Heme oxygenase-1 (HO-1) cleaves hemoglobin to form carbon monoxide (CO), biliverdin, and iron. HO-1 is a well-studied anti-inflammatory enzyme known to be elevated in various inflammatory disorders and targeting HO-1 and CO has been proposed a therapeutic modulation for inflammation. Measuring carboxyhemoglobin (SpCO) level via a finger sensor of a pulse CO-oximeter is an extremely easy, quick, and non-invasive test. Recent studies of the Coronavirus disease 2019 (COVID-19) have hypothesized that oxidative stress may be a factor in causing inflammation along with an increase in CO levels. This relationship between SpCO level and inflammation may also be present in ASD, as oxidative stress and inflammation have been recorded, which is worthy of exploring.

Gut microbiome dysbiosis and inflammatory mechanisms were reported to have a strong correlation in the pathogenesis of ASD. We previously reported a high correlation between gut microbiome dysbiosis and cytokine dysfunction (n_ASD=45; n_{Healthy Control, HC}=41). Probiotics administrations were reported to have anti-inflammatory effects and therapeutic potential in both animal and human studies. L. plantarum was found to stimulate serotonin and dopamine levels in animal studies and improve ASD behaviors in human studies. It is known that oxytocin (OT) signaling may serve as a critical link in the gut-brain axis, which may be regulated by the supplementation of probiotics, OT was widely reported to have anti-inflammatory effects and therapeutic potential, measuring OT level is of importance to monitor inflammation, ASD core symptoms and treatment response.

To further our understanding and clinical application of the concept of inflammation, autoimmunity, and gut-brain axis in the ASD population, we conducted this post-hoc exploratory analysis study of our recently published double-blind, randomized, placebo-controlled clinical trial with multiple related secondary outcome measurements to fill the gaps in the field. We explored the easily applied SpCO measuring HO-1, and the correlations with autoantibodies including AECA and Cunningham Panel, inflammatory serum markers including cytokine interleukin-1β (IL-1β) and brain injury markers (S100B, GFAP, MBP), OT serum level, microbiome profile, GI symptom severity, ASD core symptom severity and the treatment response to probiotics L. plantarum from reported clinical trial. We aim to assess the potential value of using easily administered pulse CO-oximeter by comparing the serum markers for early screening, diagnosis and subgrouping ASD, and guide corresponding treatment options which have already some reported efficacy, to facilitate early intervention and improve prognosis of ASD individuals.

Methods

Study Design and Participants

The original clinical trial design, protocol, randomization, blinding, participant eligibility, and intervention were well described in our previous publications: Kong X-J, et al. Probiotic and Oxytocin Combination Therapy in Patients with Autism Spectrum Disorder: A Randomized, Double-Blinded, Placebo-Controlled Pilot Trial. Nutrients. 2021; 13:1552. and Kong X-J, et al. Probiotics and oxytocin nasal spray as neuro-social-behavioral interventions for patients with autism spectrum disorders: a pilot randomized controlled trial protocol. Pilot Feasibility Study. 2020; 6:20, both incorporated by reference herein in their entirety and for all purposes.

In this study, we conducted an exploratory analysis and included all 35 subjects aged 3-25 with diagnosis of ASD from our phase 1 randomized, double blinded and placebo-controlled probiotics trial. Of the 35 subjects, the probiotics group received oral probiotics PS128 (Lactobacillus plantarum PS128, a total of 6×10¹⁰CFUs per day) while the placebo group received oral placebo (microcrystalline cellulose) for a total duration of 16 weeks. The study was conducted according to the guidelines of the Declaration of Helsinki. Ethical approval of the original study was issued by the Internal Review Board (IRB) of Massachusetts General Hospital (2017P001667) and this secondary use IRB (2020P004102). The original clinical trial was registered through ClinicalTrials.gov with the identifier NCT03337035 (registered Nov. 8, 2017; https://clinicaltrials.gov/ct2/show/NCT03337035). Written informed consent was obtained from either competent adult subjects, parents, or legal guardians of children and adults with cognitive impairment according to the IRB requirements. A timeline is presented in FIG. 1 to illustrate the sample size, assessed variables, and dropouts at each stage of the study.

