During TCD monitoring, the middle cerebral artery (MCA) flow was bilaterally increased during the task associated with greater cognitive effort. fNIRS also detected significantly increased activation during a more challenging task in the left dorsolateral prefrontal cortex (DLPFC), and in addition, widespread activation of the medial prefrontal cortex (mPFC) was also revealed. Robust changes in prefrontal cortex hemodynamics may explain the profound change in MCA blood flow during the same cognitive task. Overall, our data support our hypothesis that both TCD and fNIRS methods can discriminate NVC evoked by higher demand tasks compared to baseline or lower demand tasks.Understanding how the brain allocates resources to match the demands of active neurons under physiological conditions is critically important. Increased metabolic demands of active brain regions are matched with hemodynamic responses known as neurovascular coupling (NVC). Several methods that allow noninvasive assessment of brain activity in humans detect NVC and early detection of NVC impairment may serve as an early marker of cognitive impairment. Therefore, non-invasive NVC assessments may serve as a valuable tool to detect early signs of cognitive impairment and dementia. Working memory tasks are routinely employed in the evaluation of cognitive task-evoked NVC responses. However, recent attempts that utilized functional near-infrared spectroscopy (fNIRS) or transcranial Doppler sonography (TCD) while using a similar working memory paradigm did not provide convincing evidence for the correlation of the hemodynamic variables measured by these two methods. In the current study, we aimed to compare fNIRS and TCD in their performance of differentiating NVC responses evoked by different levels of working memory workload during the same working memory task used as cognitive stimulation. Fourteen healthy young individuals were recruited for this study and performed an  Under cognitive workload, activation of neurons in the brain implies the allocation of resources and nutrients by the cerebrovascular system. A better understanding of this physiological adaptation may aid timely identification of individuals in early stages of cognitive impairment, provided that a neuroimaging modality is available with adequate sensitivity to detect functional responses elicited by cognitive stimuli. Early interventions would allow improvements in healthspan and postpone deterioration of quality of life in individuals prone to dementia.Under physiological conditions, the brain is critically dependent on the structural and functional integrity of its blood vessels. To supply additional nutrients to match the metabolic demand of the cells in the central nervous system, activation of the neuronal population in the brain is accompanied by functional hyperemia, also known as neurovascular coupling (NVC). In human subjects, impaired NVC is associated with neurological diseases  that lead to cognitive impairment . InteresA relatively ubiquitous method, Transcranial Doppler sonography (TCD) allows rapid and economical assessment of dynamic blood flow regulation in the brain. Specific task-evoked NVC can be assessed during a cognitive task  while monitoring blood flow in the middle cerebral artery (MCA) . HoweverNVC can also be assessed by functional Magnetic Resonance Imaging (fMRI) . During n-back cognitive stimulation [-MCAv[0-back])/MCAv0-back], [(2-back)—(0-back)] and [(2-back)—(1-back)] was applied. Increased activation was considered significant when the false discovery rate corrected p<0.05 (FDR using Benjamini-Hochberg method) [For fNIRS analysis, the raw data were first processed using a pipeline based on a General Linear Model (GLM) approach using the Brain AnalyzIR toolbox (commit 46c645d) . Measure method) .To demonstrate the timecourse of HbO and HbR concentration changes, raw fNIRS data were analyzed and processed with nirsLAB  . Saturatn-back task. Reaction times were found to be task-dependent and were significantly longer during the 1-back and 2-back tasks vs. their corresponding 0-back task, on both visits  answers and the reaction time and performance between first and second laboratory visits. We found no significant difference in the performance or the reaction between two visits, however, we observed a trend (p = 0.06) for a minor improvement in the 2-back test performance.To evaluate the practice effect and repeatability of the n-back test for each laboratory visit.Two participants were excluded from the analysis due to an inadequate temporal acoustic window. Left-sided channel data were excluded in 3 participants, and right-sided channel data were excluded for one participant due to low mean flow velocity or signal loss during testing.We found no effect of MCAv Side  = 0.6, p = 0.45) or interaction of Cognitive stimulation×MCAv Side  = 0.61, p = 0.6), however, we found a significant effect of Cognitive stimulation that accounted for 2.54% of variation in the data  = 8.595, p<0.0001). Post-hoc testing revealed significant differences between the 0-back and 2-back conditions . Two-way ANOVA revealed no statistically significant effect for the Side of the MCA velocity (MCAv) or the interaction of Cognitive stimulation×MCAv Side. We observed a significant effect of Cognitive stimulation that accounted for 2.54% of variance in the data. Results of the Sidak’s post-hoc test are reported in the table. We observed significantly greater responses in the MCAv during 2-back task vs 0-back task.Neurovascular coupling hemodynamic responses were measured in the middle cerebral artery (MCA) using transcranial Doppler (TCD) sonography during the performance of the cognitive To evaluate the effect of cognitive workload on NVC responses measured using TCD, we also compared the percent change in the blood flow velocities between 2-back and 1-back tasks that were normalized to the preceding 0-back task. We observed significantly greater NVC responses during 2-back task compared to 1-back task in both MCAs  responsible for executive cognitive function, including the medial PFC, left and right DLPFC . The chan-back paradigm, which proved to be a repeatable paradigm to evoke NVC. No significant improvement was seen between visits in the reaction time or performance of subjects during the tasks. The n-back performance showed a trend toward improvement; however, the extent of the improvement was minor when measured in participants of the current study. Neither NVC measurements could differentiate NVC responses evoked by the task with lower cognitive workload (1-back) when compared to the baseline (0-back) condition. The more demanding cognitive task (2-back) evoked greater neurovascular coupling responses compared to both the baseline (0-back) and the less demanding task (1-back) when measured in healthy young adults.The current study compared hemodynamic responses in the brain evoked by a task with different levels of cognitive workload. Cognitive stimulation was based on the During the more difficult task (2-back condition), TCD detected a significant increase in MCA blood flow velocity , indicatn-back paradigm used in this study for both TCD and fNIRS methodologies was originally developed for TCD studies [Functional NIRS is capable of imaging the convexity of the cerebral cortex, and since the MCA supplies the PFC convexity through pial arterioles, it allowed closer investigation of the dilation of