Carcinoid heart disease (CnHD) is a frequent cause of morbidity and mortality in patients with neuroendocrine tumors and carcinoid syndrome. Although valve replacement surgery appears to decrease all-cause mortality in patients with advanced CnHD, few studies have investigated the outcomes of patients after valve replacement.We conducted a multi-institution retrospective registry of patients who received both tricuspid and pulmonic bioprosthetic valve (TV/PV) replacements for advanced CnHD from November 2005 to March 2021. Patients were followed post-operatively with echocardiographic studies every 3 months. Carcinoid valvular heart disease scores were used to monitor valve degeneration. Neuroendocrine tumor treatment, their administration times, and associations with echocardiographic findings were recorded.max was the first echocardiographic manifestation of valve degeneration in the setting of occult neurohormonal release. Post-operative telotristat ethyl and peptide receptor radionuclide therapy appeared to stabilize PV Vmax. The PV Vmax showed consistent elevation in the entire patient population when compared to baseline, while bioprosthetic TV echocardiographic parameters were relatively unchanged throughout. Post-operative warfarin therapy did not affect the rate of PV degeneration, and no major bleeding was recorded during or after post-operative anticoagulation therapy.Of 87 patients with CnHD, 22 patients underwent simultaneous surgical TV and PV replacement. In 6 patients (27.3%), increased PV VBioprosthetic valve degeneration is common in CnHD. Monitoring with echocardiographic studies every 3 months, focusing on PV velocities, could identify patients with occult disease that very likely promotes valve degeneration. Novel neuroendocrine tumor therapies may have a beneficial impact on valve degeneration. The incidence of neuroendocrine tumors (NETs) is on the rise  with a 2Although valve replacement surgery in patients with advanced CnHD has shown an all-cause mortality reduction , no postThis registry included all adult patients with biopsy-proven NET with carcinoid syndrome and severe CnHD who received valve replacement from November 2005, through March 2021 . PatientPatients underwent transthoracic echocardiography prior to and 3 months after their valve surgery; all patients underwent scheduled follow-up transthoracic echocardiography every 3 months thereafter. Standard echocardiographic equipment  was used for this purpose. In the pre-surgical and follow-up echocardiograms, the left ventricular ejection fraction was calculated using the Simpson biplane method of disks per European Association of Cardiovascular Imaging and American Society of Echocardiography recommendations . Right-sPatients were defined as having “occult disease” when subclinical valve changes were observed in the setting of controlled typical carcinoid vasomotor symptoms  and stable tumor size.Baseline demographics were obtained for all patients at the time of cardiac catheterization that preceded the valve surgery. Patients were enrolled in a monitoring program that included clinical evaluation and scheduled echocardiographic studies every 3 months post-operative. Outcome data were collected with manual chart review of electronic medical records.The American College of Cardiology/American Heart Association expert statement on CnHD indicates that transthoracic echocardiography (TTE) should be performed in all patients with carcinoid syndrome who have high suspicion of CnHD, those with any change in clinical status, or routinely every 3–6 months, depending on CnHD severity .The last echocardiographic study prior to valve surgery and the velocities recorded at that time represented the “pre-operative echocardiogram” for this study. The post-operative echocardiographic study at 3 months was considered the “baseline study” followed by scheduled echocardiographic studies and clinic visit every 3 months. All administered cardiac or oncological medications were recorded and summarized.max) and velocity- time integrals (VTI) were calculated using continuous-wave (CW) Doppler and pulse-wave (PW) Doppler echocardiography, respectively. At the time of each echocardiographic study, right and left ventricular systolic function, and blood pressure and heart rate were assessed.Valve peak velocity . Symptomatic CnHD was considered when patients had fatigue and/or dyspnea, lower extremity peripheral edema, pleural effusion, ascites, or any combination of these symptoms. The degree of TV regurgitation and PV regurgitation/stenosis were defined according to current echocardiographic guidelines and confirmed by two independent cardiologists .CVHD% score was given to each patient for each of their echocardiogram study. Expanding further upon the PV components of the CVHD score, we chose to mark patients with an increase of 20% PV Vmax in two consecutive studies as having a noteworthy increase in hemodynamics suggesting (bioprosthetic) degeneration.Lastly, the carcinoid valvular heart disease (CVHD) scoring method by Denney et al. was adapted to each patient’s echocardiography profile ; this meDuring surgery patients were placed on cardiopulmonary bypass and the beating-heart technique for valve replacement was utilized. The TV was inspected first. The anterior leaflet was excised, and the valve was measured before an Epic prosthesis was fitted. Next, the pulmonary artery was opened longitudinally, and the PV was inspected. The surgeon measured the valve and then excised the cusps before implanting the PV prosthesis into the orifice of the PV annulus. Additional left sided affected valves  were replaced as indicated and included in the post-operative monitoring program. All patients underwent pre-operative and intraoperative transesophageal echocardiography (TEE), with excellent correlation with pre-surgical transthoracic data. The same limitation as in transthoracic echo was exhibited in pulmonic valve visualization due to decreased forward flow.max increase greater than 20% observed in two consecutive echocardiographic studies, when compared to the 3-month follow-up echocardiogram , triggered additional advanced treatment of the underlying carcinoid disease while quarterly echocardiographic monitoring continued. Despite the initiation of anticoagulation upon observation of increased velocities, PV velocity continued to increase, triggering concern about uncontrolled neurohormonal disease rather than a valvular thrombotic event. Due to limited visualization of the pulmonic valve on the echocardiographic studies, clinical decision were based solely on the PV velocities. Survival status and the most recent follow-up date were determined from patient electronic medical records.Survival was measured in days after valvular surgery. Any PV Vt-test or Wilcoxon signed rank test, as appropriate. P-values of less than 0.05 were considered statistically significant. For the comparisons of echocardiographic parameters, Bonferroni adjusted alpha (0.05 divided by 11 = 0.0045) was considered as well. The overall survival time was defined as days until death or last known follow up after the initial heart surgery, and the survival probability was estimated using the Kaplan–Meier method. A Cox regression model with a time-varying covariate was used to evaluate the effect of post-surgical treatment on overall survival. Extended Kaplan–Meier method was used to illustrate the association between post-surgical treatment (as a time varying covariate) and mortality  or medians (with interquartile ranges) used for continuous variables and frequencies (with percentages) used for categorical variables. The pre-operative echocardiographic parameters were compared with the post-operative and 3 months (baseline) follow-up echocardiographic parameters using the paired ortality . Extendeortality , 18. ProFrom November 2005, through March 2021, 87 patients had CnHD and 34 patients were deemed surgical candidates; 22 of these 34 patients got simultaneous tricuspid and pulmonic valve surgical replacement for CnHD. There were 4 patients requiring additional AVR and one underwent replacement of all 4 valves. Patients were predominantly male with the majority being without cardiovascular comorbidities . For these 6 patients, telotristat ethyl treatment was the first line of treatment in patients deemed to have low tumor burdens, while peptide receptor radionuclide therapy (PRRT) was used in patients that had large tumor burden , 20 Fig. PRRT wap-values < 0.05). The first official post-operative follow up echo (done at 3 months post-op) marked the patient’s new “baseline echocardiogram,” accounting for a period of right ventricular function recovery and remodeling. At this point, the mean CVHD score, TV peak velocity, TV peak gradient were lower than the baseline values indicating improved heart function; changes in systolic blood pressure were not statistically significant. Mean PV Vmax, PV peak gradient, and PV mean gradient values were higher than the immediate post-operative values  at all follow-up echocardiographic points with no significant differences in CVHD scoring at 6 months irrespective of treatment with telotristat ethyl or PRRT . While Tp = 0.375).The median follow-up was 42.4 months , and the median overall survival was 32.4 months . A Kaplan–Meier survival plot for all patients under routine post-surgical observation-alone  is presented in max as the signal active “occult disease” despite stable tumor burden and controlled carcinoid syndrome symptoms, a phenomenon that might be best explained by recalling the bioactivity of these malignant neuroendocrine tumors: Before their biochemical breakdown, multiple vasoactive substances secreted by NETs, including 5-HT (serotonin), prostaglandins, histamine, bradykinin, tachykinins , and transforming growth factor-β, drive plaque deposition on the heart valves—predominantly the TV and PV on the right side of the heart—and a pathological cascade associated with worse outcomes in patients with CnHD  were initiated on warfarin for bioprosthetic thrombosis protection yet only completed 3 months of warfarin treatment following TV/PV replacement due to increased bleeding risk. Our experience reflects the concerns and limited use of mechanical valves that require long-term use of anticoagulation, challenging therapy in patients with abnormal coagulation profiles from liver metastasis and persistent hepatic congestion secondary to right-sided heart disease. In our population, patients with high disease liver burden and increased liver enzymes were not initially started on anticoagulation . After individualized risk/benefit discussion with the cardio-oncology team, patients with post-surgical increase in PV velocities were initiated on anticoagulation and, if velocities continued to increase, cancer treatment escalated to include advanced therapies . Notably, patients with CnHD may also require biopsies and tumor reduction procedures that pose additional risk while they are on anticoagulation therapy . Transvamax values, and the severity of valvular regurgitation) and occurred from 6 to 9 months to 2 years after surgery. One patient had a stepwise deterioration that required escalating her treatment regimen from telotristat ethyl treatment to PRRT treatment to achieve valve stabilization.Systematic echocardiographic monitoring played a major role in the post-surgical period. Echocardiography helped us identify the rather-unpredictable timing of the bioprosthetic valve degeneration –8. Pulmomax at the second (6 months post-op) follow up as well. These climbing values, in conjunction with the visualization of bioprosthetic PV degeneration on echo imaging, suggest that bioprosthetic PVs appear to be more vulnerable than TVs to degeneration from systolic neurohormonal/serotonin wash and increased velocities. This degenerative phenomenon has been noted in other studies of explanted PV autografts and has been thought to derive mostly from myofibroblast and endothelial stress (With the CVHD echocardiography scoring system parameters we have documented expected post-surgical changes in velocities, gradients, and severity levels of valvular involvement after bioprosthetic TV and PV replacement . Bioprosl stress , 30. We l stress . Early il stress .The standard monitoring systems or algorithms for the surveillance of valve integrity, and, possibly, neurohormonal suppression with advanced therapies may improve the longevity of bioprosthetic valves in patients with CnHD. The changes in post-surgical CVHD scores offer insights into ventricular remodeling, while changes in velocities underline the importance of monitoring bioprosthetic pulmonic valves for signs of degeneration in CnHD.There are no validated scores for bioprosthetic valves in CHD patients; we contemplated the existing available scoring systems for native valves and, to have a better quantitative/qualitative tool, we decided to use the Denney score , 32. BecBioprosthetic degeneration is frequently encountered in patients with CnHD after valve replacement, especially in bioprosthetic pulmonary valves. More aggressive tumor and neuroendocrine hormonal control alongside systematic monitoring with standardized echocardiographic studies every 3 months after valve replacement surgery could be major keys in prevention and early identification of patients at risk for severe valve degeneration. Extended valve monitoring leading to treatment with advanced therapies may help prevent late valve degeneration driven by disease progression. PV velocities represent an early indicator of valve degeneration and should be systematically obtained. Novel neurohormonal treatments with telotristat ethyl and PRRT could show promising bioprosthetic valve stabilization role.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by MD Anderson Ethics Committee. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.