Outcome Measures for the Analysis

Pulse Oximetry Measurements

SpCO was measured via pulse CO-oximetry which is a noninvasive technique that assesses the levels of various blood constituents, including SpCO, oxygen saturation (SpO₂), and heart rate (HR). Measurements with the Masimo devices were taken by placing a sensor on the subject's index or ring finger on either the right or left hands. This measurement was done in a clinical examination room over a two-minute period, where SpCO was recorded at 0 minutes and again at 2 minutes. These two records were then averaged for analysis. This measurement was collected during the primary trial but not included in previous publication. Based on previous studies of SpCO levels in cigarette smokers and non-smokers, non-smokers have been found to have SpCO values of <1.5%; as a result, a percentage of ≥2% is denoted as positive while a measurement of <2% is denoted as negative.

Blood Sample Collection and Circulating Serum Biomarker Measurements

Blood drawn and the serum processed during the original trial week 0 and week 16, was used to measure AECAs by an ELISA kit manufactured by R&D Systems Inc. (Minneapolis, MN, USA) following the manufacturer-supplied protocol, and sent to Moleculera labs (Oklahoma City, OK) for the Cunningham Panel measurement following their instruction. These two blood measurements were not included in our previous publication. In this study, we denote positive results for CaMKII activity, anti-Tubulin, anti-D1, anti-GM1, and anti-D2L using cutoffs cited by the testing facility (Cunningham Panel, Moleculera Labs). In brief, positive titer result cutoffs are determined based on the mean titer results of the healthy study population from the study conducted by Chain et al., such that anti-D1 titers with values of 2000 or higher, anti-D2L with values of 8000 or higher, anti-GM1 with values of 320 or higher, and anti-tubulin with values of 1000 or higher are considered positive results. Similarly, a positive result is denoted for CaMKII activation based on its activity with values of ≥130% above the basal mean activation rate. Circulating serum OT, MBP, GFAP, S100B, IL-1β measurements were described in our previous publication and designations of positive and negative cutoffs relevant to the current FIG. 2 for S100B, MBP, and IL-1β are determined based on whether detectable levels were identified in our subject population.

Other Outcome Measurements Included in this Analysis

GI symptom severity assessments by the validated GSI and the Bristol Stool Chart; Social Responsiveness Scale (SRS), Aberrant Behavior Checklist second edition (ABC-2), Clinical Global Impression (CGI), Stool Sample Processing, DNA extraction and sequencing analysis were well described in our previous publication (Kong X-J, et al. Probiotic and Oxytocin Combination Therapy in Patients with Autism Spectrum Disorder: A Randomized, Double-Blinded, Placebo-Controlled Pilot Trial. Nutrients. 2021; 13:1552, incorporated by reference herein in its entirety and for all purposes).

Bioinformatics Processing and Statistical Analysis

The analyses of a dataset generated from a double-blinded, placebo-controlled probiotic clinical trial was performed for the present study. Fecal 16S sequence reads were processed through Biobakery Workflows (v0.13.2) with default parameters using the VSEARCH-based method. Assignment of OTU taxonomies were done using the Greengenes database (v13.8) with 97% sequence similarity. The resulting reads were filtered using a prevalence threshold of 0.0001 and an occurrence threshold of 10%. Subsequently, the abundance data was transformed into relative abundances for downstream analysis. Linear discriminant analysis effect size (LEfSe) was used to evaluate differentially abundant microbiota relative abundances both at baseline and post-treatment using a one-against-all strategy for multi-class analysis with statistical significance considered at α=0.05 and a threshold for the logarithmic LDA score of 2.0. The 16s fecal microbiome sequencing data presented in this study are openly available in the Sequence Read Archive (SRA) database of The National Center for Biotechnology Information at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA675093, with accession number PRJNA675093. For assessment of correlations by placebo and probiotic treatment group effects, the absolute change between baseline (0-weeks) and post-probiotic intervention (16-weeks) were calculated for pulse oximetry, and serum inflammatory markers features. Serum autoantibody titers were log 2-transformed and correlational analysis was conducted using Kendall's rank correlation. Adjustment for multiple comparisons was not performed as each test was conducted according to pre-specified hypotheses. A table of all assessed correlations are provided in Table 1:

TABLE 1

Overview of all assessed correlations.