blood vessels distal to the MCA. An increase of blood flow to a cortical region is represented by the characteristic increase of HbO and decrease of HbR concentrations in the brain tissues measured by fNIRS . A signi studies , 27, andn-back approach [Activation of medial PFC in addition to DLPFC was only significant while participants were presented the more demanding 2-back task. According to earlier neuroimaging studies, the medial PFC  is activated when a difficult task was anticipated , and theapproach , which happroach .Involvement of the DLPFC area has been widely described in working memory , which iTo summarize, hemodynamic NVC responses were successfully assessed using fNIRS and TCD with the same cognitive stimulation paradigm after reaching a certain level of difficulty. The combination of the two NVC assessments allowed better understanding of the cerebral hemodynamics in healthy individuals, which also means that presented procedures can potentially detect cerebrovascular dysfunction. Our data suggest that if NVC function is to be monitored, utilizing the more ubiquitous TCD may be sufficient if the stimulation paradigm and task difficulty are carefully selected. On the other hand, inference from purely TCD results may be limited as it does not differentiate between the spatiotemporal pattern of cerebral hemodynamic changes. Additional cortical areas might be recruited, or the timecourse and amplitude of hemodynamic response may change depending on the task difficulty level in the cortical area that was expected to be activated.Due to limited metabolic reserve, intact neuronal and glial function underlying brain activity depends on constant provision of adequate oxygen and nutrient supply via continuous adjustment of cerebral blood flow. The importance of NVC is evidenced by the fact that disruption of NVC in a rodent model leads to the impairment of neuronal function, and consequently, cognitive impairment . FurtherThe current study demonstrates the sensitivity of fNIRS and TCD to differentiate between NVC responses evoked by cognitive stimulation with different levels of cognitive workload. In healthy adults, only a more challenging working memory task evoked significantly greater NVC responses both in the left DLPFC and medial PFC regions, as well as in the MCA supplying these regions.S1 FigpFDR<0.05. T-statistic heatmaps are plotted.Tasks were administered in the following order: 0-back→ 1-back→ 0-back→ 2-back, 0-back being the reference condition, 1-back and 2-back being the tasks with different cognitive demand. Significant inactivation was seen in areas of the prefrontal cortex (PFC) and left somatosensory cortex when the first reference 0-back condition was compared to second 0-back condition. Data were analyzed using the Brain AnalyzIR software, and a mask was applied on results to only map channels where (TIF)Click here for additional data file.S2 FigGroup average traces of oxy-hemoglobin (HbO) for each channel of the NIRS probe. Red lines represent the channels regions of interest defined in (TIF)Click here for additional data file.S3 FigGroup average traces of deoxy-hemoglobin (HbR) for each channel of the NIRS probe. Blue lines represent the channels in the regions of interest defined in (TIF)Click here for additional data file.


The evolution in imaging has had an increasing role in the diagnosis, staging and follow up of bladder cancer. Conventional cystoscopy is crucial in the diagnosis of bladder cancer. However, a cystoscopic procedure cannot always depict carcinoma in situ (CIS) or differentiate benign from malignant tumors prior to biopsy. This review will discuss the standard application, novel imaging modalities and their additive role in patients with bladder cancer. Staging can be performed with CT, but distinguishing between T1 and T2 BCa (bladder cancer) cannot be assessed. MRI can distinguish muscle-invasive from non-muscle-invasive tumors with accurate local staging. Vesical Imaging-Reporting and Data System (VI-RADS) score is a new diagnostic modality used for the prediction of tumor aggressiveness and therapeutic response. Bone scintigraphy is recommended in patients with muscle-invasive BCa with suspected bony metastases. CT shows low sensitivity for nodal staging; however, PET (Positron Emission Tomography)/CT is superior and highly recommended for restaging and determining therapeutic effect. PET/MRI is a new imaging technique in bladder cancer imaging and its role is promising. Texture analysis has shown significant steps in discriminating low-grade from high-grade bladder cancer. Radiomics could be a reliable method for quantitative assessment of the muscle invasion of bladder cancer. Bladder cancer (BCa) is the second most common urogenital malignancy, preceded by prostate cancer. It is considered the 10th most widespread cancer in the world . The incBladder cancer clinical management strategies and prognosis depend on the extent of loco-regional disease and on the discrimination between superficial (≤T1 stage) and muscle-invasive disease (≥T2 stage). Non-muscle-invasive  bladder cancer is managed with transurethral resection of the bladder tumor with or without intravesical or photodynamic chemotherapeutic adjuvants , while mThe idea of this review is to discuss and differentiate the standard imaging modalities, and the novel techniques in bladder cancer imaging. We will focus on the advantages and limitations of each imaging method and their role in discriminating muscle-invasive from non-muscle-invasive disease as well as lymph node involvement prior to operative management.Painless hematuria is the typical symptom of BCa . The firAlthough bladder cancer diagnosis is histopathologically based via cystoscopy and biopsy, cystoscopy cannot reliably determine the depth of bladder muscle infiltration and development of metastasis , so precIn approximately 1% of bladder cancers, urothelial or squamous cell carcinoma, calcification is visible on plain film. It can be focal, linear, punctuating or coarse. A plain X-ray may suggest the diagnosis for bilharzial bladder, which is complicated by carcinoma. The calcification occurs as a continuous curved line of calcification of the bilharzial bladder wall . The conThe dystrophic calcification infrequently occurs with the necrotic nature of the bladder tumor. It can be seen as fine speckles of calcification or as a dense calcified nodule within the bladder .In the delayed phase of IVU (cystogram), fungating tumors within the bladder appear as filling defects. However papillary tumors may be missed on IVU due to the masking effect of contrast material in a full bladder and in the post void phase . DivertiThe recommendations of the First International Consultation of Bladder Tumors (FICBT) state that IVU is not of significant value in the detection of bladder cancer . Palou aOnly 26% to 86% of bladder cancer could be detected with IVU. Furthermore, its reliability in detecting small cancer is still controversial, ranging from 0.5–1 cm as a lower size limit of sensitivity. Conventional urography cannot differentiate the masses of vesical origin from those of extravesical origin infiltrating the bladder as well as lymph node evaluation, therefore CT urography has been proved superior due to soft tissue and nodal discrimination in the CT examination .Bladder tumor detection by ultrasound relies upon the size and site of the tumor. Tumors less than 0.5 cm