise absorption would be negated.With regard to the relative importance of ubiquinone and ubiquinol supplemental forms of CoQn et al. , Ross ann et al. , Bersuken et al. , Doll etn et al. , and Takn et al.  are of rn et al.  establis


This study demonstrates the development and application of a novel workflow for designing and fabricating orthoses, using a combination of 3D scanning and 3D printing technologies. The workflow is applied to a clinically relevant translational case study in a patient with a neurological disorder and complex clinical needs. All traditional and commercial approaches to helping the patient’s cervical instability and resulting ‘head-drop’ had previously failed, with associated progressive deterioration in the patient’s clinical state and posture. The workflow was developed to design and fabricate a bespoke device for this patient with no viable alternative therapy. The workflow was developed to generate 3D printable geometry from obtained 3D scan data. The workflow includes algorithms to relax geometry, distribute material efficiently and for variational cutting of orthosis padding material. The 3D patient scan was validated against actual measurements to ensure accuracy of measurements. A total of four prototypes were produced with each iteration being improved based on patient and clinical feedback. There was a progressive improvement in subjective feedback through each iteration at sites of discomfort and overall comfort score. There was a marked improvement in the patient’s posture with correction at the cervical and lumbar spine with the 3D-printed padded collar being worn for 4 hour periods. This study has implications for the rapid production of personalised orthoses which can help reduce patient waiting time, improve patient compliance, reduce pain and reduce further deterioration. The workflow could form the basis for an integrated process, whereby a single hospital visit results in a bespoke orthosis optimised and personalised for each patient. This results in waiting times of up to 6 weeks, with multiple patient visits, at a social and economic cost, and no guarantee of patient concordance. Orthotics may have to be revised regularly, requiring the orthosis to be re-made and repetition of this time-consuming and skilled process. The application and combination of new technologies may help to overcome the limitations of these traditional approaches while replicating or improving the mechanical, functional and aesthetic properties.Orthoses are externally applied devices that provide structural or functional adjustment to the body. They are intended to improve the user’s level of function and quality of life by guiding, restricting or supporting movement. Traditionally, custom orthoses are made via a laborious, manual process by skilled clinicians, involving casting, sculpting, and moulding of a thermoplastic and fitting to the body3, education4, prostheses6 and drug delivery7. 3D printed orthotic devices can be made to have comparable biomechanical properties to traditionally manufactured devices, with potential for fine control over these properties11. Orthotic designs made via computer-aided design (CAD), can be widely distributed through large open-source communities and then customised by the end-user12, allowing adjustment of the resulting model to meet the user needs. This contrasts with manufactured, commercial devices - a ‘one-size-fits-all’ approach - and greatly reduces the cost associated with creating customised, bespoke devices. Patient feedback can be obtained on CAD designs before manufacture, with patient feedback influencing the design process, leading to greater patient involvement and sense of autonomy, thereby hopefully improving compliance13.Additive manufacturing, or 3D printing, is a process whereby material is sequentially added to print a 3D object from a computer model. It is becoming an accessible and widespread technology, which has demonstrated numerous applications within medicine, from surgical planning14; anthropometric measurements in scoliosis15; monitoring of chronic wounds16; and in aesthetic surgery and dermatology17. When combined with 3D printing, 3D scanning will likely become an invaluable tool in constructing bespoke orthoses, prostheses and exoskeletons19. To be applicable in clinical practice, standardised protocols must be developed that enable reliable and reproducible acquisition of scan data20.3D scanning is a process whereby shape, and sometimes colour, information is collected from a physical object to construct a digital 3D model. In medicine, 3D scanning can be used to rapidly create accurate geometry of the surface of the human body. This has shown wide applicability in healthcare - estimation of burn surface area21. They have been used to rapidly generate 3D printable geometry for wrist splints from 3D scans22, ankle-foot orthoses10 and dental implants23.The development of digital workflows typically involves generating designs based on imaging or 3D scanning data, using procedural or parametric software to create a customised, semi-automated process. Such workflows offer the potential for rapid iteration and generation of prototypes, as well as for non-expert users to generate designs following identification of anatomical landmarks24, anisotropy25, or local spatial variation in properties27. By combining computer models of bone with additive manufacturing technologies, one can create complex scaffolds that mimic both the bone material and internal structure28. This study seeks to apply similar principles to the development of a real-world orthosis.Biomimetics involves replicating Nature’s evolved, well-adapted systems to help solve human problems. By imitating the complex spatial heterogeneity and hierarchical structures present in natural tissues one can create novel, useful materials29. Recently, customisable and configurable devices specifically intended for patients with progressive neuromuscular neck weakness have been developed30. Detachable support modules facilitate planes of movement, supporting the head whilst not unnecessarily restricting movement, ensuring muscle tone31. Such devices offer a degree of customisability by the end user, a step between mass produced commercial devices and bespoke orthotics. However, these collars still rely on accommodating a normalised anatomy, and may not be suitable for patients with unique clinical needs or atypical anatomy, for whom fully bespoke devices may be the only option.Cervical collars are used for three functions: to stabilise or immobilise the neck following trauma or surgery; to support the neck in cases of chronic neck pain; or