Meiosis is undoubtedly the mechanism that underpins Mendelian genetics. Meiosis is a specialised, reductional cell division which generates haploid gametes (reproductive cells) carrying a single chromosome complement from diploid progenitor cells harbouring two chromosome sets. Through this process, the hereditary material is shuffled and distributed into haploid gametes such that upon fertilisation, when two haploid gametes fuse, diploidy is restored in the zygote. During meiosis the transient physical connection of two homologous chromosomes  each consisting of two sister chromatids and their subsequent segregation into four meiotic products (gametes), is what enables genetic marker assortment forming the core of Mendelian laws. The initiating events of meiotic recombination are DNA double-strand breaks (DSBs) which need to be repaired in a certain way to enable the homologous chromosomes to find each other. This is achieved by DSB ends searching for homologous repair templates and invading them. Ultimately, the repair of meiotic DSBs by homologous recombination physically connects homologous chromosomes through crossovers. These physical connections provided by crossovers enable faithful chromosome segregation. That being said, the DSB repair mechanism integral to meiotic recombination also produces genetic transmission distortions which manifest as postmeiotic segregation events and gene conversions. These processes are non-reciprocal genetic exchanges and thus non-Mendelian. Meiosis is a specialised type of cell division that results in the production of gametes (reproductive cells). During meiosis diploid progenitor cells undergo two sequential rounds of division without an intervening round of DNA replication. As a consequence, the hereditary material is halved to produce gametes, which are haploid meiotic products. Gametes in turn fuse during fertilisation forming a zygote which, like the parents, is diploid. During meiosis, chromosomes are also re-assorted and recombined, so that the gametes formed contain new chromosome configurations. The independent assortment of whole chromosomes already generates genetic diversity in the gametes. However, to shuffle the hereditary material within a chromosome, deliberate breakage of chromosomes  by the DNA topoisomerase-II-related transesterase Spo11 and subsequent repair into new combinations is required . Although both species are unicellular fungi (yeasts), they are actually not closely related to each other, as they had their last common ancestor roughly at the same time as nematodes and mammals had theirs  segregation patterns of wild-type vs. mutant marker genes Winkler . DissectS. cerevisiae that hypomorphic mutants of SPO11 can affect gene conversion tract length differentially between non-crossover and crossover outcomes  and ADE2 (coding for the AIR carboxylase) in S. cerevisiae, and their orthologues in Sz. pombe, ade7+ and ade6+, respectively , in bifactorial crosses parents with two different alleles  at the same test locus are crossed with each other. Bifactorial crosses have a distinct advantage in that since both parents are mutant, only progeny having undergone gene conversion can be wild-type for the associated phenotype. Therefore, this circumvents the need to perform tetrad dissection to identify gene conversion events unambiguously  are crossed to each other, and the shuffled distribution of the genetic variants in the progeny of such hybrid meioses can be used to analyse gene conversion and PMS, as well as crossover and non-crossover frequencies  genes does also affect overall gene conversion and crossover frequency , Mutsβ (Msh2-Msh3), and MutLα (Mlh1-Pms1) complexes is a major determinant of meiotic recombination outcome . All these events are initiated by programmed DSBs, but at which point in their further processing do the repair pathways leading to either crossovers or non-crossovers diverge? Answering this question will enable a detailed understanding of how given recombination outcomes are generated, which has profound implications for interpreting genetic data. Early models of meiotic recombination argued that crossovers and non-crossovers are produced by very different DSB repair pathways. Non-crossovers were considered to be the results of synthesis-dependent strand annealing Fig.  Resnick, and botS. cerevisiae revealed that non-Mendelian segregation tracts associated with crossovers and non-crossovers are considerably more complex than originally appreciated, but also more similar to one another  genetically separable from Mendelian ones (crossovers)? The short answer is, not really, apart from one partial exception. Genetic factors which direct or influence the formation and repair of DSBs will affect the frequency of both non-Mendelian and Mendelian segregation events, because these genetic outcomes are determined by the amount and type of meiotic recombination occurring at any given locus. Even the mismatch repair factors MutSα (Msh2-Msh6), Mutsβ (Msh2-Msh3), and MutLα (Mlh1-Pms1), which deal with mismatches in hDNA to produce gene conversions, and modulate the number of non-Mendelian segregation events at polymorphic sites by antirecombination, do also influence overall crossover frequency  complex in S. cerevisiae are substantially longer than in multicellular eukaryotes, and some factors regulating features of non-Mendelian events in S. cerevisiae are not conserved in Sz. pombe . The large evolutionary distance between the two model yeasts and their usefulness as experimental systems in meiosis research could be exploited comparatively to shed light on the conservation of the mechanisms enabling non-Mendelian segregation. Clearly, differences between homologous chromosomes in individuals within natural populations, influence where gene conversion events occur during meiosis, and whether they can be detected. This then also affects whether and how they contribute to genetic diversity in a given population. It will be important to bridge the knowledge gaps between molecular genetics, population genetics, and evolutionary biology, to arrive at a truly integrated model of meiotic recombination and its long-term role in generating genetic diversity.There are many aspects about the generation of non-Mendelian and Mendelian meiotic events that are still enigmatic. The extent to which the mechanism(s) governing non-Mendelian segregation events, gene conversion and PMS, are evolutionarily conserved is also not fully elucidated. There are clear differences between species. For example, gene conversion tracts in


GRIK1, EMX2, STXBP6, and KCNJ3 are disrupted in distinct cell-types. Moreover, we showed that ASD risk genes are predominantly distributed in certain human interactome modules, and that the identified genes may act as the regulator for some of the known ASD loci. In summary, our study demonstrated how tissue-wide cell-specific proteogenomic modeling can reveal candidate genes for brain disorders that can be supported by convergent lines of evidence.Autism spectrum disorders (ASD) are a set of complex neurodevelopmental diseases characterized with repetitive behavioral patterns and communication disabilities. Using a systems biology method called MAPSD  for joint modeling of proteome dynamics and a wide array of omics datasets, we identified a list of candidate ASD risk genes. Leveraging the collected biological signals as well as a large-scale protein-protein interaction network adjusted based on single cell resolution proteome properties in four brain regions, we observed an agreement between the known and the newly identified candidate genes that are spatially enriched in neuronal cells within cerebral cortex at the protein level. Moreover, we created a detailed subcellular localization enrichment map of the known and the identified genes across 32 micro-domains and showed that neuronal cells and neuropils share the largest fraction of signal enrichment in cerebral cortex. Notably, we showed that the identified genes are among the transcriptional biomarkers of inhibitory and excitatory neurons in human frontal cortex. Intersecting the identified genes with a single cell RNA-seq data on ASD brains further evidenced that 20 candidate genes, including  According to the US Centers for Disease Control and Prevention3, 1 out of 54 children are diagnosed with ASD across the US. ASD are known for extreme heterogeneity in their genetic components6 as well as 4:1 ratio of prevalence among men compared to women8. Such pervasive disorders can be attributed to distinct phenotypic subtypes. Accumulating evidences suggest that myriads of genetic signatures of ASD might converge to a tractable set of pathways or gene networks for effective therapeutic practices10. At genome level, common variants identified by genome-wide association studies12 as well as other genetic perturbations such as copy number variants, de novo mutations, and function-disrupting point mutations account the ASD liability14. Thus far, large-scale genome-wide studies such as iPSYCH project, with thousands of participants13, have brought about outstanding insights into the genetic architecture of ASD. However, there exists significant challenges to address such as spatial distribution and enrichment patterns of genetic hits across brain, cellular specificity of distinct brain regions, and complementary role of less studied omics data such as proteome granted that significant fraction of studies have focused on nucleic acids, i.e., genomics, epigenomics and transcriptomics rather than amino acids15.Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders predominantly characterized with behavioral and communication impairments. According to the American Psychiatric Association, autistic individuals share a spectrum of symptoms including difficulties with interactions with other people, repetitive behaviors, and inabilities to function properly in a daily life17, have remarkable impact on pinpointing genes and genetic variants associated with ASD. Moreover, transcriptomic20 and proteomic studies22 have significantly contributed to our understanding of the disease machinery. However, there is no clear agreement between gene expression levels and the abundance of their corresponding encoded proteins15 given that, at best, there is a 40–60% correlation between the mRNA and protein levels in certain organisms24. Such disagreements may arise from the fact that functionality of proteins are not solely determined by their abundances but by other biochemical or biophysical properties such as post-translational modifications and subcellular localization25. In spite of fascinating progresses, critical questions remain unanswered: are there any converging mediums to illustrate the confluence of small effect sizes of all of the loci, considering proteins as functional components of cellular machinery, how can we model biochemical and biophysical properties of proteins in parallel with genomic information to mimic the real circuitry of the disease and unmask novel loci which may participate in ASD development?, how we can create such a system level picture at wide range of cell-types across distinct brain regions?. Addressing these questions requires integrated mechanisms. Prikshak et al.10 had mapped ASD risk genes onto co-expression networks aimed at recovering developmental trajectories which represent fetal and adult cortical luminae. Notably, they had reported how FMRP (encoded by FMR1) regulation as well as co-expression of common transcription factors connect synaptic development with early transcriptional regulation10. Recently, Ramaswamy et al.26 reported an integrative study leveraging mRNA and miRNA expression, DNA methylation, and histone acetylation from ASD and control