Baseline

Cunningham
Baseline
Baseline
Baseline
Baseline

Panel titers
SRS
ABC
CGI
GFAP

Baseline SpCO
*
*X

Change in SpCO
*X
*

Change in ASD
*X

severity

(CGI/ABC/SRS)

Change in GSI

*X

*X

Baseline α

*X
*
*

diversity

Change in α

*X
*X
*X

diversity

Change in OT

*
*
*X
*X

Change in

*
*X
*

GFAP

*Starred cells indicate all assessed correlations and “X” indicates a significant correlation identified within the present dataset.

Results

We conducted exploratory analysis based on a cohort of 35 children with ASD aged 10.26±4.78 years, which included 26 males and 9 females. The maternal age at childbirth is 32.96±4.9 years. Each participant was assessed for their GI symptom severity via the GSI, which suggested scores of 2.86±1.77. Furthermore, the severity of ASD symptoms were assessed via the ABC-2, SRS, and CGI. The ABC-2 yielded total T scores of 275.06±32.29, the SRS gave a total score of 113.8±36.96, and the CGI-S gave scores of 5.11±1.02. Detailed participant demographics and characteristics are summarized in Table 2. The 35 subjects were randomized assigned into two groups: probiotics group and placebo group. There were no significant differences between two groups in term of age, sex, ASD severity, and GSI as reported.

TABLE 2

Summary of participant demographics and clinical characteristics.

Demographic Feature
All (n = 35)

Age (years)
10.26 ± 4.78

Sex (n)

Male
26

Female
9

Maternal age
32.96 ± 4.9

GSI Index
2.86 ± 1.77

ABC-2 Total Score (T)
275.06 ± 32.29

SRS Total Score
113.8 ± 36.96

CGI-S
5.11 ± 1.02

We summarized the baseline levels of SpCO, serum autoantibodies, and serum inflammatory markers in this cohort. Based on such results, we found that the positive rate of the respective measurements are common and widely distributed (FIG. 2). Specifically, CaM kinase II and anti-tubulin are among the highest at 94.4% of subjects with elevated titers, followed by AECA with 82.6% of subjects with positive measurements, anti-dopamine receptor D1 (77.8%), SpCO (64.7%), S100B (64%), and anti-lysoganglioside GM1, and anti-dopamine receptor D2L both with 27.8% of subjects with elevated titers, IL-1β (24%), MBP (16%). Of note, GFAP and OT were not included due to lack of reference for the determination of cutoff values.

We found that the baseline SpCO was positively correlated with baseline SRS total scores (FIG. 3A, R=0.38, P<0.05), while the absolute change in SpCO post-intervention by treatment group was found to be positively correlated with baseline titers of anti-lysoganglioside GM1 among subjects receiving the active probiotic but not among those receiving the placebo control (FIG. 3B, R_Probiotic=0.83, P_Probiotic<0.05), which suggests that subjects with lower baseline anti-GM1 titers show a trend of decreasing SpCO post-probiotic intervention.

The absolute change in CGI-I, ABC-2 total score and ABC-2 stereotypic behavior sub-score between 0-weeks and 16-weeks were found to be positively correlated with baseline titers of anti-lysoganglioside GM1 in the probiotics-treated group but not in the placebo-treated group (FIG. 4A-C, P_Probiotic<0.05). Furthermore, the change in ABC-2 inappropriate speech sub-score was found to be positively correlated with baseline titers of anti-dopamine receptor D1 in the probiotics-treated group but not in the placebo-treated group (FIG. 4D, P_Probiotic<0.05). Similarly, the change in SRS motivation sub-score was found to be positively correlated with baseline titers of anti-tubulin among the probiotics-treated group but was not observed in the placebo-treated group (FIG. 4E, P_Probiotic<0.05). Lastly, the change in serum GFAP concentration was found to be positively correlated with baseline scores of ABC-2 inappropriate speech sub-score among subjects receiving the active probiotic but not among those receiving the placebo control (FIG. 4F, P_Probiotic<0.05).