in size and tumors at the bladder neck are hard to delineate. However, a higher detection rate with an accuracy of 95% can be obtained for tumors >0.5 cm located on the lateral or posterior bladder walls .The ultrasound appearance of a bladder tumor may be an intraluminal nonmobile mass or localized thickened bladder wall .The flow within the mass can be assessed with Doppler ultrasound and thus can distinguish the tumor from a blood clot. A bladder tumor appears as an echogenic soft-tissue lesion projecting inside the lumen and adherent to the bladder wall. The existence of tumoral blood flow does not assess the stage or aggressiveness of tumor. Ultrasound has shown little value in the staging of bladder cancer  21]..21].Many authors studied the usefulness of contrast-enhanced ultrasonography on different organs in the assessment of the vascular nature of tissue and for overcoming some of the limitations in tumor characterization with gray-scale and Doppler ultrasound examination .At gray-scale US, the bladder wall appears homogeneously hyperechogenic. However, the bladder wall layers can be differentiated with the use of contrast-enhanced sonography . It alsoDevelopments in image reconstruction have made three-dimensional (3D) US possible. This provides additional information including a 3D display of different pathologies. It allows BCa scanning in different planes and increasing the sensitivity of diagnosis. It also allows the distinction of the superficial disease from the infiltrative disease .CT urography is the most frequently used imaging technique for detection and staging of bladder cancer with a diagnostic accuracy estimated of 91% in detection and 35–55% in staging ,26. The Perivesical fat infiltration by the tumor can be detected on CT cystography . HoweverT staging classification of bladder tumor: the American Joint Committee on Cancer (AJCC) TNM system is the most widely used staging system . He. He56]. k et al. . He founk et al.  and thatIn 2009, Takeuchi et al.  describeHowever, the connective tissue stalk is characteristic for T1 or less bladder cancer. It was reported that some cases of stage T2 or more represented with “inchworm sign” on diffusion-weighted magnetic resonance imaging (DW-MRI) ,63 Figu. So, DWIA multiparametric (mp) MRI, including morphological T2-weighted imaging (T2WI) together with the added value of functional sequences of diffusion-weighted and dynamic contrast-enhanced imaging are of great importance in terms of the local staging of primary tumors, nodal affection, distant metastasis, detection of recurrence and assessing the therapeutic response ,65.The increased capability of MRI in the field of local staging using morphologic sequences as T2-weighted images (T2WI) together with functional diffusion-weighted images (DWI) / apparent diffusion coefficient (ADC map), and dynamic contrast-enhanced MRI (DCE), allow for the identification of tumor site, size, and morphology, thus decreasing the false staging assessment using T2 high resolution sequence alone ..89].Cystitis glandularis is a common chronic inflammatory disorder in which transitional cells have undergone glandular metaplasia . Masses On ultrasound, a bladder endometrioma more commonly appears as a hypoechogenic mass that mimics bladder cancer. The echogenic bladder wall mostly surrounds it, while bladder cancer often does not have this boundary . On T1-wBladder tuberculosis is most often represented by an irregular asymmetric bladder wall. Later, marked fibrosis results in a marked contraction and small capacity of the bladder, “thimble bladder” .Transurethral resection of the tumor remains the surgical mainstay for the diagnosis and treatment of non-muscle-invasive bladder cancer (NMIBC), however, radical cystectomy is the established treatment for muscle-invasive tumors. NMIBCs have a higher incidence of recurrence than muscle-invasive tumors due to conservative management. If left untreated or with improper management of NMIBC, its incidence of transformation into muscle-invasive tumors increases .Recurrence occurs after transurethral resection (TUR) of superficial bladder tumors in about 70% of patients . HoweverSeveral studies have assured that multiparametric MRI (mpMRI) is superior in the staging of bladder cancer, where morphologic (T2WI and DCE) and functional imaging such as DWI, diffusion tensor imaging (DTI) and perfusion-weighted are used in combination ,97.Differentiation of the normal bladder wall from the bladder tumor can be assessed on T2-weighted images , howeverIn 2018, VI-RADS has been evolved to allow accurate implementation of MRI acquisition and interpretation for the staging of bladder cancer .VI-RADS is a useful method for the prediction of muscle invasion using the anatomical category on a T2-weighted image (T2WI), the functional assessment with a diffusion-weighted image (DWI), dynamic contrast-enhanced (DCE) MRI and the overall VI-RADS score .Applications of VI-RADS: VI-RADS score is best used prior to TURBT or intravesical Bacillus Calmette-Guerin (BCG). Therapeutic assessment can be achieved using combined preoperative MRI and VI-RADS scoring . In patiThe 18F-fluorodeoxyglucose (18F-FDG)-PET/CT is a functional method for assessment of the metabolic state of glucose intracellularly, via measurement of the standardized uptake value (SUV) maximum. Most cancers show increased uptake, with accurate detection of metastasis in small sized LN with increased metabolic activity, that are not suspicious by CT alone .Many researchers reported that combined 18F-FDG PET/CT was superior in picking up distant metastasis in BCa .FDG-PET/CT has a significant role in the accurate evaluation of tumor response to chemo-radiotherapy in patients with MIBC, and can select the appropriate time for operative management .The positron emitting radiopharmaceutical agent 18F-Sodium Fluoride (NaF) is used for PET/CT for the detection of osseous metastasis and shows superior sensitivity with better image quality than bone scintigraphy in picking up the bony metastasis of bladder cancer ,107. BonTexture analysis is a component of computer aided diagnosis by quantitative measurement of the tumor heterogeneity and pixel distribution analysis. Entropy is a method for measuring the heterogeneity of the tumor and complexity of pixel intensity . Many stHigh grade bladder cancers tend to have great values of entropy. It may be due to greater heterogeneity and aggressiveness of the tumor and direct tumor invasion of the surrounding structures associated with increased tumor angiogenesis and peritumoral edema .Quantitative CT texture analysis has proved to have a significant role in discriminating the low grade from the high grade bladder cancer. Lower entropy signifies low-grade bladder cancer .Radiomics is a technique where medical images are converted into high-dimensional data through the extraction of a large number of quantitative features, with sequential data analysis for decision-making using image SI, morphological, and textural features ,113.Up until now, radiomics have shown potential in bladder cancer detection, staging, grading, follow up and therapeutic response .This article aims to serve as a concise review of the different current and advanced imaging modalities, used in the diagnosis and follow up of bladder tumors, however further studies are needed to assess their full potential and correlation with the pathological findings.