brains to uncover convergent molecular subtypes of ASD. Their research has led to a substantial expansion in the repertoire of differentially expressed (DE) genes as well as identification of highly enriched hyper-acetylated noncoding genomic regions. Additional integrated studies combining protein-protein interactions27 and gene expression suggest molecular convergence in subsets of ASD risk loci30. Yet, it remains an open question on how intra-cellular molecular interactions and protein trafficking paradigms within the cell in certain central nervous system (CNS) regions can be employed to identify highly-specific convergent gene modules to contribute to ASD etiology.Genetic association studies, such as GWAS, CNV, whole genome, and exome sequencing31, Markov Affinity-based Proteogenomic Signal Diffusion, for joint modeling of biochemical and biophysical properties of proteins with genomic and transcriptomic signatures of ASD aimed at identification of novel ASD candidate risk genes. Upon collecting multiple sources of omics data from the literature, MAPSD is able to generate tissue and cell-specific interactomes followed by diffusing the known biological signals throughout the protein-protein interaction networks (PPI) networks in order to predict unannotated susceptible genes. MAPSD is designed to be used in complex diseases where a wide array of biological data is available. A good example of such diseases is schizophrenia (SCZ) in which high polygenicity of the disease has been extensively studied in the literature32. Our previous study on SCZ had revealed a shortlist of novel disease associated candidate risk genes with a similar enrichment patterns in specific cellular micro-domains in neuronal cells in human cerebral cortex31. Given the availability of valuable resources including distinct molecular data on ASD, this is a great opportunity to account for the complexities of ASD through a novel systems-level approach. Our findings indicate the importance of parallel consideration of omics data-types in improving the understanding of the genetic architecture of ASD.To address these questions, we have employed our develop method called MAPSD31, a multi-omics data integration method, to identify additional candidate genes for ASD from existing knowledge. MAPSD first identifies omics information  on known candidate genes for a specific disease from various sources; it then receives PPI networks, subcellular localization of proteins within cellular micro-domains, and protein abundances across >130 different combinations of tissues and cell-types as well as a repertoire of other omics data-types followed by diffusing the accumulating signal intensities of available genetic signatures through tissue/cell-type adjusted PPI networks, to uncover disease-relevant genetic drivers of the disease with small effect sizes which cannot be captured using available single-omics pipelines  were highly enriched in neuronal cells within cerebral cortex while cerebral cortex shared the largest fraction of ASD risk genes . This observation supports how the identified genes and their protein products are similarly localized within subsets of cell-types in the brain compared to ASD risk genes. Some of these observations have been made in autistic individuals. For example, a study by Beheshti et al.39 shows how ASTN2, a gene associated with ASD through disruptive copy number variation, binds to surface of synaptic proteins affecting their trafficking leading to the modulation of synaptic strength. In addition, certain subcellular micro-domains to show a strong association with the disease in our study have been corroborated to be implicated in ASD. For instance, plasma membrane40, nucleus41, and cytosol42 have been recognized to implicate in ASD. We were interested to conduct a functional enrichment analysis to check what biological pathways are enriched for the set of the identified candidate risk gene. Using WebGestalt43, we performed a pathway enrichment analysis. We observed a number of pathways significantly enriched including: Neuroactive ligand-receptor interaction , SNARE interactions in vesicular transport , and Glutamatergic synapse . In fact, these pathways have been previously found to be involved in ASD46 providing further evidence on potential contribution of the identified risk genes to the disease.We were interested in finding where ASD risk genes localize in subcellular micro-domains. We used subcellular localization data from the Human Protein Atlastex Fig. . 82% of tex Fig. . We chec47, we mapped the ASD and MAPSD risk genes onto the GTEx data and obtained the enrichment P-values across all of the available tissues. First, for each gene, we obtained the tissue in which the gene shows the highest expression level. 14 regions related to CNS were included in this analysis including tibial nerves and 13 brain regions as follows: frontal cortex, cerebral hemisphere, cortex, spinal cord, CB, nucleus accumbens, anterior cingulate cortex, caudate, hypothalamus, substantia nigra, amygdala, putamen, and hippocampus. We found both ASD and the new gene set which had the highest signal intensities in the brain to be significantly enriched in frontal cortex, cerebral hemisphere, cortex, spinal cord, and nucleus accumbens  superior temporal cortex (area 22c) (STC), anterior  cingulate  cortex (MFC), dorsolateral prefrontal cortex (DFC), orbital frontal cortex (OFC), inferolateral temporal cortex  (ITC), hippocampus50, ventrolateral prefrontal cortex (VFC), primary auditory cortex (A1C), primary visual cortex (V1C), striatum (STR), primary motor-sensory cortex (M1C), posteroventral (inferior) parietal cortex (IPC), primary somatosensory cortex (S1C), CB, and mediodorsal nucleus of thalamus51. Then, we categorized the BrainSpan data into two large sets of prenatal and postnatal developmental stages. Prenatal stage includes: 0–12 post-conception weeks (pcw), 13–24 pcw, and 25–36 pcw. Postnatal stages include: 0–2 yr, 3–8 yr, 9–16 yr, and >17 yr. Next, we averaged the expression levels of the both gene sets across different stages of pre- and postnatal stages and looked for DE genes at FDR < 0.05 We were interested in further focusing on the top DE genes in each brain region, so we obtained the genes to be DE in all of the sixteen brain regions. Next, we checked all of the MAPSD brain-specific high intensity signal genes  and inhibitory neuronal (In) subtypes in the cortex, Purkinje (Purk) neurons and cerebral granule (Gran) cells as well as non-neuronal cells such as astrocytes (Ast), microgolia (Mic), oligodendrocytes (Oli) and their precursor cells (OPCs), endothelial cells (End), and pericytes. We obtained the list of the marker genes across six cell-types where their expression levels were significantly higher compared to other cells including: Exc neurons, In neurons, cerebral hemisphere cluster cells, Oli/OPCs, cerebral hemisphere Ast (CH-Ast), and cerebral hemisphere OPCs (CH-OPCs). We found that all of the MAPSD genes with the highest signal in the brain are highly enriched in all of these sets. Yet, the enrichment degress in neuronal cells such as In  was apparently higher than non-neuronal cells such as CH-OPCs  data from Lake et al.19 to intersect with the newly identified gene set. Notably, we found 20 genes to be DE across unique cell-types between ASD and normal brain tissues  database66 and queried these candidate genes for behavioral and neurological phenotypes that are relevant to ASD. 14 out of 21 genes showed phenotype annotations related to behavioral and neurological impairments as well as phenotypes related to nervous system. 10 genes were related to both categories including: ELAVL4, EMX2, MAGI2, GRIA4, UNC13B, CPE, GABRG2, GRIK1, GRM3, and MAGI1. Five genes were related either to behavioral/neurological impairments or phenotypes related to the nervous system including: ADGRL2, GPATCH8, KCNJ3, ASIC2, and KCNH5. For each of these candidates, we listed behavioral and neurological phenotypes in Table EMX2, MAGI2, GABRG2, GRM3, KCNJ3, and KCNH5. We observed hyperactivity/hypoactivity and impaired motor capabilities as relatively consistent behavioral patterns among these knockout mice while abnormal neuron physiology was shared predominantly as a common phenotype in the nervous system. We sought to investigate if any of these genes are associated with any Mendelian diseases. Using Online Mendelian Inheritance in Man (OMIM) database67, we queried the 15 genes showing phenotypic manifestations of neurological impairment in mice. We found five genes to be associated with some known Mendelian diseases including neurodevelopmental impairments seizure, and certain types of epilepsy and Parkinson’s disease  where proteomics has garnered less attention. At the proteome domain, there are several important aspects in ASD domain which have not been addressed such as biochemical and biophysical properties of proteins as well as their tissue/cell-specific characteristics which may affect PPIs and that how they can contribute to the disease onset and progression. In this study, using a novel single cell-based systems biology approach, we modeled intra-cellular protein trafficking patterns as well as subcellular localization of the proteins in tens of different tissues. We identified a new set of disease susceptibility genes followed by extensive examination of these genes to gather additional support of the findings.t = 4. The final signal intensities generated by MAPSD are real numbers and are calculated for every gene  in the network across all the tissues and cell-types. Then, the genes with the highest signal intensities in brain-related tissues or cell-types are picked as susceptible risk genes.With the advent of single cell-based technologies, exciting opportunities have been created for more accurate investigation of the disease machinery. Launching large-scale proteomic projects such as the Human Protein Atlas paved the way to make novel discoveries by modeling mutual correspondence between genome, transcriptome, and proteome. In this paper, we collected all of the available genomic, transcriptomic, and proteomic data on ASD followed by leveraging the single cell protein abundances from the Human Protein Atlas project. These multi-omic data were then fed to the MAPSD, a novel network-based systems biology method which models biophysical and biochemical properties of proteins including tissue/cell-specific protein abundances as well as subcellular localization of proteins in distinct cellular micro-domains. Our findings led to the identification of a list of new susceptibility genes that may contribute to the disease pathogenesis. Based on our analysis, the algorithm converged at the diffusion time 72. These regions have been recognized to play a key role in critical cognitive processes such as decision making and motivation74. An important finding was the similarity of enrichment patterns of known ASD risk genes and the newly identified genes by MAPSD. In addition, we found that the encoded proteins of the new candidate genes highly co-localize in certain cellular micro-domains including: cytosol, nucleoplasm, nucleus, and vesicles. These micro-domains have been indicated to be impacted during neurodevelopment leading to autistic phenotypes75. Applying MAPSD to the collected data, we observed that the identified candidate genes resemble the same localization patterns as known ASD risk genes. This creates a certain level of confidence about potential association of these genes with ASD which made us put them under extra tests. Given that ASD are a clinically heterogeneous class of neurodevelopmental disorders76, we were interested in studying the expression variations of these genes over the course of neurodevelopment. Considering 