The change in GSI index among subjects receiving the active probiotic was found to be positively correlated with baseline GFAP concentration (FIG. 5A; R_Probiotic=0.44, P_Probiotic<0.05), SRS awareness (FIG. 5B; R_Probiotic=0.45, P_Probiotic<0.05), and SRS communication sub-score (FIG. 5C; R_Probiotic=0.45, P_Probiotic<0.05). However, these corresponding correlations are not found to be significant among the subjects receiving the placebo control (FIG. 5).

Assessment of differentially enriched microbiota by group both at baseline and post-intervention suggested several significant genus-level differences in relative abundance. The presented LDA scores of each microbiota with respect to the interventional groups were found to be significantly different at both baseline (FIG. 6A) and post-intervention (FIG. 6B) between probiotics interventional group and placebo control group (FIG. 6, P<0.05).

Baseline Shannon index was found to be negatively correlated with baseline scores of SRS cognition and SRS communication (FIG. 7A-B; R=−0.31, P<0.05 and R=−0.3, P<0.05, respectively). The change in Shannon index was found to be positively correlated with baseline CGI-S, SRS total score, SRS sub-scores, including cognition and motivation, and ABC-2 sub-scores, including hyperactivity/noncompliance and social withdrawal among subjects receiving the active probiotic (FIG. 7C-H, P_Probiotic<0.05). The change in OT was found to be positively correlated with baseline measurements of CGI-S and GFAP among subjects receiving the active probiotic (R=0.46, P<0.05 and R=0.46, P<0.05, respectively) but these correlations are not observed among controls (FIG. 7I-J).

In the present study, we conducted an exploratory analysis to examine and compare multiple inflammatory markers including SpCO measured by pulse CO-oximetry, several serum autoantibodies, cytokines, brain injury markers, OT level, gut microbiome composition, the correlations among them, and their correlations with ASD core symptom severity, GI symptom severity and the treatment response to the probiotic strain L. plantarum in randomized, double-blinded, placebo-controlled clinical trial. We found that positive measurements of inflammatory/autoimmunity markers were common among the study cohort but with varying prevalence: CaMKII and anti-tubulin are among the highest at a positive rate of 94.4%, followed by AECA with a rate of 82.6%, anti-D1 titer 77.8%, SpCO 64.7%, S100B 64%, anti-lysoganglioside GM1 and anti-D2L titer both 27.8%, IL-1β 24%, MBP 16%, which indicated that autoimmunity to tubulin, D1, AECA and related ongoing streptococcus infection are extremely common in this ASD cohort, autoantibodies to lysoganglioside GM1 and D2L are with much lower presence, while cytokine IL-1β and brain injury markers such as MBP are even less prevalent. From the previous Cunningham panel related studies indicated that D2L and tubulin correlated to ASD severity. AECA was initially reported present in 30% of the ASD later reported significantly higher than control group and correlated with ASD severity. SpCO measured by pulse CO-oximetry was first explored in ASD population in this study, found to be abnormal in 64.7% of this ASD cohort, which is very close to inflammation rate found in the postmortem brains with multifocal perivascular lymphocytic cuffs contain increased numbers of lymphocytes in ~65% of ASD compared to control brains in males and females, across all ages, in most brain regions.