Newly synthesized small GTPases in the Ras and Rho families are prenylated by cytosolic prenyltransferases and then escorted by chaperones to membranes, the nucleus, and other sites where the GTPases participate in a variety of signaling cascades. Understanding how prenylation and trafficking are regulated will help define new therapeutic strategies for cancer and other disorders involving abnormal signaling by these small GTPases. A growing body of evidence indicates that splice variants of SmgGDS (gene name RAP1GDS1) are major regulators of the prenylation, post-prenylation processing, and trafficking of Ras and Rho family members. SmgGDS-607 binds pre-prenylated small GTPases, while SmgGDS-558 binds prenylated small GTPases. This review discusses the history of SmgGDS research and explains our current understanding of how SmgGDS splice variants regulate the prenylation and trafficking of small GTPases. We discuss recent evidence that mutant forms of RabL3 and Rab22a control the release of small GTPases from SmgGDS, and review the inhibitory actions of DiRas1, which competitively blocks the binding of other small GTPases to SmgGDS. We conclude with a discussion of current strategies for therapeutic targeting of SmgGDS in cancer involving splice-switching oligonucleotides and peptide inhibitors. The Ras superfamily consists of over 150 different small GTPases belonging to specific families. The most well characterized small GTPases in this superfamily are members of the Ras family (36 members), Rho family (20 members), and Rab family (over 60 members) . These pThe intracellular site where a small GTPase is located defines how the GTPase will be activated and which signaling pathway it will modulate. Cell membranes are a major site for activation and signaling by small GTPases. Ras and Rho family members anchored at the plasma membrane are activated by membrane-localized GEFs and participate in signaling cascades initiated by plasma membrane receptors . Rab famSmall GTPases that are activated by GEFs associated with membranes must be prenylated to localize at the membrane and interact with their GEFs. K-Ras4B is an excellent example of a small GTPase that relies primarily on membrane localization for its activity . HoweverThere is growing evidence that some small GTPases participate in signaling events before they are prenylated , leadingNewly synthesized small GTPases in the Ras and Rho families are soluble, hydrophilic proteins residing in the cytosol. The majority of these small GTPase have a C-terminal CAAX motif consisting of a cysteine (C), two aliphatic amino acids (AA), and a terminal amino acid (X). When a newly synthesized small GTPase enters the prenylation pathway, the CAAX motif undergoes prenylation and post-prenylation processing, converting the small GTPase to a hydrophobic protein that can anchor at membranes .Ras and Rho family members are prenylated by a cytosolic prenyltransferase (PTase), which irreversibly attaches a hydrophobic prenyl group to the cysteine in the CAAX motif. Small GTPases that have a CAAX motif ending in alanine, glycine, serine, methionine, or phenylalanine receive a 15-carbon farnesyl group from farnesyltransferase (FTase). In contrast, if the CAAX motif ends in leucine or phenylalanine, the GTPase receives a 20-carbon geranylgeranyl group from geranylgeranyltransferase-I (GGTase-I) . The preWhen post-prenylation processing is completed, the prenylated, hydrophobic GTPase can take two different routes to the plasma membrane. Small GTPases that have an additional cysteine near the CAAX motif, such as H-Ras and N-Ras, move to the Golgi to become palmitoylated before localizing at the plasma membrane . In contRab family members are prenylated in a pathway differing from the one that prenylates Ras and Rho family members. Newly synthesized Rab family members associate with the Rab Escort Protein REP1 before prenylation. A trimeric complex consisting of REP1, the Rab protein, and the Rab geranylgeranyltransferase (RabGGTase) is needed for the RabGGTase to sequentially prenylate two cysteines in the C-terminus of the Rab protein. After prenylation, REP1 serves as a chaperone for the prenylated Rab small GTPase as it moves through the cytosol to membranes. The importance of REP1 in this pathway is indicated by its interactions with both the pre-prenylated and prenylated forms of the Rab protein, facilitating prenylation of the newly synthesized Rab protein and then escorting the prenylated Rab protein to membranes .The participation of REP1 in the prenylation and trafficking of newly synthesized Rab family members suggests that proteins with functions similar to REP1 might participate in the prenylation and trafficking of newly synthesized Ras and Rho family members. Such proteins that might assist Ras and Rho family members during prenylation were not known prior to the discovery of the two major splice variants of SmgGDS . The disIn 1990, members of the Takai laboratory isolated a protein from bovine brain that interacted with Rap1A and Rap1B, and they named this protein “small G protein guanine dissociation stimulator” or SmgGDS . A cDNA SmgGDS has been the subject of several controversies regarding its interactions with small GTPases. One of these controversies arose from inconsistent reports that a small GTPase must be prenylated before it can associate with SmgGDS. Some groups reported that SmgGDS interacts only with prenylated small GTPases , whereasAn explanation for these conflicting reports that only prenylated GTPases bind SmgGDS was provided in 2010, when the Williams group reported the identification of two splice variants of SmgGDS that differ in their ability to bind prenylated GTPases . A long The structural features that cause SmgGDS-607 to bind pre-prenylated GTPases and SmgGDS-558 to bind prenylated GTPases are beginning to be understood. SmgGDS is composed of multiple armadillo (ARM) domains. An ARM domain consists of approximately 40 amino acids folded into alpha helices. ARM domains can be identified from the amino acid sequence of a protein using a paradigm provided by Andrade and colleagues . Using tAnother major controversy regarding SmgGDS arose from the proposal that SmgGDS is a GEF for many different Ras and Rho family members. Early studies suggested that SmgGDS might act as a GEF for multiple PBR-containing small GTPases, including Rap1A and Rap1B , K-Ras4BThe Sondek group finally clarified the GEF activity of SmgGDS in 2011 . Using rEven though SmgGDS has limited intrinsic GEF activity, SmgGDS still might promote the activity of many different small GTPases by serving as a scaffold that facilitates the interactions of GEFs with small GTPases bound to SmgGDS . The forMultiple studies indicate that SmgGDS-607 and SmgGDS-558 work together to regulate the prenylation and trafficking of small GTPases in the Ras and Rho families  Berg et. SmgGDS-SmgGDS-607 recognizes the last amino acid in the CAAX motif of the GTPase, preferring to interact with small GTPases that have a CAAX motif ending in leucine rather than methionine . This fiSmgGDS-558 differs significantly from SmgGDS-607 because SmgGDS-558 binds only prenylated small GTPases . SmgGDS-It is likely that SmgGDS-558 also acts as a chaperone that helps prenylated small GTPases move to the plasma membrane or other regions of the cell after post-prenylation processing has been completed