to hold the head up in neuromuscular weakness. Most commercial collars are intended for use in trauma; long-term use of these collars is uncomfortable, restrictive and poorly toleratedThis study demonstrates the development and application of a novel workflow for creating cervical orthoses, using a combination of 3D scanning, biomimetic design and additive manufacturing. The process is validated with a translational case study demonstrating the successful generation of a bespoke device for a patient in whom traditional cervical orthotic approaches had failed.Transverse myelitis is an acute or subacute inflammation of the spinal cord resulting in neurological deficits such as weakness, sensory loss or autonomic dysfunction. The aetiology may be autoimmune or infectious, though in many cases is unknown, and deemed idiopathic. This case study considers the case of a 55-year-old female patient at the time of injury, who became unwell in November 2011 and experienced a 2-month progressive history of bilateral upper limb weakness, neuropathic pain and sensory disturbance. MRI investigations suggested a C4–6 Transverse Myelitits resulting in a central cord syndrome. The patient underwent extensive investigations that identified central and peripheral demyelination. Formal assessment categorized a spinal cord injury at C1, ASIA Impairment Scale (AIS) D Incomplete tetraplegia . The resulting neurological deficits at the time of injury were: significant weakness in her neck muscles so that she was unable to maintain upright neck alignment with a tendency to hold her neck in right side and forward flexion; neck posture that contributed to a flexed/kyphotic upper thoracic spine with poor shoulder girdle alignment, bilateral shoulder subluxation with no active upper limb movements. Whilst able to walk independently she required full assistance for all activities of daily living. Upper limb orthoses were required for stability and pain relief; she was unable to tolerate a cervical collar due to a sense of restricted breathing. Over the intervening years, her cervical posture deteriorated, causing pain, reducing her field of vision and affecting the patency of her airway and general respiratory function. In order to be able to mobilise and visualise her surroundings, the patient would compensate by exaggerating her lumbar lordosis to lean posteriorly, resulting in further deviation of her thoracic and lumbar spine. No viable commercial neck support or traditional bespoke collar fabrication was found that could accommodate her marked cervical kyphosis without resulting in partial compression of her airway and the neurovasculature of her neck in conjunction with an inability to tolerate a feeling of pressure across her chest, compounded by partial sensory denervation of her diaphragm.Informed consent was given by the patient and her family to produce and evaluate a bespoke orthosis and for online publication of 3D scans, images and clinical case details in an open-access scientific journal. Approval was gained from the Innovation Development Committee at the Royal National Orthopaedic Hospital. All methods were carried out in accordance with relevant guidelines and regulations for production of other bespoke cervical devices, by trained clinicians.15. The 3D scan mesh was validated by comparing real-world measurements to scan data, based on distances between anatomical landmarks32.A 3D surface scan was obtained, with the clinical support of the patients head in a corrected position, using the ‘Artec EVA’, a handheld scanner, with the software Artec Studio 12 Professional used to process 3D surface scans. This scanner has previously shown excellent agreement with high-resolution reference scans for torso and spinal imagingThe design process and workflow was developed using Houdini software . The 3D scan mesh is imported into the Houdini software, and the user positions simple guide geometry, a cylinder, over the 3D scan. This geometry is projected onto the 3D scan mesh Fig. . The pro−3 acting under gravity on the collar geometry. The internal stresses from this simulation can be stored in the mesh. The larger the deformation, the greater the energy stored in the mesh. The local density of the deformation energy is mapped to the surface, and can be used to influence subsequent steps in the workflow. Specific, physical material parameters were not used, and the mesh was simplified as being isotropic i.e. physical properties of the mesh were modelled as uniform in all dimensions. It should be noted fused deposition modelling (FDM) results in an anisotropic structure, with strength varying according to orientation of extruded filament deposition. This anisotropy could be accounted for in a more accurate simulation, with orientation of deposited material being optimised according to the direction of load.This mesh is used in Houdini’s finite element analysis solver. The mesh is inputted with default stiffness parameters for a solid object; the 3D scan head is modelled with a uniform density of 1 kgmThe porous pattern is generated to improve ventilation, as well as reducing the material cost and weight of the final orthosis. This is achieved using a reaction diffusion algorithm, used to simulate a variety of patterns in nature, with the deformation energy used as input for pattern generation Fig.  and 3. T33 with a build resolution of 0.178 mm  and used for padding. The fitted and relaxed collar geometry was used as input for a variational surface cutting algorithm Fig. , which slar Fig. .29. The patient evaluated the collar at home, with regular, real-world use. The collar was fitted by a career.The collar was evaluated by the patient using a modified questionnaire, previously validated for commercial cervical orthosesMean and standard deviation of the maximum errors were calculated for both data acquisition methods. An independent t-test  was used to compare average maximum errors of both data acquisition methods.32. This scanner has previously shown excellent agreement with high-resolution reference scans for torso and spinal imaging15. It has previously been validated for acquisition of 3D scan data for bespoke 3D printed devices18.The Artec EVA 3D scanner has a 3D point accuracy of 0.1 mmThis process was integrative and relied on close communication between engineering, the clinical team and the patient. A total of 4 prototypes were designed to capture and rectify patient and clinical feedback. With each design, patient comfort and usability was improved by making small changes to adopt design considerations and to correct cervical posture and improve patient compliance.The first prototype validated