Interestingly, we first found that the SpCO level was significantly positively correlated with SRS total score at baseline, meaning SpCO could be indicative to ASD core symptom severity; Furthermore, the change of SpCO with intervention was found to be significantly positively correlated with serum GM1 level in probiotics treated group but not in placebo-treated group, which suggest those with lower level of GM1 had a greater improvement in the reduction of SpCO, which is hypothesized to be mediated through PS128 treatment. Similarly, the change in severity of ASD core symptoms by probiotics treatment were also found to be significantly positively correlated with GM1, and additionally with D1 and tubulin levels, these correlations were not found to be significantly associated among subjects of the placebo group. Taken together, these findings suggest that SpCO is a potentially promising autoimmunity/inflammatory biomarker for ASD. Considering the measurement of serum autoantibodies and inflammatory markers are requiring blood draw, not being able to perform in the local conventional labs, expensive price/mostly self-pay, SpCO measured by pulse CO-oximetry has a lot of advantage such as easy, quick (2-min), cheap, on-site, and completely non-invasive. We believe that SpCO could be promising to serve as a screening and diagnostic biomarker to indicate inflammation and autoimmunity therefore subgroup ASD for corresponding further testing or treatments, and also could be used for monitoring treatment outcome as we found in this study. As proposed, same as this promising application in ASD, SpCO could also be a useful quick test for COVID-19 to indicate inflammatory level, monitor severity, and clinical course, similarly for other conditions involved autoimmunity and inflammation. The validation of the application in ASD, specific correlations with each condition and involved pathways are warrant for further studies.

Some studies have shown a correlation between serum level of cytokines and gut microbiome composition, while others reported the serum level of autoantibodies are significantly correlated with ASD severity in both Cunningham panel and AECAs; however, the correlation between autoantibodies and gut microbiome and treatment response with probiotics were not explored in these previous studies. In the present study cohort, the change in GSI showed positive associations against baseline SRS score and GFAP level (P<0.05), suggesting that those with less severe ASD symptoms and lower GFAP levels show a greater extent of reduction in GI symptom severity, which are also potential better respondents to the administered probiotic. Moreover, the baseline gut microbiome alpha diversity was found to be negatively correlated with SRS cognition and communication sub-scores. Additionally, the change in alpha diversity over the intervention course was found to be positively correlated with baseline measurements of CGI-S, ABC-2 and SRS, while being negatively correlated with baseline anti-D2L titer among the probiotic group subjects. These associations, however, are not observed with statistical significance among subjects receiving the placebo control, which likely indicates that the associations between changes in gut microbiome diversity, ASD core symptom severity, and autoantibodies are likely mediated through the supplementation of the probiotic. The abundance changes of certain microbiome with probiotics intervention support the inflammatory mechanism of ASD and anti-inflammatory effect of probiotics. Following intervention, subjects receiving the active probiotic displayed higher abundances of Lactobacillus, and an unidentified genus belonging to the Clostridiales vadin BB60 group. Relative to the active probiotic group subjects, those receiving the placebo show higher abundances of an uncultured genus belonging to Ruminococcaceae and higher abundances of Escherichia-Shigella. While literature remains largely heterogenous regarding the clinical implications of the differentially abundant unidentified Clostridiales and Ruminococcaceae genera, there has been a suggestion of anti-inflammatory effects through the production of short-chain fatty acids (SCFAs) by the increased Lactobacillus genus. Furthermore, the observed higher abundances of Escherichia-Shigella within the gut microbiome in subjects receiving the placebo relative to that of the probiotic group suggests that the probiotic supplementation has the potential to suppress the dysbiosis of the Escherichia-Shigella genus, which is a well-known opportunistic pathogen and has been shown to be associated with higher severity in constipation among individuals with ASD. These interesting findings of this secondary analysis study displayed the internal relationship of autoimmunity/inflammation, gut microbiome dysbiosis and ASD severity, and the therapeutic role of probiotics in this setting. As mentioned earlier, OT signaling may serve as a critical link in the gut-brain axis, OT was widely reported to have anti-inflammatory effects and therapeutic potential. In this study, we found that the changes of OT was significantly positively correlated in the probiotic group with CGI-S and GFAP at baseline but not with placebo group subjects. OT was reported to be regulated by probiotics, and also interacted with other neurotransmitters and hormones. It's warranted to further explore autoimmunity mediated inflammatory process and gut microbiome dysbiosis, the therapeutic role of probiotics and other anti-inflammatory/immunomodulatory treatments.