at the ER  SmgGDS-5In addition to escorting prenylated small GTPases to the plasma membrane, it is likely that SmgGDS-558 also escorts prenylated GTPases into the nucleus . NucleocWhile SmgGDS-558 serves as a nuclear chaperone for prenylated small GTPases, SmgGDS-607 might serve as a nuclear chaperone for pre-prenylated GTPases . Pre-preEvents that alter the interactions of SmgGDS with Ras and Rho family members are being recognized as important regulatory mechanisms that control the prenylation and trafficking of these small GTPases. When SmgGDS-607 binds a newly synthesized small GTPase, SmgGDS-607 may retain the small GTPase until the correct signal releases the small GTPase into the prenylation pathway . The sigThere are over 100 GEFs located in the cytoplasm, nucleus, and at the plasma membrane that can activate members of the Ras and Rho families , and a sSimilar to the mechanisms that regulate SmgGDS-607, specific signaling events may control the ability of SmgGDS-558 to deliver and release prenylated small GTPases at specific sites in the cell. Certain signals may direct SmgGDS-558 to the ER membrane, the plasma membrane, or to the nucleus when a prenylated GTPase is bound to SmgGDS-558. The prenylated GTPase may be released from SmgGDS-558 at these sites when it encounters its GEF and undergoes GDP/GTP. By releasing a GTPase from SmgGDS-558, these GEFs will control when the small GTPase will undergo post-prenylation processing  and wherIn addition to GEFs, proteins called GDI displacement factors (GDFs) might also release prenylated GTPases from SmgGDS-558 . There a1–36  that restore normal splicing are providing new therapies for cancer and other diseases . The valSSO Ex5 suppresses the prenylation of multiple Ras and Rho family members in cancer cells, consistent with SSO Ex5 reducing SmgGDS-607 expression . This ex1–38 bind SmgGDS-607 in osteosarcoma (1–38, decreases RhoA activity, and reduces cell migration and invasion. Furthermore, this peptide inhibitor diminishes lung metastases of osteosarcoma in a mouse model, and increases survival time of the mice bearing the tumors (In addition to SSOs, other strategies to inhibit SmgGDS functions in cancer are beginning to be developed. Chemical inhibitors of SmgGDS have not been reported, but a peptide inhibitor that targets SmgGDS-607 has recently been described . The Kanosarcoma . They foe tumors . These fe tumors , abnormae tumors , and deve tumors , indicatHow do cells regulate the expression and activity of SmgGDS-607 and SmgGDS-558?• The balanced expression of SmgGDS-607 and SmgGDS-558 in cells is regulated through specific splicing programs and spliceosome factors that have yet to be characterized. Additionally, cells control the activities of these splice variants through the actions of DiRas , which iHow do post-translational modifications of the SmgGDS splice variants affect their abilities to regulate small GTPases?• Online databases such as PhosphoSitePlus® indicate that SmgGDS has multiple residues that are ubiquitinated, acetylated, or phosphorylated. Our understanding of how SmgGDS-607 and SmgGDS-558 might be post-translationally modified and how these modifications might affect SmgGDS functions is still very rudimentary.Which small GTPases interact with SmgGDS, and what are the functional consequences of these interactions?• 1–36 (1–38 (SmgGDS preferentially binds small GTPases that contain a PBR, including RhoA, RhoC, Rac1, Cdc42, K-Ras4A, Rap1A, Rap1B, and DiRas1, as discussed above. SmgGDS probably binds many more PBR-containing small GTPases , and the1–36 , Rab22a136 (1–38  and poteWhich signaling pathways control the prenylation and trafficking of small GTPases by altering their interactions with SmgGDS?• β-adrenergic receptors promotes phosphorylation of serines in the PBR of pre-prenylated Rap1B. This phosphorylation of the PBR disrupts the interactions of pre-prenylated Rap1B with SmgGDS-607, suppressing the prenylation of Rap1B and causing it to accumulate in the nucleus instead of localizing at the cell membrane (Activation of A2B adenosine receptors and membrane . Other sWhat are the identities of the GEFs that regulate the prenylation and trafficking of small GTPases, and how do they interact with SmgGDS?• Most studies of GEFs for Ras and Rho family members have focused on GEFs that activate prenylated small GTPases at membranes . Very liHow does SmgGDS participate in different diseases, and what are the best approaches to target SmgGDS therapeutically?• It is well known that SmgGDS promotes cancer, and it is beginning to be recognized that SmgGDS also contributes to other pathologies, including neurological deficits , and vasThe importance of SmgGDS throughout the animal kingdom is indicated by phylogenetic analyses suggesting that it was present in the last common eukaryotic ancestor that existed over 500 million years ago . The exp


Following the publication of the original article , the autFunding noteResearch reported in this publication was supported in part by two NSF Grants (DBI 1759934 and IIS1763246), a DOE grant(DE-SC0021303) and an NIH Grant (R01GM093123) to JC. The funding agencies did not play a role in this research.The original article  has been


There were no significant differences in complication rates. Therefore, EBUS-GS is an effective alternative for the diagnosis of PPLs in patients with ILD, without additional complications.The occurrence of interstitial lung disease (ILD) with peripheral pulmonary lesions (PPLs) is closely linked to the development of lung cancer. Yet, the best diagnostic approach for identifying PPLs in patients with ILD remains elusive. This study retrospectively investigated the application of transbronchial biopsy (TBB) using endobronchial ultrasonography with a guide sheath (EBUS-GS) to the effective and safe diagnosis of PPLs when compared with conventional TBB. The study sample included a consecutive series of 19 patients with ILD who underwent conventional TBB or TBB using EBUS-GS at Tosei General Hospital between 1 April 2013 and 31 October 2015. The two techniques were compared based on diagnostic yield and associated complications. The diagnostic yield of EBUS-GS TBB was significantly higher than that of conventional TBB ( The widespread use of low-dose computed tomography (CT) for lung cancer screening has produced an increase in the detection rates of peripheral pulmonary lesions (PPLs) . In the However, there are few reports detailing the application of EBUS-GS in patients with ILD, specifically. In patients with ILD, TTNB is associated with a higher frequency of pneumothorax, and SLB carries the risk of acute ILD exacerbation . In addin = 19) or TBB using EBUS-GS (n = 19) for PPL diagnosis at Tosei General Hospital between 1 April 2013 and 31 October 2015. The patients were divided into two groups according to the biopsy technique: a conventional TBB group and an EBUS-GS group. PPLs were defined as visible lesions surrounded by normal lung parenchyma or interstitial lung lesions that were identified by CT but not visualized by bronchoscopy. The inclusion criteria were as follows: confirmed ILD accompanied by bilateral reticular shadows on chest radiographs and the application of conventional TBB and TBB using EBUS-GS for PPL sample recovery and diagnosis. Patients with endobronchial lesions and uncertain diagnoses were excluded. ILD was identified by