the 3D scanning to 3D printing workflow - i.e. to successfully take a 3D scan and create 3D printable geometry that conforms to the patient. The first design was hinged along one side, and printed with a semi-flexible nylon material. Although this design offered the possibility of an easily fitted, wrap-around ‘clamshell’ structure, the material was not sufficiently flexible to be easily opened to accommodate the neck. This design was separated along the hinge and fitted to the patient. This collar was poorly tolerated as it was too close fitting and could not accommodate the necessary padding.The second prototype involved a number of modifications - the edges of the collar were everted, away from the skin to prevent skin pressure at the collar edges. The clearance between the skin and the orthotic surface was increased to accommodate padding. The collar was printed in two opposing sections, with straps at the sides. The third prototype involved the manual removal of material away from the second prototype, at the shoulders and head, and a corrective adjustment to the patient’s tendency of lateral and axial rotation of the head with padding. The fourth prototype was re-printed taking into account previous improvements, including the removal of material at sites of discomfort from prototypes 2 and 3.The patient-specific cervical collar was designed and produced using the workflow described in section 2.2. The final prototype took 2 min 45 sec to generate 3D printable geometry from 3D scan data (Intel Core i7 8700 K 3.70 GHz), i.e. this is the time taken to proceed through the procedural workflow. The FEM process combined with the porosity algorithm reduced the volume of the orthosis by 13%, with the added benefit of improving ventilation. A FEM approach with topological optimisation may mean the material is used more efficiently, although such methods do not factor in patient comfort or aesthetics. The resulting 3D printed orthosis was provided to the clinical team for inspection and discussion. Once a design was finalised, it was 3D printed and fitted to the patient by an Occupational Therapist (the collar was fitted at home by the carer).Final fabrication of orthoses was done using Stratasys Fortus 380mc 3D printer. Acrylic Styrene Acrylonitrile (ASA) was identified as the most appropriate material with the required mechanical properties and surface finish required for the orthosis in terms of strength and comfort. The total time for printing and post-processing of the 3D printed collar was 48 hours (~2 days).2. Manufacturing is performed by a skilled technician in between these appointments. For a typical orthosis, this results in a turnaround time of around 6 weeks in our hospital. With the use of a 3D scanning process, it is possible capture all required measurements of patients in short appointments lasting 10–15 min, removing the need for moulding or casting steps, and saving considerably on labour-hours at busy orthotics units. We anticipate by having integrated 3D design, printing facilities within orthotic divisions of hospitals, the delivery time for 3D printed orthosis can be further reduced in future. CAD/CNC controlled cutting of the padding material would enable the padding process to be fully automated, and integrated into the design workflow in future.The collar was fitted with commercial orthotic padding for durability and patient comfort. The variational surface cutting algorithm ensured the padding conformed to both the patient’s body and the inner surface of the orthosis and automated the process of creating a template from a 2D plane that conformed to the 3D orthosis with minimal distortion. Total time from scanning to delivery of 3D printed orthosis was approximately 72 hrs (3 days). The time taken for supplying traditional orthoses typically involves separate patient appointments for initial assessment, measurement, evaluation, adjustment and final supply of the device, with each appointment lasting around 40 minutes in a complex caseFour prototypes were produced for this case study, with each prototype being altered based on patient feedback from the previous iteration. The patient and clinical team was involved throughout the design process, improving patient compliance and engagement. Each prototype was evaluated using a modified questionnaire previously used to evaluate commercial cervical orthoses . The initial required steps in the workflow could be performed by a clinical team, relying on inputting guide geometry and positioning it over the region of interest on the 3D scan. By far the most time-consuming and time-limiting stage of the process is the 3D printing of the orthosis. However, with integrated 3D printing facilities and access to multiple printers can reduce this time. The cost of the 3D printed collar was £325, with printing undertaken at a commercial 3D printing company with the necessary printing facilities. This compares to an estimate of £350 for a similar device produced using traditional methods at our hospital.35. Further optimisation of the design via a more intensive FEM approach may reduce material cost and print time.Fused filament fabrication, the most commonly used printing technology, involving deposition of a heated filament through an extruder, is inherently slow. Selective laser sintering (SLS) may be a cheaper and faster process, although is generally less accessible and therefore was not used in this proof-of-concept study. Newer printing technologies such as large-scale rapid liquid printing, or ‘CLIP’ (Continuous liquid interface production of 3D objects) are promising as potentially much more rapid production additive manufacturing technologiesThis study demonstrated the combination of 3D scanning and 3D printing in a clinically relevant and validated case study. The workflow was demonstrated to be feasible and potentially more rapid than traditional methods, with quantifiable clinical and patient benefits. In future work, the proposed workflow will be extended to larger group of patients with varying anatomy and cervical pathology, in whom traditional or commercial approaches have failed This study has implications for the rapid production of personalised orthoses which can help reduce patient waiting time, improve patient compliance, reduce pain and reduce further deterioration. The workflow could form the basis for an integrated process, whereby a single hospital visit results in a bespoke orthosis optimised and personalised for each patient.Supplementary information 1.Supplementary information 2.Supplementary information 3.Supplementary information 4.Supplementary information 5.Supplementary information 6.