16 brain regions from BrainSpan, we showed that over half of the known ASD genes as well as the new identified candidate loci are DE between prenatal and postnatal stages. We found that the disrupted biological pathways by the candidate risk genes show an overlap with previous findings such as Glutamatergic Synapse, Cholinergic Synapse, Embryonic and Cardiac Development as well as signaling pathways such as Insulin and Neurotrophin Signaling pathways77. To further investigate the list of the identified candidate risk genes, we obtained the list of ASD-associated genes by Ramaswamy et al.26 which contained an expanded list of 5439 dysregulated genes in ASD. Comparing our findings with this set of genes, we observed a significant overlap . We sought to check if the overlapping gene set is enriched for any biological functions. We conducted a pathway enrichment analysis on the list of overlapping genes and observed multiple neuronal-related pathways including: retrograde endocannabinoid signaling , circadian entertainment , neuroactive ligand-receptor interaction , and glutamatergic synapse . Such an observation serves as additional evidence regarding biological relevance of the outcomes of this research.A key observation being made was the enrichment of known ASD genes in neuronal cells within the human cerebral cortex. This observation is in line with the previous findings regarding associations between cortices such as prefrontal cortex and anterior cingulate cortex with ASD19. We showed that 21 candidate genes which have not been captured previously by bulk transcriptional experiments were actually DE in specific cellular subpopulations in the brain. We sought to investigate the effect of subcellular localization on the outcome of MAPSD. For this, we assigned a weight of 1 to all of the edges in the PPI network and ran the algorithm. 291 genes showed the highest signal intensity in the brain after signal diffusion among them 101 genes were shared with the original list of genes identified by MAPSD (n = 962). Then we looked at the list of the candidate genes that were found to be dysregulated in the Velmeshev’s study19. However no overlapping genes were observed between the MAPSD results and their list of genes while 21 genes were found to be shared by the Velmeshev’s study when incorporating the subcellular localization information indicating the importance of considering localization information when running the model. We note that the outcomes of this study are merely a result of computational modeling without a functional assessment of the risk genes in diseases etiology. Moreover, MAPSD solely relies on molecular data to characterize potential disease-associated genes while not employing paired clinical information. Therefore, this is a limitation that the users of this method should consider.Using the gene expression data on 53 human tissues from the GTEx consortium, we made critical findings regarding spatial enrichment of ASD risk genes and the new identified loci. We showed that known ASD risk genes are highly expressed in specific brain regions including frontal cortex, cerebellar hemisphere, and nucleus accumbens. For the purpose of comparison, we repeated the same analysis of the new set of genes and observed an almost similar enrichment levels in the same areas. The only brain region where the new candidate gene set did not pass the significance threshold is CB where ASD risk genes showed a borderline statistical significance. Diving deep into the scRNA-seq from human brain tissue also further showed that a large fraction of the identified risk genes are in fact signature markers of inhibitory and excitatory neurons. An important discovery of this study was further validated by the scRNA-seq data from ASD brain by Velmeshev et al.In conclusion, using a novel systems biology technique, called MAPSD, we could successfully model proteome-specific information at the single cell resolution and pinpoint intra-cellular micro-domains where ASD risk genes-encoded proteins are enriched. As the outcome of the model, we identified a list of novel candidate loci whose disruption in mouse models increasingly leads to autistic phenotypic traits. We believe that the identified ASD risk loci as well as the systems biology approach used in this study paves the way to gain further insights into the genetic architecture of the disorder.https://github.com/adoostparast/MAPSD. The tissue-wide protein abundance levels and subcellular localization information are available in the MAPSD directory. MAPSD requires a list of genetic signatures (a sample is provided in the MAPSD GitHub directory) and a PPI network as well as proteomics information for adjusting the PPI networks. Users can readily use the proteomics data available at MAPSD GitHub account without any changes.MAPSD is a network-based systems biology method aimed at identifying tissue and cell-specific candidate risk genes in complex diseases. MAPSD models biochemical and biophysical properties of proteins along with other available molecular data collected by the user to create a signal intensity vector of the known signatures of the disease. Next, the created signal vector is diffused through the regulatory network or PPI network fed to the model followed by adjusting the signals given the tissue and cell-type of interest to identify candidate risk genes. MAPSD is available at S, is an overlaid column vector which contains the cumulative levels of biological evidences such as transcriptional signatures, methylation, GWAS, etc. For each level of information for a specific gene, we add a point 1 if there was a significant hit such as an FDR threshold of 0.05 on transcriptome signals and 5 × 10−8 for GWAS loci. To create S, first we introduce evidence matrix EG×L where G denotes the total number of genes and L is the number of omics data layers. Therefore:The initial signal matrix E, we can create S as follows: i is DE and differentially methylated, then Si = 2. To avoid generating spurious signals, we should make sure that the data being collected to create the signal vector have been generated from the same tissue or appropriate surrogate tissues.Next, using 71 included 1089 genes. Pre-calculated list of DE and differentially spliced ASD genes were obtained from PsychENCODE project36. This data are available on PsychENCODE Knowledge Portal under Synapse ID syn4587609.SFARI Gene set 3.0 is used for collecting genetic signatures of ASD which are categorized as rare single gene mutation, functional mutations, common variants, and syndromic loci. SFARI gene list48 and calculated the DE genes between prenatal and postnatal stages using t-test followed by false discovery rate correction (FDR < 0.05). To characterize spatial expression of genes in different brain regions, we used GTEx V878 and downloaded the processed RNA-seq data on the human brain. To characterize the ASD manifestations of disrupting the candidate risk genes, we used MGI database66. MGI contains knockout mouse models to investigate the phenotypic similarities between human patients and mouse models. We queried the candidate risk gene in MGI search tab and downloaded the resulting output.To study the expression patterns of genes during neurodevelopment, we download the RNA-seq data from the Atlas of the Developing Human Brain (BrainSpan)34, The Human Reference Interactome27, and human Interactome Database35. Next, duplicate interactions were removed leading to 232,801 unique interactions to be used in the study.Protein-protein interactions were collected from three sources including: PICKLE 2.3Supplementary Figure 1Supplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4Supplementary Table 5Supplementary Table 6


Extra-axial developmental venous anomalies (DVAs) are important anatomic structures that contribute to supplemental venous drainage of intracranial contents into the extracranial veins. We present the case of a 35-year-old woman with a sudden-onset severe headache, nausea, and vomiting who was found to have an atraumatic subarachnoid hemorrhage of left frontal convexity. Workup revealed a large anomalous extra-axial vein originating in the right frontal area, traversing the left frontal region, penetrating the left frontal bone just above the supraorbital foramen with likely drainage into the left external jugular vein. This vein could not be classified as an emissary vein given the lack of direct communication with the superior sagittal sinus anterior portion, which was found to be hypoplastic. This case report adds to the literature a description of a previously unreported midline traversing frontal extra-axial vein directly draining frontal lobes with a potential implication in an atraumatic subarachnoid hemorrhage of frontal convexity. Extra-axial developmental venous anomalies (DVAs) such as sinus pericranii, persistent embryonic sinuses, and emissary veins connect intracranial and extracranial veins . These vA 35-year-old woman without any significant past medical history presented with sudden-onset “thunderclap” headaches while singing on a teleconference call, with subsequent nausea and vomiting, without a clear history of significant head trauma. The patient denied any history of headaches prior to this event. Upon presentation, the patient had a Glasgow Coma Scale score of 15 with no focal neurologic deficits. The initial CT angiogram revealed a left-sided superior frontal sulcus hyperdensity consistent with a small area of subarachnoid hemorrhage (SAH)  was performed to further study the source of SAH. DSA revealed a large and tortuous anomalous vein originating in the right frontal area, traversing anteriorly and leftward into the left frontal region Figure , Panel FDifferential diagnosisWhile this venous anomaly most closely resembled an emissary vein , there wOutcome and follow-upThe venous anomaly was thought to be the most likely etiology of the patient’s SAH. During hospitalization, she was treated with nimodipine, magnesium oxide, blood pressure control, and supportive measures. On follow-up, the headaches improved, but she still reported a mild residual ache behind her eyes with nocturnal worsening. However, there were no other neurologic symptoms. She also reported avoiding sleeping flat due to concerns about exerting pressure on the left-sided venous anomaly. The patient did not report any symptoms with Valsalva maneuver or straining. Four-week follow-up non-contrast head CT demonstrated resolution of the previously noted SAH.Cerebral venous drainage occurs via the superficial and deep venous systems. The superficial bridging vein system provides venous drainage of the cortical regions, while the deep cerebral veins drain the deep gray and white matter . The orbeDVAs such as the emissary veins have been implicated in several physiologic processes and neurosurgical pathologies including the normalization of intracranial pressure , brain cSurgical approaches should be carefully planned, and pre-operative vascular imaging was reviewed taking into consideration the location and anatomic relationships of eDVAs to the bony anatomy and soft tissue structures, given the risk of complications in case of iatrogenic obliteration or manipulation ,16,17. GThe frontal extra-axial stand-alone midline traversing eDVA described in this report provides venous drainage of the frontal brain parenchyma directly into the external jugular vein and, to our knowledge, has not been previously reported. This case report contributes to the limited literature on frontal cerebral venous anomalies. Knowledge of anomalous veins and their anatomic associations is relevant for neurosurgeons and neurointerventionalists, given their potential role in brain physiology, pathology, and surgical complications.