There are several limitations of the study that deserve consideration. 1) Despite our adoption of proper recruitment and retention strategies, the participant enrollment and retention for this trial were challenging. This is a secondary analysis study from the parent trial, the sample size is smaller due to limited availabilities of the test results and high dropout rate for blood draw; A small sample size limited the statistical power and further subgroup analysis. 2) The wide age range used in this study resulted in high subject population heterogeneity and potentially variable treatment efficacy. Future studies with a larger sample size and subgroup stratification are warranted. 3) Due to considerable Asian and other minority patients with some cultural and language barriers, in addition to multiple influencing factors on behavioral variabilities, the parent rating of social behavioral scales may be somewhat biased. 4) Adjustments for multiple comparisons was not performed as each correlational test was conducted in the presence of a pre-specified hypotheses. Nonetheless, we acknowledge this as a limitation as the abundance of correlational tests performed may introduce possibilities of false discovery. Despite the several limitations of our analysis strategy, we believe that the presented evidence in the current study is an efficient way to provide suggestive evidence that may serve as preliminary results for future studies in the elucidation of the interactions between ASD symptom severity and biomarkers of inflammation and autoimmunity.

In the present secondary analysis of RCT study, we found a high rate of presence of serum anti-tubulin 94.4%, CaMKII 94.4%, AECA 82.6%, anti-D1 titer 77.8%, and SpCO 64.7% measured by pulse CO-oximetry in this ASD cohort, we first demonstrated that SpCO was positively correlated with ASD core symptom severity measured by total SRS score (p=0.029), the improvement of SpCO by probiotics was found positively correlated with baseline GM1 level (P<0.05) compared with placebo group. Similarly, ASD core symptom improvement with probiotics was also found to be positively correlated with GM1 and other autoantibodies. These findings indicated that easily administered, non-invasive SpCO a potentially promising autoimmunity and inflammatory biomarker to screen and subgroup ASD and monitor treatment response; The autoantibodies, gut microbiome profile, Serum OT level, GI symptom, and ASD corn symptom severity were all found to be highly correlated in probiotics treated group (P<0.05) compared with placebo-treated group from this study, which warrant further studies to improve ASD early diagnosis and treatment outcome.

Software and Computing

FIG. 8 shows an example 800 of a system (e.g., a data collection and processing system) for evaluating a subject is shown in accordance with some embodiments of the disclosed subject matter. As shown in FIG. 8, a computing device 810 can receive data pertaining to a measure of CO in a subject from a CO detector 800. In some embodiments, computing device 810 can execute at least a portion of a system for evaluating a subject 804 to obtain a level of at least one biomarker based on the CO measurement data received from the CO detector 800. Additionally or alternatively, in some embodiments, computing device 810 can communicate information about the CO measurement data received from the CO detector 800 to a server 820 over a communication network 806, which can execute at least a portion of system for evaluating a subject 804 to obtain a level of at least one biomarker based on the CO measurement data. In some such embodiments, server 820 can return information to computing device 810 (and/or any other suitable computing device) indicative of an output of system for evaluating a subject 804, such as a report based on obtaining the level of the at least one biomarker. This information may be transmitted and/or presented to a user (e.g., a researcher, an operator, a clinician, etc.) and/or may be stored (e.g., as part of a research database or a medical record associated with a subject).

In some embodiments, computing device 810 and/or server 820 can be any suitable computing device or combination of devices, such as a desktop computer, a laptop computer, a smartphone, a tablet computer, a wearable computer, a server computer, a virtual machine being executed by a physical computing device, etc. As described herein, system for evaluating a subject 804 can present information about the CO measurement and/or the report based on obtaining the level of the at least one biomarker to a user (e.g., researcher and/or physician).