To investigate whether dog and cat owners and their pets share a risk of developing diabetes.Cohort study.Register based longitudinal study, Sweden.208 980 owner-dog pairs and 123 566 owner-cat pairs identified during a baseline assessment period (1 January 2004 to 31 December 2006).Type 2 diabetes events in dog and cat owners and diabetes events in their pets, including date of diagnosis during the follow-up period (1 January 2007 to 31 December 2012). Owners with type 2 diabetes were identified by combining information from the National Patient Register, the Cause of Death Register, and the Swedish Prescribed Drug Register. Information on diabetes in the pets was extracted from veterinary care insurance data. Multi-state models were used to assess the hazard ratios with 95% confidence intervals and to adjust for possible shared risk factors, including personal and socioeconomic circumstances.The incidence of type 2 diabetes during follow-up was 7.7 cases per 1000 person years at risk in dog owners and 7.9 cases per 1000 person years at risk in cat owners. The incidence of diabetes in the pets was 1.3 cases per 1000 dog years at risk and 2.2 cases per 1000 cat years at risk. The crude hazard ratio for type 2 diabetes in owners of a dog with diabetes compared with owners of a dog without diabetes was 1.38 , with a multivariable adjusted hazard ratio of 1.32 (1.04 to 1.68). Having an owner with type 2 diabetes was associated with an increased hazard of diabetes in the dog , which was attenuated after adjusting for owner’s age, with the confidence interval crossing the null . No association was found between type 2 diabetes in cat owners and diabetes in their cats .Data indicated that owners of a dog with diabetes were more likely to develop type 2 diabetes during follow-up than owners of a dog without diabetes. It is possible that dogs with diabetes could serve as a sentinel for shared diabetogenic health behaviours and environmental exposures. Type 2 diabetes is recognised as a major global public health challenge, with more than 400 million individuals affected worldwide.Dogs and cats that develop diabetes usually present with distinct and rapidly progressing symptoms, including excessive thirst, polyuria, and weight loss, similar to the clinical presentation of type 1 diabetes in humans.The pathogenesis of diabetes in cats is suggested to largely correspond to that of type 2 diabetes in humans,Cross sectional studies have indicated an association between adiposity in dog owners and their pets,We aimed to contribute to the knowledge base on the potential interplay between human and pet health as outlined in One Health initiatives.Agria Pet Insurance is the largest animal insurance company in Sweden and covers an estimated 40% of the dog populationWe identified 151 054 dog owners and 74 336 cat owners born before 1961 with an active veterinary care policy with Agria Pet Insurance at any time from 1 January 2004 to 31 December 2006 (the baseline assessment period). The cut-off at 1961 in the initial register linkage was chosen to exclude younger individuals who were at lower risk of type 2 diabetes. From the Register of the Total Population we further extracted information on 94 327 spouses or cohabiting partners of the dog owners and 41 764 spouses or cohabiting partners of the cat owners. For non-married partners, information was only available for cohabiting partners who had children in common. The spouses and cohabiting partners were also considered to be pet owners, and the term pet owner used hereafter refers to both.We excluded 2958 dog owners and 1192 cat owners who died or emigrated from Sweden before 1 January 2007, and 5306 additional dog owners and 2428 cat owners with incomplete baseline or follow-up data or unclear emigration status. After these exclusions we had 237 117 dog and 112 511 cat owners. We then included 218 392 eligible insured dogs and 122 063 eligible insured cats and identified all owner-dog pairs and owner-cat pairs in our study population.Of these owner-pet pairs, we excluded 30 025 owner-dog pairs and 8898 owner-cat pairs because the owners each had more than 10 insured pets and probably represent breeders where not all pets are kept within the household of the registered owner. We also excluded 105 owner-dog pairs and 83 owner-cat pairs with incomplete pet follow-up or an invalid pet birth date, as well as 101 126 owner-dog pairs and 43 955 owner-cat pairs that included a pet without diabetes whose insurance was terminated before 1 January 2007. Lastly, we excluded 46 owner-dog pairs and 16 owner-cat pairs when both owner and pet had a diagnosis of diabetes before 1 January 2007. The final study population comprised 208 980 owner-dog pairs and 123 566 owner-cat pairs .If an individual owned both a dog and a cat, we included that individual as both a dog owner and a cat owner, and together with the pets these formed both an owner-dog pair and an owner-cat pair. Similarly, if an individual owned multiple dogs and cats, that individual formed one owner-pet pair for each pet.Owners were considered to have type 2 diabetes if they had a main or secondary diagnosis  of type 2 diabetes (ICD-10 code E11) within the National Patient Register  or in the Cause of Death Register, or both. In addition, we considered owners to have type 2 diabetes if they had at least one dispensed prescription of an oral diabetes drug or a non-insulin injectable diabetes drug, or both (Anatomic Therapeutic Codes A10B and A10X) in the Swedish Prescribed Drug Register, even without registration in the other registers. These data were available from 1 July 2005. Type 1 diabetes diagnoses or insulin prescriptions were not included in our study. Information on diabetes status of the pets was extracted from the Agria Pet Insurance data. In Sweden, veterinarians report disease diagnoses to this register using a standardised coding system.From the Total Population Register, we retrieved information on country of birth , or a non-Nordic country) and on Swedish region of residence . Baseline household socioeconomic circumstances in 2006 were extracted from the Longitudinal Integration Database for Health Insurance and Labour Market Studies,Information on date of birth, date of start and end of insurance, breed, and date of death of the pets were available from the Agria Pet Insurance data. We categorised the dog and cat breeds as those with high, moderate, or low risk of diabetes in accordance with two previous Swedish large cohort studies that investigated diabetes incidence in dogWe utilised a Weibull-Markov multistate modelFor each owner-pet pair, we defined the combined baseline status as no diabetes in owner