Background: Accurate gastrointestinal (GI) lesion segmentation is crucial in diagnosing digestive tract diseases. An automatic lesion segmentation in endoscopic images is vital to relieving physicians’ burden and improving the survival rate of patients. However, pixel-wise annotations are highly intensive, especially in clinical settings, while numerous unlabeled image datasets could be available, although the significant results of deep learning approaches in several tasks heavily depend on large labeled datasets. Limited labeled data also hinder trained models’ generalizability under fully supervised learning for computer-aided diagnosis (CAD) systems. Methods: This work proposes a generative adversarial learning-based semi-supervised segmentation framework for GI lesion diagnosis in endoscopic images to tackle the challenge of limited annotations. The proposed approach leverages limited annotated and large unlabeled datasets in the training networks. We extensively tested the proposed method on 4880 endoscopic images. Results: Compared with current related works, the proposed method validates better results  on the challenging multi-sited datasets, confirming the effectiveness of the proposed framework. Conclusion: We explore a semi-supervised lesion segmentation method to employ the full use of multiple unlabeled endoscopic images to improve lesion segmentation accuracy. Experimental results confirmed the potential of our method and outperformed promising results compared with the current related works. The proposed CAD system can minimize diagnostic errors. Gastrointestinal (GI) tract cancers affect the human digestive system, resulting in one of the most critical healthcare problems ,2. AccorNowadays, automatic segmentation of lesions and anatomical structures in biomedical imaging has led to improvements in accurately diagnosing medical conditions. Advances in deep learning (DL), particularly convolutional neural networks (CNNs), have resulted in significant progress in several vision-related tasks ,9,10, frOne way of addressing this limitation is to use a semi-supervised learning (SSL) approach. SSL is a hybrid of supervised and unsupervised learning methods where a model is trained on a large dataset where only a small volume of the data is annotated. Typically, these data will be the targets linked with some samples . Thus, bThe lack of sufficiently labeled images for classical DL techniques means that there is the need for SSL techniques that (1) require little supervision  and (2) (1)We propose a GAN-based semi-supervised GI lesion segmentation framework that uses reasonably small labeled endoscopic images.(2)We demonstrate a full use of numerous unlabeled GI datasets to improve lesion segmentation accuracy.(3)The proposed framework was tested on five multi-sited datasets from different centers and integrated the predicted result to improve the segmentation performance through generative adversarial training.(4)The proposed method outperforms baseline supervised segmentation models as well as other related semi-supervised segmentation frameworks.Inspired by , this woThe next section of this work is organized as follows. In the following section, we describe the materials and methods of our proposed GAN-based semi-supervised framework. We collected 4880 GI images in total from a private hospital and publicly available sources to evaluate our method. The details of each dataset are as follows:We employed 2112 GI images of 484 patients collected and verified by gastroenterologists from the Digestive Endoscopy Center of the West China Hospital in Sichuan, China. The images were stored as RGB color channel in JPEG format. Original images were captured at a resolution of 384 × 384 pixels. Lesion areas were marked and labeled as ground truth (GT) by gastroenterologists. The GT images are stored as black and white binary images and also in JPEG format. From 2112 GI images, only 192 images were GT, and eighty percent of the datasets were used for training with the remainder used for testing. Approval from the medical ethical review committee of the University of Electronic Science and Technology of China (UESTC) and West China Hospital and informed patients’ consent were obtained.To ensure a robust framework, we used four related public datasets. Firstly, CVC-ClinicDB  data werThis study discusses an SSL method based on adversarial generative training techniques for GI lesions segmentation. Our framework uses a weighted amalgamation of losses.The general framework of the semi-supervised GI lesion segmentation is divided into four main consecutive steps, with the output of each step being the input to the subsequent step. The main detailed steps are explained as follows.Step 1: Preprocessingđ. Moreover, due to the varying nature of training images sizes, all training images were resized into 192 × 192 to fit the input model we used. All the resized RGB images were converted into a PNG file during Step 1.In the first stage, unnecessary background and artifacts were removed from each original dataset denoted Step 2: Supervised Training1đ, was used to train the network in this step in a supervised manner. The outputs of Step 2, i.e., the model weight from supervised learning, denoted 2đ, and the unlabeled datasets, denoted 4,,đ were forwarded to Step 3 for semi-supervised training.The preprocessed dataset from Step 1, denoted Step 3: Adversarial Training4đ, and the pre-trained model, 2đ. The segmentation probability map is generated by adversarial training in Step 3. The output, 6đ , will be reserved in Step 4, for further evaluation. Subsequently, the EN evaluates the input’s segmentation quality, which comprises the GT from the labeled images, and the predicted output from labeled and unlabeled images. The proposed network can accomplish adversarial learning between the SN and EN based on the test scores. Through adversarial training, the predicted maps generated from SN can be close to the GT. As shown in Step 4: Testing ModelIn Ŷ denotes the predicted maps. The model is implemented by reducing the following loss function as follows:ce), and adversarial loss calculated to approximate the performance from either the labeled or unlabeled images. The The loss function of the network is defined as in . Both thBCE loss during the supervised training with stochastic gradient descent (SGD) [https://www.tensorflow.org (accessed on 10 June 2021)) [We implemented the nt (SGD)  optimizee 2021)) . ExperimTP, FP, and FN represent the number of true positive pixels, false-positive pixels, and false-negative pixels in segmentation regions. a and b are points of A (GT contour) and B (predicted contour), respectively; d is a metric between these points. In this case, we take d as the Euclidian distance between a and b.The following five pixel-level evaluation metrics are utilized to compare the segmentation performance of the proposed method. Labeled datasets by physicians were used as GT; five different evaluation metrics were utilized to evaluate the performance, namely: Dice similarity coefficient (DSC), intersection over union (IOU), precision (Pre), recall (Rec), and Hausdorff distance (HDist). DSC is the most common metric for comparing the predicted segmentation and GT. IOU is a typical metric to measure a prediction, and it calculates the overlaps between the prediction and its corresponding GT . HDist mIn this part, we describe performance comparisons for baseline network trained in a supervised fashion and then the semi-supervised technique for adversarial training. Fixed images with GT were utilized for all unlabeled images and trained adversarially for lesion segmentation.After preprocessing, we compared the baseline framework with two state-of-art works: U-Net  and UNetWe compared our method with two recent related deep adversarial learning-based methods on our dataset to validate the segmentation accuracy. The two comparison methods were DAN  and GAN Moreover, our model can achieve better GI lesion accuracy on all testing datasets. Extensive experimental results confirm that the proposed method can produce promising segmentation maps. Usually, GI images’ appearance differs across generating equipment, with an effect on GI lesion . HoweverWe have performed several experiments with initial validation using two different methods. The first approach is based on a supervised way that includes U-Net  convolutThis paper proposed an adversarial learning-based semi-supervised segmentation framework for GI lesion diagnosis in endoscopic images. Automatic segmentation of lesions from endoscopic images is crucial to assist physicians in GI digestive tract disease diagnosis. We conducted intensive experiments using five different datasets and test data evaluated using five evaluation metrics. We explore a semi-supervised lesion segmentation method to employ the full use of multiple unlabeled images to improve lesion segmentation accuracy. Extensive experimental results demonstrated the potential of our method and outperformed other related works. A proposed CAD system can assist physicians and minimize diagnostic medical errors. Improving the approaches with more robustness and generalizability to other related semi-supervised learning tasks will be the basis of future work.