In some embodiments, CO detector 800 may include a suitable light source and optics 802 for obtaining CO levels from the subject, e.g., through the skin. In other embodiments, light source and optics 802 can be local to computing device 810. For example, light source and optics 802 may be incorporated with computing device 810 (e.g., computing device 810 can be configured as part of a device for capturing and/or storing optical interferometric information). As another example, light source and optics 802 may be connected to computing device 810 by a cable, a direct wireless link, etc. Additionally or alternatively, in some embodiments, light source and optics 802 can be located locally and/or remotely from computing device 810, and can communicate information to computing device 810 (and/or server 820) via a communication network (e.g., communication network 806). In various embodiments, the light source and optics 802 may be disposed within a housing which includes the components of the CO detector 800 along with a processor, memory, and user interface.

In some embodiments, communication network 806 can be any suitable communication network or combination of communication networks. For example, communication network 806 can include a Wi-Fi network (which can include one or more wireless routers, one or more switches, etc.), a peer-to-peer network (e.g., a Bluetooth network), a cellular network (e.g., a 4G network, a 5G network, etc., complying with any suitable standard, such as CDMA, GSM, LTE, LTE Advanced, WiMAX, etc.), a wired network, etc. In some embodiments, communication network 806 can be a local area network, a wide area network, a public network (e.g., the Internet), a private or semi-private network (e.g., a corporate or university intranet), any other suitable type of network, or any suitable combination of networks. Communications links shown in FIG. 8 can each be any suitable communications link or combination of communications links, such as wired links, fiber optic links, Wi-Fi links, Bluetooth links, cellular links, etc.

FIG. 9 shows an example 900 of hardware that can be used to implement computing device 810 and server 820 in accordance with some embodiments of the disclosed subject matter. As shown in FIG. 9, in some embodiments, computing device 810 can include a processor 902, a display 904, one or more inputs 906, one or more communication systems 908, and/or memory 910. In some embodiments, processor 902 can be any suitable hardware processor or combination of processors, such as a central processing unit, a graphics processing unit, etc. In some embodiments, display 904 can include any suitable display devices, such as a computer monitor, a touchscreen, a television, etc. In some embodiments, inputs 906 can include any suitable input devices and/or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, etc.

In some embodiments, communications systems 908 can include any suitable hardware, firmware, and/or software for communicating information over communication network 806 and/or any other suitable communication networks. For example, communications systems 908 can include one or more transceivers, one or more communication chips and/or chip sets, etc. In a more particular example, communications systems 908 can include hardware, firmware and/or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, etc.

In some embodiments, memory 910 can include any suitable storage device or devices that can be used to store instructions, values, etc., that can be used, for example, by processor 902 to present content using display 904, to communicate with server 820 via communications system(s) 908, etc. Memory 910 can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory 910 can include RAM, ROM, EEPROM, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, etc. In some embodiments, memory 910 can have encoded thereon a computer program for controlling operation of computing device 810. In such embodiments, processor 902 can execute at least a portion of the computer program to present content (e.g., images, user interfaces, graphics, tables, etc.), receive content from server 820, transmit information to server 820, etc.

In some embodiments, server 820 can include a processor 912, a display 914, one or more inputs 916, one or more communications systems 918, and/or memory 920. In some embodiments, processor 912 can be any suitable hardware processor or combination of processors, such as a central processing unit, a graphics processing unit, etc. In some embodiments, display 914 can include any suitable display devices, such as a computer monitor, a touchscreen, a television, etc. In some embodiments, inputs 916 can include any suitable input devices and/or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, etc.

In some embodiments, communications systems 918 can include any suitable hardware, firmware, and/or software for communicating information over communication network 806 and/or any other suitable communication networks. For example, communications systems 918 can include one or more transceivers, one or more communication chips and/or chip sets, etc. In a more particular example, communications systems 918 can include hardware, firmware and/or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, etc.

In some embodiments, memory 920 can include any suitable storage device or devices that can be used to store instructions, values, etc., that can be used, for example, by processor 912 to present content using display 914, to communicate with one or more computing devices 810, etc. Memory 920 can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory 920 can include RAM, ROM, EEPROM, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, etc. In some embodiments, memory 920 can have encoded thereon a server program for controlling operation of server 820. In such embodiments, processor 912 can execute at least a portion of the server program to transmit information and/or content (e.g., results of a tissue identification and/or classification, a user interface, etc.) to one or more computing devices 810, receive information and/or content from one or more computing devices 810, receive instructions from one or more devices (e.g., a personal computer, a laptop computer, a tablet computer, a smartphone, etc.), etc.