high-resolution CT and clinical data collected before bronchoscopy ..4].It has been reported that intra-procedural EBUS visualization was a significant factor for achieving successful diagnosis. Moreover, a positive bronchus sign has been shown to correlate with EBUS visualization ,13,14,15In the patients with ILD, approximately 20% of the PPLs biopsied by conventional TBB and EBUS-GS were invisible by chest radiography. There was no significant difference in the diagnostic yield for lesions invisible on radiographs between the two groups; however, we acknowledge that our observation may have been affected by sample size. For lesions detected during the chest X-ray (approximately 80%), the diagnostic yield of EBUS-GS was significantly higher than that of conventional TBB, which is considered to be in agreement with the explanation that whether the lesion was visible on chest X-ray or not depends on whether the device was guided to lesions appropriately during EBUS-GS . TherefoEpidemiological evidence shows that as many as 22% of patients with IPF develop lung cancer; this risk is nearly five times greater than that of the general population . HoweverOther studies have demonstrated the overall complication rate of EBUS-GS to be 1.3% (13/965). In this regard, 0.8% (8/965) of patients experienced pneumothorax and 0.5% (5/965) experienced subsequent pulmonary infection . In thisOur study was limited by the small number of consecutive patients with ILD and by its retrospective design. Larger sample sizes and a prospective study design are needed to confirm and further explore our observations.In conclusion, the findings of our study suggest that EBUS-GS is a useful and acceptable initial diagnostic procedure for diagnosing PPLs in patients with ILD. In doing so, our study works at addressing a diagnostic shortfall of lesion assessment in ILD-afflicted lungs with the intent of supporting the early detection of malignant lesions and bettering the outcomes in patients with ILD.


We report a statistical approach to model the resonant peak wavelength (RPW) equation(s) of a photonic crystal fibre (PCF)-based surface plasmon resonance (SPR) sensors in terms of the PCF structural parameters  at various tolerance levels. Design of experiments  is used to investigate the role played by the PCF structural parameters for sensing performance evaluation—RPW, across three tolerance levels ( Hollow-core photonic crystal fibres (HC-PCFs) are types of photonic crystal fibres (PCFs) that offer enormous promise for sensing applications because of the direct interaction of light and the gaseous or liquid analyte within the hollow fibre core. The periodic cladding structure of these PCFs gives them a photonic band-gap that restricts light inside the hollow-core or core filled with analytes having lower refractive index (RI). A key advantage of PCFs is that optical characteristics, such as confinement loss, dispersion and non-linearity can be influenced by varying their structural parameters ,2,3,4. TRecent studies have been reported on filling of air-holes for different SPR sensing applications. A HC-PCF-based SPR sensor was proposed by Luan and Yao  for SPR me value . This waOver the years, fabrication techniques, such as capillary stacking , drillinIn this paper, a combined strategy with FEM simulations and statistical methods of DoE for tolerance study on a HC-PCF-based SPR sensor is proposed. To ascertain the fabrication tolerance limit possible in order to achieve stable and accurate sensing performance of a HC-PCF-based SPR sensor, this research will use the DoE statistical procedure to investigate the rate at which the RPW shifts for analytes with RI = 1.45 at higher fabrication tolerance levels. We hope that the use of DoE statistical modelling of sensing performance parameters would find wider applications in the design and optimisation of PCF sensors used for applications, such as alcohol sensing, tuberculosis cells sensing, cancer cell detection, toxic waste monitoring, etc.A cross-sectional structure of a HC-PCF-based SPR sensor similar to the structural design in  is preseThe schematic cross-section of the proposed HC-PCF-based SPR sensor is illustrated in lections . The fuslections (1)nSiO2old (Au) . The con ± 0.05) , monocyt ± 0.04) , aqueous= 1.445) , adsorbe = 1.46) , poly-acThe sensing performance of this HC-PCF-based SPR sensor was evaluated in terms of the sensitivity, which is proportional to the wavelength change of resonance peaks for two different analytes  with respect to the structural parameters of the HC-PCF-based SPR sensor across different tolerance levels.k is the number of parameters  equation :5)Y=β0+Y=β0+(5)Yficients . Defininquation  was introduced by Box and Wilson  to derivequation :(9)Y=β0+In the CCD procedure, the 2 levels (maximum and minimum) used for factorial analysis are increased to 5 levels. The centre point is introduced as well as the embedded fractional factorial levels.Using the data in % levels  for the d, Using the structure parameters for the The relationship between the DoE RPW model obtained in Equation  and the x-axis label and with three parameters fixed at their own tolerance levels, The 15 RPW values calculated from FEM simulations are plotted on the corresponding RPW model  curves  is a vervely see . The ▪, From the subplots, except the one for the pitch, the curves for the RPW in the air-hole diameter, gold layer thickness and core diameter plots are separated from each other without any crossing. The RPW lines in the air-hole diameter subplot are having more curvature, which shows that air-hole diameter has a slight quadratic effect influence for the RPW estimation for the We can conclude that pitch plays the most important role (nonlinear influence) in determining the RPW for the DoE Model with The DoE quadratic model of a higher tolerance  in determining the RPW for the DoE Model with The DoE quadratic model of a higher tolerance  in determining the RPW for the Comparison between the coefficients of the three DoE models with different fabrication tolerances are listed in Equation  correspoEquation . ParticuResults from the DoE models for three tolerance levels, Equation ), ±5% (EEquation ) and ±10Equation ) as illuIn this paper, we presented a statistical approach of determining the influence of the optical fibre structural design parameters on the sensing performance of HC-PCF-based SPR sensors at three tolerance levels  have linear or quadratic effects on the RPW determination for the sensing process. A clear understanding of the individual parameters influence in determining the RPW changes was presented with the DoE methodology combined with a few FEM simulation results which would not be feasible with numerical computations only. For the proposed HC-PCF sensor, pitch was the most significant parameter for the quadratic effect influence on RPW and gold layer thickness and core diameter are the least contributing parameters. The significance of quadratic effect influence for air-hole diameter was found to be proportional to the increase of fabrication tolerance level. Due to the amount of time and computational resource saved by using the proposed research methodology on tolerance study, we envisage that this novel statistical model derivation of sensing performance parameters can be applied in fibre sensor fabrication industry to improve efficiency and lower cost.