Severe acute respiratory infections (SARI) are the most common infectious causes of death. Previous work regarding mortality prediction models for SARI using machine learning (ML) algorithms that can be useful for both individual risk stratification and quality of care assessment is scarce. We aimed to develop reliable models for mortality prediction in SARI patients utilizing ML algorithms and compare its performances with a classic regression analysis approach.Administrative data (dataset randomly split 75%/25% for model training/testing) from years 2016–2019 of 86 German Helios hospitals was retrospectively analyzed. Inpatient SARI cases were defined by ICD-codes J09-J22. Three ML algorithms were evaluated and its performance compared to generalized linear models (GLM) by computing receiver operating characteristic area under the curve (AUC) and area under the precision-recall curve (AUPRC).The dataset contained 241,988 inpatient SARI cases . In-hospital mortality was 11.6%. AUC and AUPRC in the testing dataset were 0.83 and 0.372 for GLM, 0.831 and 0.384 for random forest (RF), 0.834 and 0.382 for single layer neural network (NNET) and 0.834 and 0.389 for extreme gradient boosting (XGBoost). Statistical comparison of ROC AUCs revealed a better performance of NNET and XGBoost as compared to GLM.ML algorithms for predicting in-hospital mortality were trained and tested on a large real-world administrative dataset of SARI patients and showed good discriminatory performances. Broad application of our models in clinical routine practice can contribute to patients’ risk assessment and quality management.The online version contains supplementary material available at 10.1186/s12931-022-02180-w. Severe acute respiratory infection (SARI) has been defined by the World Health Organization (WHO) in 2011 and is described by the following criteria: acute respiratory illness, history of fever (or measured fever of ≥ 38 degrees Celsius) cough, dyspnea (or tachypnoea), onset within the past 10 days, required hospitalization . AccordiThe capability of machine learning (ML) algorithms to predict patient outcomes has been studied among different disease entities . For exaIn regard of cardiovascular diseases, several studies with focus on risk stratification have been performed , 24 alsoDifferent case definitions exist to identify SARI patients from administrative data in a hospital setting considering that not all SARI-defining conditions can be assessed by this data source. In the above mentioned ICOSARI-sentinel, one approach used SARI-specific main and secondary diagnoses of ICD-10-codes  J09-J22 for case definition and proved to be sensitive . This mest 2016 to December 31st 2019. In-hospital death as the primary outcome measure of interest was identified via the type of discharge. ICD-10-GM-codes (German Modification of the ICD-10) as main and secondary diagnoses at hospital discharge were used to identify relevant comorbidities according to the Elixhauser comorbidity score without distinguishing between preexisting comorbidities and newly diagnosed conditions [Our dataset included administrative data from 86 hospitals within the German Helios network. Inclusion criteria were 1) inpatient treatment and (2) SARI as main or secondary diagnosis as defined by ICD-10-codes (see above). We retrospectively analyzed urgent or regular patient admissions from January 1nditions , 29. A d inpatienThe methodological approach presented here was successfully applied to a dataset of HF patients before and was used similarly for this analysis . The iniIn a first step, we evaluated and cross-validated two different variable sets based on the training dataset: one contained Elixhauser comorbidities as separate variables and one contained the Elixhauser weighted comorbidity scores .Variables which were highly sparse and unbalanced (near-zero variance variables ), were rThe two variable sets were evaluated using four different algorithms applied on the training dataset: GLM, random forest (RF), single layer neural network (NNET) and extreme gradient boosting (XGBoost).Model tuning was carried out in accordance to previous descriptions  using a The final dataset included 241,988 SARI cases from 86 Helios hospitals. Baseline characteristics are summarized in the  were tuned keeping the following values :The cross-validation during model training showed a slightly better performance of the ML models when compared to GLM . The best-performing algorithm was XGBoost . The models containing separate Elixhauser comorbidities turned out to be superior to the Elixhauser score model among all algorithms used and were therefore kept during model testing. Plots of the SHAP analysis depicting variable importance for each algorithm are available from the Additional file Applied to the testing cohort, the ML models did not markedly outperform GLM. Yet, a marginal better performance could be demonstrated for all three ML models, but confidence intervals (CIs) overlapped with those of GLM. AUCs and corresponding AUPRC with 95%CIs are given in Table In this study, we present real-world administrative data on in-hospital mortality of 241,988 patients with SARI which is derived from a nationwide German hospital network. Different ML mortality prediction models displayed an overall good discriminatory performance with respect to AUC and AUPRC. Compared to standard statistical methods (GLM), NNET and XGBoost showed a small but statistically significant difference in ROC AUCs. However, the relevance of this marginal better performance remains unknown from a clinical perspective and warrants further evaluation. Future studies are therefore needed to explore the usefulness and advantages of ML concepts in the context of outcome prediction.The results highlight comorbidities as important influencing factors with respect to SARI-related deaths. Implementation of our mortality prediction models, utilizing only easily and widely available variables, in clinical care can help assessing the patients’ individual mortality risks and could moreover be useful for hospital benchmarking. We chose to include patient data from 2016 to 2019 for our analysis as the COVID-19 pandemic could have been a major influencing factor with regard to SARI mortality in 2020. This assumption should be investigated in future analyses also relating to a possible scalability of our proposed mortality prediction models in view of the ongoing pandemic.Mortality data for SARI can be derived from large-scale prospective studies. In the globally conducted SPRINT-SARI trial, overall mortality rate is given as 9.5% and in patients > 60 years of age as 18.6% which is comparable to our findings  and may be an indicator of the good reliability of our retrospective claims-based dataset. Organ dysfunction as assessed by SOFA-scores  at initial patient presentation and increased age were identified as independent predictors of in-hospital mortality in this study . The higIn a recent analysis of the ICD-code based ICOSARI-sentinel , 5-year In another presentation of ICOSARI-data, the investigators reported unexpectedly low numbers of influenza (defined by ICD-codes) among SARI cases in general which is in accordance to our findings  comparing different models with the A-DROP score, a modified version of the CURB-65 score , by addiWith regard to ML application for outcome prediction in patients with respiratory diseases, Hu et al. presented a retrospective study on 336 cases with severe influenza. XGBoost and RF algorithms provided an AUC of 0.842 and 0.809 in predicting 30-day mortality and outperformed LR and certain clinical prognostic scores  which highlights the usefulness of ML for outcome assessment also in critically ill patients . From ouAnother interesting approach to predict patient-specific mortality in CAP was reported by Wu et al. It comprised disjunctive normal forms learning algorithms which were compared to ML with promising results . HoweverWe assessed the predictive abilities of our algorithms not only with ROC AUC but also with AUPRC and calibration plots. The two best performing algorithms (XGBoost and NNET) also showed very good calibration Fig. , Table 3As mentioned before, several well-established risk scores for SARI patients exist which represent important clinical tools and can help treating physicians to assess SARI severity and the individual mortality risk at the time of the patient’s hospital admission, for example in an emergency room setting. The more complex PSI which comprises comorbidities, clinical parameters and results from laboratory analyses and instrumental examinations tends to provide better accuracy in predicting 30-day mortality when compared to CURB-65  and A-DROur proposed mortality prediction models should be broadly applicable in clinical routine practice as administrative data is commonly available in hospital information systems (HIS). Automatic data extraction and implementation of risk score calculators in the HIS is conceivable. Individual risk prediction at the time point of the patients’ hospital admission or after a SARI diagnosis is established during a hospital stay could assist the physician in estimating disease severity. For CAP, it has been shown that this initial assessment of disease severity is crucial . DiffereIn a population-based approach, these models can furthermore be used to calculate standardized mortality ratios for different patient cohorts, differentiated for instance according to specific geographic regions, time periods and hospitals and can hence serve as a basis for quality of care evaluation and assurance. However, external validation of our models among different patient cohorts is required to prove applicability and its benefits.We acknowledge several limitations in connection with this study. First, we used retrospectively collected data only which is widely seen as of inferior quality in comparison to prospective studies. However, as has been shown above, mortality rates in our dataset did not differ markedly when compared to prospective studies. Second, some limitations must be attributed to claims-based datasets in general, as the collected data is not stored for research purposes but for administrative and remuneration reasons. The validity of the datasets is dependent on correct coding and cannot always be ensured if no control variables exist  as has been stated before . HoweverOur results show that the application of ML algorithms together with the use of routinely available administrative data is feasible for mortality prediction in SARI patients. In a large real-world multicenter cohort, ML approaches performed slightly better when compared to regression analysis. Implementation of our models into a clinical or quality management context could contribute decisively to risk stratification and hospital benchmarking respectively and ultimately could improve the quality of patient care.Additional file 1: Table S1. ICD-10-GM-codes used to calculate Elixhauser comorbidity score . Figure S1. SHAP (SHapley Additive exPlanations) analysis for variable importancee et al. ). Table