In some embodiments, any suitable computer readable media can be used for storing instructions for performing the functions and/or processes described herein. For example, in some embodiments, computer readable media can be transitory or non-transitory. For example, non-transitory computer readable media can include media such as magnetic media (such as hard disks, floppy disks, etc.), optical media (such as compact discs, digital video discs, Blu-ray discs, etc.), semiconductor media (such as RAM, Flash memory, electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), etc.), any suitable media that is not fleeting or devoid of any semblance of permanence during transmission, and/or any suitable tangible media. As another example, transitory computer readable media can include signals on networks, in wires, conductors, optical fibers, circuits, or any suitable media that is fleeting and devoid of any semblance of permanence during transmission, and/or any suitable intangible media.

In some embodiments, the optical signals are detected by photodiodes. It should be recognized that any opto-electronic conversion device including but not limited to photo detectors, photodiodes, line-scan and two-dimensional cameras, and photodiode arrays can be used to perform this detection function.

It should be noted that, as used herein, the term mechanism can encompass hardware, software, firmware, or any suitable combination thereof.

FIG. 10 shows an example 1000 of a process for evaluating a subject in accordance with some embodiments of the disclosed subject matter. As shown in FIG. 10, at 1002, process 1000 can receive a measure of CO in a subject suspected of having at least one of autism spectrum disorder (ASD), autoimmunity, or inflammation, where the measure of CO may be received from a CO detector in communication with a carbon monoxide (CO) detector. At 1004, process 1000 can obtain, by the processor, a level of at least one biomarker associated with the subject based on receiving the measure of CO in the subject. Finally, at 1006, process 1000 can generate, by the processor, a report based on obtaining the level of the at least one biomarker.

It should be understood that the above described steps of the process of FIG. 10 can be executed or performed in any order or sequence not limited to the order and sequence shown and described in the figures. Also, some of the above steps of the processes of FIG. 10 can be executed or performed substantially simultaneously where appropriate or in parallel to reduce latency and processing times.

FIG. 11 shows an example 1100 of a process for assessing efficacy of treatment of a subject suspected of having at least one of Autism Spectrum Disorder (ASD), autoimmunity, or inflammation in accordance with some embodiments of the disclosed subject matter. As shown in FIG. 11, at 1102, process 1100 can conduct a first evaluation of a subject suspected of having at least one of ASD, autoimmunity, or inflammation. At 1104, process 1100 can conduct a first evaluation by receiving, by a processor in communication with a carbon monoxide (CO) detector, a first measure of CO of the subject. At 1106, process 1100 can further conduct a first evaluation by obtaining, by the processor, a first level of at least one biomarker associated with the subject based on receiving the first measure of CO. At 1108, process 1100 can further conduct a first evaluation by generating, by the processor, a first report based on obtaining the first level of the at least one biomarker. At 1110, process 1100 can conduct a second evaluation of the subject following administration of a treatment to the subject. At 1112, process 1100 can conduct a second evaluation by receiving, by the processor from the CO detector, a second measure of CO of the subject. At 1114, process 1100 can further conduct a second evaluation by obtaining, by the processor, a second level of the at least one biomarker associated with the subject based on receiving the second measure of CO in the subject. At 1116, process 1100 can further conduct a second evaluation by generating, by the processor, a second report based on obtaining the second level of the at least one biomarker. Finally, at 1118, process 1100 can compare, by the processor, the first evaluation and the second evaluation.

It should be understood that the above described steps of the process of FIG. 11 can be executed or performed in any order or sequence not limited to the order and sequence shown and described in the figures. Also, some of the above steps of the processes of FIG. 11 can be executed or performed substantially simultaneously where appropriate or in parallel to reduce latency and processing times.

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