Delirium is an acute change in attention and cognition occurring in ~ 65% of severe SARS-CoV-2 cases. It is also common following surgery and an indicator of brain vulnerability and risk for the development of dementia. In this work we analyzed the underlying role of metabolism in delirium-susceptibility in the postoperative setting using metabolomic profiling of cerebrospinal fluid and blood taken from the same patients prior to planned orthopaedic surgery. Distance correlation analysis and Random Forest (RF) feature selection were used to determine changes in metabolic networks. We found significant concentration differences in several amino acids, acylcarnitines and polyamines linking delirium-prone patients to known factors in Alzheimer’s disease such as monoamine oxidase B (MAOB) protein. Subsequent computational structural comparison between MAOB and angiotensin converting enzyme 2 as well as protein–protein docking analysis showed that there potentially is strong binding of SARS-CoV-2 spike protein to MAOB. The possibility that SARS-CoV-2 influences MAOB activity leading to the observed neurological and platelet-based complications of SARS-CoV-2 infection requires further investigation. Some early studies of hospitalized patients indicated that 20–30% of COVID-19 patients develop some form of delirium or mental status change rising to 60–70% for those patients with severe illness of all age groups3. The exact mechanisms are not understood although a number of possible causes have been proposed including direct viral invasion, cerebrovascular involvement, hypoxia, pyrexia, dehydration, hyperinflammation (cytokine storm), medications or metabolic derangements3. The X-ray structure of SARS-CoV-2 spike protein binding to Angiotensin-converting enzyme 2 (ACE2)5 as well as recently demonstrated specific structural features of SARS-CoV-2 spike protein6, suggest specific features of SARS-CoV-2 spike protein structure for binding to human protein in the SARS-CoV-2 influence virulence and major relevance of this protein and its interactions in the development of drugs against the virus8.COVID-19 is an ongoing major global health emergency caused by severe acute respiratory syndrome coronavirus SARS-CoV-2. Patients admitted to hospital with COVID-19 show a range of features including fever, anosmia, acute respiratory failure, kidney failure and gastrointestinal issues and the death rate of infected patients is estimated at 2.2%9 is an unpleasant experience for patients, relatives and healthcare staff and is associated with negative outcomes such as dementia and death. As has been described by Fong et al.10, whilst even those with the most resilient of brains can develop delirium in the face of severe stressors, delirium in the face of more moderate insults may be a sign of underlying neurodegeneration11. An improved understanding of the causes of postoperative delirium could provide a better appreciation of the vulnerabilities causing delirium following surgery, as well as following SARS-CoV-2 infection.Delirium, an acute disorder of attention and cognition12. Metabolomic analysis of body fluids, including blood and CSF, provides a wide-ranging molecular window into the major processes of the body including an insight into the brain metabolism. CSF metabolite biomarkers of delirium risk have already been identified13. Major differences in the concentrations of polyamines (including spermidine and putrescine) are present in delirium-prone patients even before surgery or the presentation of delirium. However, it is not clear whether such metabolic changes occur more peripherally in the blood circulation, or whether the transfer of these metabolite across the blood–brain barrier is important.Planned orthopedic surgery under spinal anaesthesia provides a unique opportunity to preoperatively sample cerebrospinal fluid (CSF) in a group of patients where an estimated 17% will subsequently develop delirium14 presented metabolomics and proteomics analysis of serum of mild and severe COVID-19 patients indicating major concentration differences in serotonin, kynurenine, a number of amino acids, as well as, alterations in tryptophan and polyamine metabolic pathways. The metabolomics data were in agreement with clinical studies showing coagulopathy, stimulated by platelets, as one of the major issues in severe COVID-19 cases14, Additionally, delirium, possibly associated with changes in neurotransmitters has been indicated as a severe consequence of SARS-CoV-2 infection in some patients15. The involvement of the mitochondrial membrane bound enzyme monoamine oxidase (MAO) emerges as a common potential candidate causing many of the observed COVID-19 side-effects. MAO is important for neurotransmitter metabolism, has prior associations with delirium, and is involved in platelet regulation and coagulation18 as well as anosmia19. MAO including MAOA and MAOB are flavoenzymes that catalyze the oxidative transformation of monoamines. Inhibition of these enzymes is an established therapeutic target which is still in development, and the selectivity of candidate molecules for one isoform over another is a key consideration21. Alterations in the activities of MAOs is a potential source of various neuropsychiatric disorders including depression, autism or aggressive behavior23. Additionally, the function of MAOs represents an inherent source of oxidative stress, leading to the damage and death of neurons, ultimately leading to neurodegenerative diseases such as Parkinson's or Alzheimer's disease25.Recently, Shen et al.13. Briefly, patients aged 65 years or older entering the elective hip or knee arthroplasty to a single surgical centre were eligible for inclusion. Exclusion criteria included a pre-existing diagnosis of dementia or other neurodegenerative condition. Participants were assessed for delirium daily postoperatively by a single researcher for the first three days. Confusion Assessment Method (CAM) was used for the delirium diagnosis. The incidence of postoperative delirium was 14%. Cohort had a mean age of 74.4 years and 57% were female. Paired CSF and blood metabolomics analysis was undertaken for 54 age and gender matched patients where 28 of the patients experienced post-operative delirium (based on CAM test) and 26 did not show any delirium symptoms (control). Metabolomic analysis was undertaken where sufficient CSF was available for age and gender matched delirium and control participants. General patient cohort characteristics are shown in Table p values. Details of the sample selection of power analysis for this set were provided previously13.Preoperative blood and CSF samples were collected within an observational cohort study of patients aged over 65 years without a diagnosis of dementia presenting for planned hip and knee replacements prior to the SARS-CoV-2 pandemic [approved by Office for Research Ethics Committee for Northern Ireland REC ref: 10/NIR01/5)]. All methods were performed in accordance with the relevant guidelines