Fat infiltration (FI) of the paraspinal muscles (PSMs) measured using MRI is an aspect of muscle quality and is considered to be worse in chronic low back pain (cLBP) patients. However, there is not a clear association between paraspinal muscle FI and cLBP, leaving the clinical importance of paraspinal muscle composition unestablished. The spatial distribution of FI in the PSMs may inform mechanistic understanding of non-specific cLBP as it relates to degenerative intervertebral disc (IVD) pathology. We hypothesized that paraspinal muscle fat-mapping would reveal distinct FI distribution patterns in relation to cLBP symptoms and proximity to symptomatic IVD degeneration.From advanced-sequence water-fat MRI of 40 axial cLBP patients and 21 controls, we examined the spatial distribution of paraspinal muscle FI in relation to the center of rotation at the L4L5 disc. Using statistical parametric mapping, we compared FI patterns for multifidus (MF), erector spinae (ES), and psoas between patients and controls, and to the presence and severity of adjacent degenerative IVD pathology.The spatial distribution of PSMs FI differs between PSMs and according to symptoms and the adjacent degenerative IVD pathology. Furthermore, the region of MF closest to the disc center of rotation appears most susceptible to FI in the presence of symptomatic IVD degeneration.Our study identified spatial distribution patterns of FI in the PSMs as a potential diagnostic biomarker that may also provide granular mechanistic insights into spine biomechanics related to cLBP, as well as advancing the use of prior summary measures limited to overall muscle FI.The online version contains supplementary material available at 10.1007/s00586-022-07296-7. Chronic low back pain (cLBP), the world's most disabling condition, is the highest (non-cancer) reason for opioid prescription in the USA, and its rates are on the rise with the aging population . While aMRI-based characterization of paraspinal muscle composition by quantifying fat infiltration (FI) is a popular approach for assessing muscle quality for spine conditions. However, the relationship between paraspinal muscle FI and cLBP is not straightforward and the underlying mechanistic structure–function relationship remains poorly understood despite advances in quantifying both paraspinal muscle FI and degenerative IVD pathology , 6. IncoTo investigate whether spatial distribution of paraspinal muscle FI relates to symptoms and adjacent degenerative IVD pathology, we generated paraspinal muscle ‘fat-maps’ using axial MRI and referencing the center of rotation (CoR) at the L4L5 disc segment. We hypothesized that paraspinal muscle fat-mapping would reveal distinct FI spatial distribution patterns in relation to cLBP symptoms and proximity to the CoR of a symptomatic IVD degeneration. Due to its key role in spinal segment stabilization, we expected that the region of multifidus closest to the CoR would display the highest fat content adjacent to symptomatic IVD degeneration.With IRB approval  and informed consent, 3 T multi-echo MRI was collected for 40 cLBP patients and 21 age- and sex-matched controls. Patients with more than three months of LBP (VAS ≥ 4 or ODI ≥ 30) and between ages 18 and 70 were included. Exclusion criteria for enrollment included pregnancy, diabetes, smoking, cancer, spondylolisthesis, scoliosis, prior lumbar surgery, disc herniation, compression fractures, taking osteoporosis medication. Controls reported no prior history of back pain (VAS ≤ 1) or known spinal conditions. VAS scores were collected once, at baseline, directly prior to the subject’s MRI visit.1ρ and T2 mapping [P < 0.001; inter-rater ICC: 0.99, P < 0.001; see [Lumbar scans were performed on a 3 T scanner and sequences included standard clinical T1- and T2- weighted MRI sequences and advanced-sequences used for cartilage endplate detection (high-resolution 3D ultrashort echo time (UTE) ), disc c mapping ), and FI mapping ). Specif mapping –15). Car mapping . Modic c001; see  for fullwww.python.org).Our novel fat-mapping method is depicted in Fig. The mean FI% of the whole muscle  and the fat-maps were separately compared between patient and control groups and further stratified based on spinal pathology measurements from the adjacent disc segment. Statistical analyses utilized unpaired t-tests (depending on groups and conditions compared) to compare overall mean FI% values. For the fat-maps, we utilized statistical parametric mapping (SPM) to conduct statistical analyses across the entire fat-map. In prior studies, SPM has been used to analyze regional differences in intramuscular damage within the quadriceps femoris . We calc2) and controls . The percent representation of females to males was 50.0% for the cLBP patients and 47.1% for the control group. There were no significant differences in either age or BMI across any two groups separated for statistical analysis. The overall mean FI% in the multifidus and psoas were significantly greater in patients compared to controls . Similarly, SPM identified specific regions with higher FI% in patients versus controls, specifically in the deep multifidus and intermediate to superficial psoas  concentrated in the deep to intermediate regions of the multifidus (p = 0.013). The highest peak in FI for psoas was 4.6 percent points higher for patients  compared to controls . There was no significant difference in peak FI for erector spinae between patients  and controls .The percent of individual muscles within our sample that had their highest peak of FI in the deep region was 99.2% for multifidus, 96.7% for erector spinae, and 33.6% for psoas Fig. . The higp = 0.02–0.04), but low VAS was not different from either high VAS or controls. There were no differences in fat-maps for high and low VAS with controls for erector spinae or psoas.When comparing fat-maps for PSMs between high (> 6) and low (≤ 6) patient-reported pain (VAS), we observed trends between fat-map patterns progressing from controls with no pain to cLBP patients with high VAS Fig. . For mulUsing a novel application of fat-mapping analysis, we identified different spatial distribution patterns of FI% between and within the PSMs and in relation to symptoms and degenerative IVD pathology. First, with respect to cLBP symptoms alone, we identified differences in whole muscle and spatial distribution of FI% for multifidus and psoas, with different PSMs showing distinct regions of peak FI% in regard to the IVD CoR. Second, when examining differences based on degenerative IVD pathology, we found that cLBP patients with CEP damage and higher IVD degeneration (PG) had larger regions of significantly higher paraspinal muscle FI% compared to patients without these pathologies. Furthermore, fat-maps from cLBP patients with low IVD degeneration and no CEP damage did not differ from controls. Lastly, the shape of the spatial distribution of FI% relative to the IVD showed a significantly higher peak of FI in multifidus between patients and controls located in the deepest part of the muscle closest the CoR. When comparing the shape of the fat-maps between patients with relatively high and low self-reported pain, we identified a progression of increasing FI% specifically in the deep region of multifidus. Our results suggest that 1) spatial distribution patterns of FI in the PSMs differ with respect to symptoms and adjacent degenerative IVD pathology and 2) that the deep region of the multifidus has the highest amount of FI in relation to symptomatic IVD degeneration.Prior studies seeking to associate paraspinal muscle FI with cLBP have shown an inconsistent relationship between FI and patient-reported symptoms or outcomes . This maIt is unclear how or why PSMs become infiltrated with fat in cLBP patients; however, changes in loading on different muscle regions or fascicles may introduce susceptibility to FI. Muscular dystrophy patients show predominant accrual of FI in muscle attachment sites. In the rotator cuff, unloaded muscle tissue post tear has higher FI . Our finOur results reveal the highest distribution of FI in the deepest multifidus region in relation to a symptomatic IVD degeneration in cLBP patients. Of the structural changes that PSMs are thought to undergo in the transition from acute to chronic LBP, FI arguably occurs slower than atrophy and is therefore considered a characteristic of chronicity . ParaspiWhile our results show an association between cLBP symptoms and increased FI in the intermediate and superficial psoas, prior studies have not found such an association. The relationship between psoas FI and cLBP is inconclusive. In part, this may be a result of the relatively low levels of FI in the psoas. Unlike the multifidus and the erector spinae, we found little variation in FI levels across the distribution. As the spatial differences in the psoas are much smaller, there is more ambiguity as to potential underlying mechanisms, and the differences in FI may be more affected by confounders such as age, sex, BMI, or activity levels.Our study limitations should be considered in wider interpretation. We limited our analysis to a single spinal level (L4L5) that, while prevalent in IVD pathology, may limit generalizability to other lumbar levels where different sizes and orientation to the CoR may influence results. With respect to confounding factors, studies have shown increased fat infiltration with age in muscles of the extremities and the trunk, including the paraspinal muscles, which may be independent of sarcopenia and muscle mass changes . FurtherIn conclusion, our results suggest that: 1) spatial distribution patterns of FI in the PSMs differ with respect to symptoms and adjacent degenerative IVD pathology; and 2) that the deep region of the multifidus has the highest amount of FI in relation to symptomatic IVD degeneration. We also show that spatial distribution patterns of FI in the PSMs provide higher granularity and thus serve as a better diagnostic imaging-based biomarker compared to conventional summary measures for overall muscle FI%. Based on these results, we theorize that the presence of symptomatic degenerative disc pathology results in arthrogenic inhibition and a selective shutting off of the local stabilizing muscles, notably the deep multifidus, as a protection mechanism, resulting in disuse and eventually, elevated levels of fat infiltration localized to the shorter, deeper fascicles of the multifidus. Future study should look to collect longitudinal data to further explore causation. With regard to the multifactorial and uncertain nature of diagnosing patients with non-specific cLBP, establishing mechanisms of how paraspinal muscle FI occurs and understanding its relationship to painful symptoms and degenerative IVD pathology will guide the development of effective conservative therapies targeting paraspinal muscle health.Supplementary file1 (DOCX 29 kb)Below is the link to the electronic supplementary material.