or pet, owner with type 2 diabetes, diabetes in pet only, and diabetes in owner and pet. Owner-pet pairs that shared a diagnosis of diabetes during baseline were not assessed during follow-up and were excluded from analyses as they could make no further transition. During the longitudinal follow-up period, we defined a transition as when the owner or the pet received a diagnosis of type 2 diabetes or diabetes, respectively.After the start of follow-up period, we censored owner-pet pairs at death of the owner or pet, emigration of the owner, termination of pet insurance, or end of follow-up (31 December 2012), whichever occurred first. However, we did not censor owner-pet pairs due to death of the pet if the pet had already had a diagnosis of diabetes since any further transition would only be due to change in the type 2 diabetes status of the owner.A total of four transitions were possible (see figure 3): no diabetes in owner or pet to owner with type 2 diabetes; no diabetes in owner or pet to diabetes in pet; owner with type 2 diabetes to diabetes in both owner and pet; and diabetes in pet only to diabetes in both owner and pet. Hazard ratios were calculated for each transition. To investigate whether the hazard ratios differed according to diabetes status of the other part of the owner-pet pair, we investigated whether the hazard ratios in our Weibull-Markov model  were significant at an α level of 0.05. In the main analyses, we present a crude unadjusted model and a fully adjusted model . In the supplementary file we have included information on the attenuations of the estimates for owner-dog pairs and owner-cat pairs when the covariates are added consecutively. In those analyses, model 1 constituted the crude non-adjusted model. Models 2a and 2b were adjusted for the age and sex of the owner and pet, with certain constraints depending on the transition. Model 3 was adjusted for age and sex of the owner and pet, without constraints. To model 3, we sequentially added breed group (model 4), country of birth (model 5), population density and region of residence (model 6), and highest attained education level, marital status, and disposable income . In all analyses, breed group, country of birth, education level, and region of residence were modelled as categorical variables. Income group was modelled as a continuous variable ranging from 1 to 10, with the first decile corresponding to the value 1, the second decile to the value 2, and so on. Age of owner and age of pet were modelled using restricted cubic splines, with knots placed on the 10th, 50th, and 90th centiles. Owner-dog and owner-cat pairs were analysed separately in all analyses, and cluster robust standard errors were used to account for the dependence of owners and pets within households.The hazard ratio of diabetes in dogs was attenuated when age of owner was included as a covariate in model 2b (see supplementary file). Supplementary figure 3 shows the association between age of dog owners on hazard of diabetes in their dogs.We further aimed to assess potential bias from the decision to include owner-pet pairs with dead pets with a diagnosis of diabetes. We therefore also performed a sensitivity analysis in which we excluded all pets who died before the start of the follow-up period, regardless of their diabetes status, as well as follow-up time contributed by pet-owner pairs after the death of a pet with a diagnosis of diabetes.All analyses were performed in Stata/MP 14 .Neither study participants nor the public were in any way involved in the design, conduct, reporting, or planning of dissemination of our research.The owner-dog study population included 208 980 pairs, comprising 175 214 owners and 132 783 pets . Most ofThe owner-cat study population included 123 566 owner-cat pairs, consisting of 89 944 cat owners and 84 143 cats . Most ofDuring a maximum of six years (median 3.4 years) of follow-up, the observed incidence rate of type 2 diabetes in dog owners was 7.7 cases per 1000 person years at risk and in dogs was 1.3 cases per 1000 dog years at risk.Compared with owning a dog without diabetes, owning a dog with diabetes was associated with an increased hazard of type 2 diabetes . This estimate, however, was attenuated in the fully adjusted model, with the confidence interval crossing the null . In additional analyses the main attenuation was observed when age of the owner was introduced as a covariate , as illustrated in supplementary figure 2.During a maximum of six years (median 3.8 years) of follow-up, the observed incidence rate of type 2 diabetes in cat owners was 7.9 cases per 1000 person years at risk and in cats was 2.2 cases per 1000 cat years at risk.Compared with owning a cat without diabetes, owning a cat with diabetes was not associated with an increased risk of type 2 diabetes .In this large cohort study, we found that ownership of a dog with diabetes was associated with an increased hazard of type 2 diabetes in the dog owner. The estimate was not attenuated when available shared risk factors were considered. We detected no association between type 2 diabetes in cat owners and diabetes in their cats.Strengths of this study include the population based prospective study design, unique data linkage, and essentially complete follow-up. Some potential limitations apply. Firstly, information on health behaviours such as diet and physical activity level were not available, preventing investigation as to whether these represented the underlying causes of the associations. Secondly, owners with type 2 diabetes, or owners of a dog with diabetes, might have an increased awareness of overt diabetes symptoms and thus contribute to a surveillance bias effect within the owner-dog pair. Dogs with diabetes exhibit a distinct and rapidly progressive symptomatology, and subclinical diabetes or a pre-diabetic state is not commonly identified in dogs, not even in breeds with a high risk of diabetes.Several different underlying mechanisms might explain the observed association between type 2 diabetes in dog owners and risk of diabetes in their pet. Firstly, dog owners and their dogs could share lifestyle behaviours that affect the risk of diabetes. Cross sectional studies in veterinary clinics have reported an association between owner and dog adiposity.A potential partial explanation for our findings could be shared microbiota influencing diabetes risk in both owners and their pets. Dog owners have been found to share skin microbiota with their pets, and ownership of household pets including dogs and cats has been suggested to be associated with differences in the gut flora of the owner.Moreover, shared exposures to diabetogenic factors in the environment in terms of noise or air pollution, or exposure to endocrine disrupting chemicals, might influence the risk of diabetes in both owners and their dogs. We did not have access to information on environmental pollutants or chemical exposures in our cohort but we noted no attenuation of our estimates when we adjusted for population density in home municipality, which is a proxy for urban or rural dwelling and might represent different levels of pollution. In addition, the lack of an effect of regional circumstances does not support that distance to healthcare facilities has a large influence on the observed estimates.Lastly, in a high income country such as Sweden, the risk of type 2 diabetes is higher among individuals of a lower socioeconomic class.In contrast, we could not detect any association between incidence of type 2 diabetes in cat owners and the development of diabetes in their pets, even though the cat diabetes phenotype more closely resembles that of humans with type 2 diabetes than that of dogs with diabetes. Environmental risk factors for diabetes in cats include indoor confinement, being a greedy eater, eating predominantly dry foods, and being overweight.Owning a dog with a diagnosis of diabetes was associated with an increased hazard of type 2 diabetes in the owner. Potential underlying mechanisms for our findings on diabetes in owner-dog pairs possibly include shared health behaviours such as level of physical activity, and possibly also shared environmental exposures.Dog owners and their pets might share certain health behaviours, such as physical activity levelCross sectional studies have indicated an association between adiposity in dog owners and their petsNo previous study has investigated shared diabetes risk in dog and cat owners and their petsDog owners who have a pet with diabetes were more likely to develop type 2 diabetes during follow-up than owners of a dog without diabetesPersonal and socioeconomic circumstances of the dog owners could not help to explain the shared diabetes risk of the owner-dog pairs; underlying mechanisms might include shared diabetogenic health behaviours and environmental exposuresNo shared risk of diabetes was found between cat owners and their pets


The discovery of new antiviral therapies is imperative to address this challenge, and antiviral peptides (AVPs) represent a valuable resource for the development of novel therapies to combat viral infection. We present a new machine learning model to distinguish AVPs from non-AVPs using the most informative features derived from the physicochemical and structural properties of their amino acid sequences. To focus on those features that are most likely to contribute to antiviral performance, we filter potential features based on their importance for classification. These feature selection analyses suggest that secondary structure is the most important peptide sequence feature for predicting AVPs. Our Feature-Informed Reduced Machine Learning for Antiviral Peptide Prediction (FIRM-AVP) approach achieves a higher accuracy than either the model with all features or current state-of-the-art single classifiers. Understanding the features that are associated with AVP activity is a core need to identify and design new AVPs in novel systems. The FIRM-AVP code and standalone software package are available at  The control of viral disease continues to be a challenging task due to increased resistance to available antiviral therapies, which are limited, and the continual emergence of novel viral pathogens. Antiviral peptides (AVPs) are a subset of antimicrobial peptides and are a potential resource for the development of new potent therapeutics for preventing or treating viral infection. The ability of AVPs to target various aspects of the viral lifecycle, ranging from their attachment to host cells to their ability to impair viral replication within the cells has been the subject of multiple studies13. AVPs can be natural or synthetic, obtained by introducing chemical groups or non-natural amino acids into natural peptide sequences14. Considering AVPs in the design of new antiviral therapeutics is advantageous because it allows us to capitalize on their low molecular weight, low toxicity, high specificity and effectiveness, and minor side effects15. Machine learning is a powerful strategy for identifying AVPs by leveraging the ever-increasing data available in public databases, such as AVP Prediction (AVPpred)16, Antimicrobial Peptide Database (APD3)17, Collection of Antimicrobial Peptides (CAMPR3)18 and HIV inhibitory peptides database (HIPdb)19.Zoonotic viruses such as Ebola virus, Zika virus, West Nile virus and recently severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can cause life-threatening disease outbreaks due to their high genetic diversity, variety of routes for transmission, and ability to replicate efficiently and to persist in their hosts25 for predicting AVPs. Thakur et al.16 developed AVPpred, a web server for collecting and detecting highly effective AVPs. The authors used a support vector machine (SVM) to build two machine learning models based on amino acid composition (AAC) and physicochemical features. This was then extended to use a random forest (RF)-based model20, which was able to outperform the SVM utilized in AVPpred. The RF models were constructed using AAC, physicochemical properties, aggregation propensities of amino acids and secondary structure. Lissabet et al.21 developed a portable software version of the RF method called AntiVPP 1.0 that gives improved prediction accuracy. Qureshi et al.22 introduced a regression-based algorithm AVP-IC50Pred to predict AVP half maximal inhibitory concentration (IC50). Various peptide features such as AAC, binary profile, physicochemical properties, solvent accessibility were considered, and a number of machine learning techniques with individual and different combination of features were used to predict the IC50 value of the peptide sequences. Further, based on the assumption that AVPs have low sequence similarity the use of pseudo amino acid composition (PseAAC)26 was introduced as AVP peptide features in the AdaBoost machine learning model23. In recent years ensemble-based methods have been introduced, such as Meta-iAVP25 and PePred-Suite24. The Meta-iAVP approach uses machine learning to transform the feature space into a modified 6-dimensional predicted output vector, which then becomes the input data to the meta-classifier to predict the class of validation data set. PEPred-Suite is similar to Meta-iAVP where a RF is used as both the base and meta classifiers. Both Meta-iAVP and PEPred-Suite use these ensemble