The last two decades have brought ground-breaking advances in genetics, culminating in deep profiling of the human genome and high resolution detection of genetic variants. These developments have rewired preclinical and clinical efforts in understanding cancer, paving the way for precision therapies . LymphomThe first targeted agent approved for treating B cell lymphoma was the monoclonal antibody rituximab (R), which has significantly improved patient survival in combination with chemotherapy (R-CHOP), compared to chemotherapy alone . FollowiMonoclonal antibodies conjugated to cytotoxic payloads via a chemical linker, so-called antibody-drug conjugates (ADCs), represent a promising advancement on monoclonal antibodies, since they combine the tumour-homing function of a monoclonal antibody with a highly cytotoxic payload . As ADCsThe development of novel therapeutic agents aimed at disrupting well-defined oncogenic signalling pathways has also been extensively explored in the last decade. Small molecules targeting kinases have shown robust pre-clinical and clinical activity in lymphomas, leading to the approval of three Bruton’s tyrosine kinase (BTK) inhibitors and three phosphatidylinositol 3-kinase (PI3K) inhibitors ,26,27,28Another recently developed treatment strategy involves targeting adaptive immune escape mechanisms to unmask cancer cells and in turn reactivate immune surveillance. Monoclonal antibodies targeting PD-L1, PD-1 and CTLA-4 are the big players in this approach, but while they have been game changers in many solid tumours, lymphomas have failed to show robust responses. The factors governing the lack of response, as well as early progression in lymphoma patients treated with immune checkpoint therapy, remain obscure, but might be related to an unfavourable tumour microenvironment (TME) and/or genetic alterations that favour immune escape. Notorious exceptions to this are Hodgkin’s lymphoma and primary mediastinal B cell lymphoma ,31, likeThe classes of targeted agents presented here, and their mechanisms of action, demonstrate that we are on the cusp of a new molecular era, whose success will require a deeper understanding of the processes underlying lymphomagenesis. Much more work needs to be done to identify predictive biomarkers and develop simpler platforms for the routine identification of molecular subgroups. Our improved understanding of these mechanisms will inform the design of more effective therapeutics with less toxicity, which target synthetic vulnerabilities while promoting antitumour immunity.We have just begun to decipher the molecular fingerprints of B cell lymphomas and, as such, routine personalised medicine is still a long way off. However, the strides made thus far are encouraging and fuel an expectation for the future implementation of personalised treatments in standard clinical practice.


Studies including, but not limited to, STICH, STICH-II, MISTIE, MISTIE-II, MISTIE-III, ENRICH, and ICES have all shown that, in certain limited patient populations, evacuation can be done safely and mortality can be decreased, but functional outcomes remain statistically no different compared to medical management alone. Only 10–15% of patients with ICH are surgical candidates based on clot location, medical comorbidities, and limitations regarding early surgical intervention. To date, no clearly effective treatment options are available to improve ICH outcomes, leaving medical and supportive management as the standard of care. We recently identified that remote ischemic conditioning (RIC), the non-invasive, repetitive inflation-deflation of a blood pressure cuff on a limb, non-invasively enhanced hematoma resolution and improved neurological outcomes via anti-inflammatory macrophage polarization in pre-clinical ICH models. Herein, we propose a pilot, placebo-controlled, open-label, randomized trial to test the hypothesis that RIC accelerates hematoma resorption and improves outcomes in ICH patients. Twenty ICH patients will be randomized to receive either mock conditioning or unilateral arm RIC (4 cycles × 5 min inflation/5 min deflation per cycle) beginning within 48 h of stroke onset and continuing twice daily for one week. All patients will receive standard medical care according to latest guidelines. The primary outcome will be the safety evaluation of unilateral RIC in ICH patients. Secondary outcomes will include hematoma volume/clot resorption rate and functional outcomes, as assessed by the modified Rankin Scale (mRS) at 1- and 3-months post-ICH. Additionally, blood will be collected for exploratory genomic analysis. This study will establish the feasibility and safety of RIC in acute ICH patients, providing a foundation for a larger, multi-center clinical trial.Spontaneous Intracerebral hemorrhage (ICH) is a devastating injury that accounts for 10–15% of all strokes. The rupture of cerebral blood vessels damaged by hypertension or cerebral amyloid angiopathy creates a space-occupying hematoma that contributes toward neurological deterioration and high patient morbidity and mortality. Numerous protocols have explored a role for surgical decompression of ICH  Intracerebral hemorrhage (ICH), the most common form of hemorrhagic stroke, accounts for up to 15% of all strokes, with 67,000 Americans suffering an ICH annually . One-yeaHematoma volume is associated with neurological deterioration and high patient mortality, but no available treatment options significantly improve patient outcomes. The current American Heart Association Guidelines for the Management of Spontaneous ICH in Adults recommend surgical clot removal in neurologically deteriorating patients and in patients presenting with lobar clots > 30 mL and within 1 cm from the brain surface; however, the benefits of neurosurgical clot evacuation remain unclear for most ICH patients . Along tvia a process deemed “ischemic conditioning” , which found a 7-day protocol of RIC was safe, well-tolerated, and accelerated hematoma resolution rate in 40 patients with supratentorial ICH  trial . All patients admitted to the neuro-intensive care unit and meeting inclusion/exclusion criteria will be eligible for enrollment. A total of twenty patients that meet pre-defined study criteria and providing informed consent will be randomized to either standard of care plus mock arm conditioning or standard of care plus arm RIC (4 cycles × 5 min inflation/5 min deflation per cycle) groups. Intervention will be initiated within 48 h of ICH onset and will be continued twice daily for seven consecutive days. The primary outcome measure will be safety. Secondary outcome measures will include hematoma volume and functional outcomes, as assessed by the modified Rankin Scale (mRS) at 1- and 3-months post-ICH. Cranial computed tomography (CT) scans will be obtained upon admission and daily thereafter until stabilization of clot expansion. CT scans will additionally be obtained at 7- and 14-days post-admission to monitor hematoma volume. Arterial blood will be collected over the first 7 days post-ICH and used for exploratory biomarker analysis. The study design, as depicted in Computed tomography scans will be reviewed with the patient, when possible, or the person authorized to sign on behalf of the patient based on institutional criteria.•Age 18–80 of both genders.•Diagnosis of ICH confirmed by CT scan.•Hematoma volume > 10 mL per neuroradiology calculation.•Eligible to start RIC therapy within 48 h of ictus .•Signed informed consent.•Intracerebral hemorrhage associated with trauma, tumor, ruptured aneurysm, or arteriovenous malformation, venous infarct/venous sinus thrombosis, or post-procedural .•Planned surgical intervention within 48 h of ictus.•Impending herniation (shift > 10 mm) or clinical signs of brainstem dysfunction.•Severe hepatic or renal dysfunction.•Life expectancy is <3 months secondary to other comorbid conditions.•Inability to administer RIC for any reason.Enrolled patients will be randomized in a 1:1 ratio into two study arms (mock conditioning control group or RIC group). The randomization sequence will be generated by a computer program and the group assignment will be coded by a research coordinator. Assessments of CT scans, neurological outcomes, and biomarker analysis will be performed by qualified investigators blinded to treatment assignments. At study conclusion, patient data will be decoded and analyzed.Routine standard medical care will be provided to all subjects, in accordance with the American Heart Association Guidelines for the Management of Spontaneous ICH . BrieflyControl arm will receive current standard of care plus unilateral, twice-daily mock conditioning whereby a cuff will be placed on an upper limb, but inflation/deflation will not be performed. The Intervention arm will receive current standard of care plus unilateral, twice daily RIC. RIC will be performed by placing an automated blood pressure cuff on the upper arm  day 3/Vc day 2, Vc day 2/Vc day 1]. Volumetric data obtained at 7- and 14-days post-admission will be divided by the maximum clot volume (around day 3). Images will be quantified by neuroradiologists blinded to group randomization. Secondary study outcome is functional outcome, as assessed by modified Rankin Scale (mRS) score at 1- and 3-months post-ICH. The mRS is a well-characterized measure of functional status. Patients will be grouped based on good functional outcomes (mRS 0–2) or poor functional outcomes (mRS 3–6). Neurological assessments will be conducted by physicians that are blinded to group randomization.The primary study outcome is hematoma volume, as assessed by cranial CT scanning. Volumetric data from CT scans will be obtained at the time of admission and daily thereafter until clot expansion has stabilized on two successive CT scans. We will then obtain ratios of clot volume to map the rate of clot progression [i.e., clot volume  approaches show promise in early-stage clinical trials; however, the Minimally Invasive Surgery Plus Alteplase for Intracerebral Hemorrhage Evacuation III (MISTIE-III) trial observed a reduction in hematoma volume without significant functional improvement . This lavia pleiotropic mechanisms that could prove more efficacious than surgical evacuation. Moreover, RIC could provide a potential adjunct therapy in conjunction with minimally invasive surgery and tPA.We identified that RIC accelerated hematoma resolution, reduced peri-hematoma edema, improved cerebral blood flow, limited white matter loss, and improved neurological outcomes following ICH in rodents . InteresA potential strength of this study is the high number of ICH patients seen in our institution. Our medical center is located in the South Eastern United States, a region of the country dubbed the “stroke belt,” due to an elevated incidence of stroke including ischemic stroke, subarachnoid hemorrhage, and spontaneous ICH. Our institution treats an average of 840 stroke diagnoses per year with 120 (14%) of these patients having an intracerebral hemorrhage. Due to our regional demographics, we see a larger percentage of younger and African-American patients, which will allow our study to address ICH treatment in these less studied populations. A limitation of our study is that the proposed protocol is based on pre-clinical work; however, it remains unknown whether this proposed protocol is optimal for humans, as compared to rodents. To partially address this point, we will monitor changes in macrophage activation to determine whether this RIC effect parallels that observed in rodents. To avoid bias with respect to data interpretation, scans and neurological scale rating will be performed by investigators blinded to experimental groups; however, given the nature of RIC, subjects will be aware of their experimental group. Nonetheless, our pilot data will provide feasibility and safety of RIC in ICH patients.The proposed clinical pilot study will provide early-stage safety and feasibility data to indicate whether RIC may non-invasively accelerate spontaneous hematoma resolution and improve ICH patient outcomes. This knowledge will establish a crucial foundation for larger, multi-center clinical studies of efficacy.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.The studies involving human participants were reviewed and approved by Augusta University Institutional Review Board (IRB). The patients/participants provided their written informed consent to participate in this study.AJ, KD, and JV conceptualized the study and drafted the manuscript. All authors contributed to experimental design and reviewed the final version of the 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.