strategies to improve the AVP prediction accuracy.Researchers have previously developed machine learning models16 and Chang et al.’s method20. We packaged the resulting approach into a software tool called Feature-Informed Reduced Machine Learning for Antiviral Peptide Prediction (FIRM-AVP).The series of machine learning developments in AVP have to date focused on increasing the features that characterize a peptide and making minor modifications to the machine learning algorithm. They have not included feature reduction techniques that would determine the most relevant and non-redundant features from the initial set of input features. The performance of a machine learning model can rely heavily on using the most informative features, with the inclusion of non-informative features resulting in potential degradation in classifier performance. In the current study we identified the most important features by estimating Pearson's correlation coefficient and mean decrease of Gini index (MDGI) for all candidate features, which is a metric of feature importance based on the individual decision trees in a random forest model. The candidate features were generated from the physicochemical and secondary structure properties of a library of known AVP and non-AVP sequences. Subsequently, we applied a recursive feature elimination (RFE) algorithm in combination with the SVM to determine the order of importance of the different features. We evaluated multiple machine learning approaches, including SVM, RF and deep learning (DL) via multiple neural network architectures and hyperparameters, for training and testing purposes using our selected feature set. Our SVM-based method achieved the best test accuracy and Matthews correlation coefficient (MCC) values compared to the RF and DL approaches as well as outperformed AVPpred16 that has been used consistently since its introduction to evaluate AVP prediction models. It consists of a total of 1056 unique peptides. This set of peptides was distilled from a starting collection of 1245 peptides that were reduced to remove peptides with too high of similarity. Out of them, 604 sequences are highly effective (positive samples), and 452 sequences are minimally or non-effective AVPs (negative samples). These datasets were used for training and validating the machine learning model. To construct the training and independent test sets to benchmark our results with existing SVM and RF-based models we followed the same process as described previously20. This yields 544 and 407 positive and negative samples in the training dataset, respectively, and the validation/independent test set consisted of 60 and 45 positive and negative samples, respectively as defined by prior publications to assure accurate comparison. This validation set has similar overall viral diversity as the training set. On the AVPpred server there are additional peptides for the negative samples set, 544 in training set and 60 in the independent test set, however; these peptides have not been confirmed experimentally and thus are not included here.We used the same experimentally validated dataset reported in AVPpred27 to incorporate sequence-order information. The dimension of the PseAAC feature vector is 31 to generate feature vectors for 8 physicochemical properties; hydrophobicity, normalized van der Waals volume, polarity, polarizability, charge, secondary structure, solvent accessibility and surface tension of peptide sequences. In the CTD model, amino acids are classified into three classes based on their physicochemical properties. For composition, we obtained 3D feature vector that give the fraction of each encoded class in a peptide sequence. A transition feature vector of 3D gives the transition of one class followed by another class and vice versa. We also obtained a 15D feature vector for distribution that indicates the percent distribution  of each class in a peptide sequence. As we have 8 physicochemical properties, the CTD model gives a (3 + 3 + 15)34. In summary, we generated 649 peptide sequence-based features listed in Table ver 4.0.0)35. We utilized the protr (ver. 1.6-2)30 and DECIPHER (ver. 2.14.0)36 packages to extract features from peptide sequences.We combined several sets of features based on the peptide sequences: a 20D feature vector for AAC expressed as the percentage representation of a particular amino acid in a peptide; a 400D feature set was generated based on the dipeptide composition (DC) which represents the fraction of dipeptides within a peptide sequence; and the PseAAC and amphiphilic pseudo amino acid composition (APseAAC) proposed by Choumentclass2pt{minimThe DC feature vector  are the dipeptide composition (Supplementary Table caret (ver. 6.0-86)37. For the DL, variations on the Multi-layer Perceptron were the most successful. These binary classification models were then used to classify the test set of peptides. Note that we tuned the SVM and RF models with the training dataset and used the best models for prediction. The SVM model was tuned using the radial basis function kernel with cost values of 4, 8, 16, 32, 64, and 128. The RF model was tuned with ntree values of 50, 100, 200, 300, 400 and 500 and mtry values of 2, 4, 8, 16, and 32. The final SVM model used a cost value of 8, and RF model was with ntree = 100 and mtry = 32, which was chosen as best models for the selected feature on the training data. We utilized the e1071 (ver. 1.7-3)38 package to tune the models.We utilized three machine learning approaches to train the AVP prediction model, traditional SVM and RF methods, as well as DL via multiple architectures and hyperparameters using the machine learning library, The 649 features may contain redundant and information irrelavent to the classification of AVPs. To reduce the dimensions of the features we calculated the Pearson's correlation coefficient [using Eq. ] betweenstats package (ver. 3.6.2) to compute the Pearson correlation values between features.If the absolute value of the correlation between two features is greater than a threshold value, one of the two features were removed randomly from further consideration. We considered a range of correlation threshold from 0.7 to 0.95 in increments of 0.05. A correlation threshold was selected to optimize the Area Under a Receiver Operating Characteristic Curve (AUC) associated with the feature selection, which set the parameter to 0.85 and reduced the dataset to 568 features. We utilized the R 39. A node is considered as more pure in the RF model if the Gini index is closer to 0. The Gini index is calculated using Eq.  using an RF model for the remaining features. We can find the feature importance using MDGI to measure the contribution of each feature to the homogeneity of the nodes and leaves in the RF modelsing Eq.  where we20. For each feature, MDGI is the average value of all the decrease of Gini index over all the decision trees created in the RF model and higher MDGI value indicate elevated feature importance. Based on the MDGI we down-selected to 169 features with positive MDGI. The randomForest (ver. 4.6-14)40 package was used to estimate the MDGI values of the features.So, the Gini index values of 0 and 1 indicate completely homogeneous data and completely heterogeneous data, respectively. To find the feature importance, whenever a feature is used to divide data at a node, we calculated the Gini index at the root node and at both the leaves. The difference in the Gini index of splitting root node and weighted Gini index of the child nodes was estimated to find the fall of Gini index values in a decision tree of the RF model41 to the machine learning models using the training data for the reduced feature set to order the features by importance. RFE evaluates the training performance of a machine learning model for a feature set and gives the ranking of the features. We considered 10-fold cross validation with 5 repeats to evaluate the training performance of the machine learning models. We utilized caret (ver. 6.0-86)37 to implement the RFE algorithm.Following reduction of the number of features based on Pearson's correlation coefficient and MDGI values, we applied the RFE techniqueWe utilize the area under the receiver operating characteristic (ROC) curve (AUC) values to measure the training performance of the models via RFE for the reduced feature set. ROC curves use a combination of the true positive rate and false positive rate to provide a summary of the prediction capability of a machine learning model where a perfect classifier has an AUC of 1.0 and a random binary classifier will have an AUC of 0.5. We report the final test performance of our classifiers using the same metrics as previously reported for other AVP prediction algorithms, which include sensitivity, specificity, accuracy and MCC values Eqs. –6], wher, wher6)]https://github.com/pmartR/FIRM-AVP.All experimental data are available at p-value ~ 0.14). Thus, there is marginal evidence that the SVM yields a more confident identification, but it is not statistically significant based on this data at a p-value threshold of 0.05. However, when evaluating the negative class there is a significant improvement gained by the SVM. The average non-AVP peptide is generally correctly classified with a larger probability by ~ 0.09 . This difference in strength of classification of the non-AVP class is what is largely driving the reduction in false positives for the SVM, which is observed in the specificity values reported in Table The performance of the FIRM-AVP SVM, RF and DL models were compared based on the standard metrics of sensitivity, specificity, accuracy, and MCC for their performance on the validation/independent dataset where a positive AVP peptide is defined as a probability of greater than 0.5 and a negative AVP as less than or equal to 0.5. Evaluating overall accuracy, we observe that the SVM and RF models have very high AUC values, 0.962 and 0.958, respectively. Table 16 and the Chang et al.’s RF approaches (RFcompo + structure + agg)20 , as well as with the AVPpredWe performed RFE operations on the SVM model with the training data using 169 features from the initial feature selection with repetition measure the training performance of the SVM (in terms of AUC) via RFE algorithm. Note that the AUC values gradually decreased as features were removed from the model as depicted in Supplementary Fig. https://github.com/pmartR/FIRM-AVP. Additionally, a web-based version of the software is available at https://msc-viz.emsl.gov/AVPR/ . To use the web application the users need to provide either a single peptide sequence or a FASTA file of peptide sequences to be analyzed and predictions will be returned that include the probability that a peptide sequence is antiviral 42.We developed the standalone software tool, FIRM-AVP based on the SVM algorithm. The open source software are available at ral Fig. . As prev20. We achieved higher accuracies and MCC values relative to single classifier models that did not include feature reduction, as well as existing published models, demonstrating the effectiveness of the feature selection approach. The software tool FIRM-AVP based on our approach is publicly available for user with flexible options to not only make predictions, but to update the underlying prediction model. The need for more training data was a limiting factor to the DL approach, which had lower overall accuracy than the SVM and RF approaches.Identifying potential AVPs is of great importance for the discovery of new drugs to treat viral infection. In this work, we introduced a machine learning model for predicting AVPs using a core set of 169 features identified via correlation and machine learning analyses. Our SVM and RF models were developed based on the features generated from the AAC, DC, PseAAC, APseAAC, CTD, and predicted secondary structure properties of peptide sequences. To verify the effectiveness of our best feature sets, we tested the performance of our models using an independent dataset that included the same validation/independent as prior methods20 and HIPdb19. The observed significance of α-helical structure is consistent with the fact that many known antimicrobial peptides exhibit varied degrees of helical conformation and spatial partitioning of cationic and hydrophobic residues43. Here, both the SVM and RF approaches establish helix distributional features that are associated with antiviral peptides45. How these properties factor in peptide antiviral activity is not clear, however they are known to contribute to their interactions with cell membranes.We evaluated multivariate feature importance using our selected feature sets via RFE. Secondary structure and distribution features were identified as top ranked features in our SVM and RF models, respectively. Location oriented features for The discovery of new antiviral therapies is imperative to address the challenge of new viral epidemics and AVPs can be a valuable resource for the development of novel therapies to combat viral infection. One of the core needs is not only improving the accuracy of AVP prediction models, but also building explainable models that can aid in understanding the fundamental multivariate properties that are associated with anti-viral activity. This is a necessary step in the design of AVP design for novel viral systems.Supplementary Information.