and regulations and all participants signed an informed consent form. The study methodology has been described in detail previously0/NIR01/513. In this investigation the corresponding blood plasma samples of each of the same patients were examined by an identical kit-based methodology. Quantitative metabolomic profiling was performed using the Biocrates AbsoluteIDQ p180  using a Xevo TQ-MS triple-quadrupole mass spectrometer  as previously described26. Briefly, this comprised of two general methods: UPLC  reversed-phase  with multiple reaction monitoring (MRM), and flow injection analysis (FIA) also operating in MRM mode. Metabolite concentrations were calculated and expressed as micromolar (µM).The analysis of metabolic profiles of CSF samples from the nested case–control postoperative delirium cohort has previously been published14. The provided dataset includes metabolomic analysis of serum samples from patients with mild or severe COVID-19 as well as control subjects . Measurements of CSF samples is not available for this cohort. In the original study COVID-19 patients are classified into mild or severe subgroups as: (a) mild symptoms without pneumonia; (b) severe: fulfill any of the three criteria: respiratory distress, respiratory rate R 30 times/min; means oxygen saturation % 93% in resting state; arterial blood oxygen partial pressure/oxygen concentration % 300 mmHg (1 mmHg = 0.133 kPa). Briefly, authors used the ultra-performance liquid chromatography/tandem mass spectrometry (UPLC-MS/MS) for untargeted metabolomics of serum samples providing identification and quantification of 941 metabolites including 36 drugs and their metabolites. Details of methodology and validation are provided in the original publication14.Plasma metabolomics profiles of SARS-CoV-2 patients were published and made available by Shen et al.27, TMeV28 and Python—Jupyter Notebook were used for the analysis of metabolomics data. Principal component analysis (PCA) and 2-class Partial Least Squares-Discriminant Analysis (PLS-DA) model investigation was performed using jupyter notebook on data that was log10 transformed with selection to have an under 20% standard deviation and fewer than 10% of missing values in measurements for all samples. Selection of major features in different groups was performed using Significant Analysis of Microarrays (SAM)29. Machine learning analysis was performed using Python and Matlab with the results of Python Random Forest classification with SHAP (SHapley Additive exPlanations) algorithm shown to explain the output of machine learning30. SHAP routine was adapted from31.Different machine learning and statistical methods running under Matlab 2020 (Matworks Inc), Orange 3.25Feature selection with Random Forest method was performed using a robust probabilistic method for our sample in order to augment our training set and, at the same time, utilize the actual errors associated with the measurements. Errors were estimated at 2 percent of the absolute value of measurements. Here, we adopt a Gaussian function with the standard deviation equals the errors . Considering these errors, random sampling of the function should provide acceptable values. In this way, we have augmented our training set significantly. This data set is then randomly separated into a training set (75%) and a validation set (25%) to control overfitting. The uncertainties, including the size of the sample, are reflected in the error bars. The Random Forest procedure was as follows: (1) The most effective feature among all the features (metabolites) is found by RF; (2) The 'first' most effective feature is recorded and then eliminated from the table; (3) Remaining features are used to find the second most effective parameter among the subgroup. (4) This method is repeated to find the rest of the important features providing ranking for all features. RF was performed with bootstrap sampling. In order to obtain optimal probabilistic results, the method is repeated 100 times to obtain 100 different results providing distribution for each feature. Gaussian function is subsequently fitted to the distribution, and the final metabolite ranking is obtained from the mode of the fitted function. The error in the ranking, for a selected parameter, is estimated from the scatter (one sigma) of the function. This method is implemented within Jupyter Python notebook.distcorr in Matlab32. Distance correlation provides information about linear and non-linear correlation there-by giving insight into both direct and indirect feature correlations. Distance correlation analysis was utilized in order to explore differences in metabolite behaviour across the blood–brain-barrier in the two patient groups. Correlation was calculated between each metabolite in CSF and all metabolites in blood providing correlation measures across the blood brain barrier. Metabolites are selected based on the change in the distance correlation between the two subject groups determined by performing linear regression analysis of distance correlation for a metabolite in delirium-prone group vs control group. Significant change in correlation between control and delirium-prone group was obtained using linear regression analysis (using regression function in Matlab) between distance correlation values for each metabolite in CSF relative to all other metabolites in blood. Metabolites showing negative correlation (regression R < 0) are shown as significantly changed.Correlation between metabolites in the CSF and blood was determined using Distance correlation using function https://www.ebi.ac.uk/Tools/msa/clustalo/). A comparison of the structures of MAOB and ACE2 bonded to SARS-CoV-2 Spike proteins are performed using UCSF Chimera, Schrodinger software package (Schrodinger Inc.) and PDB Data Bank structure analysis methodologies including jFATCAT33. Protein X-ray structures were obtained from https://www.rcsb.org/ and included for MAOB protein PDB ID 1GOS34 and ACE2 bonded to SARS-CoV-2 Spike protein PDB ID: 6M0J.Protein sequences off MAOB, ACE2 and SARS-CoV-2 Spike protein were obtained from UniProt database and compared using Clustal Omega  using OPLS3e force field after addition of hydrogens13. The importance of specific metabolites to the model's discriminatory power are shown in coefficient plots , but also 3-hydroxypalmitoleylcarnitine (C16-1-OH), linoleic acid (C18-2), carnitine (C0) and, to a lesser extent, a number of other metabolites . Threonine was not one of the most significant contributors to the classification coefficients, but was still a significant contributor to the PLS-DA sample grouping. SAM analysis of blood metabolites shows major differences in pre-operative blood samples in proline, threonine, lysine, ornithine and phosphatidylcholines .The above results were further corroborated by application of Statistical Analysis for Microarrays (SAM)a36 Fig. A. SAM anRandom forest (RF) is a supervised learning algorithm used here for the selection of significant metabolic features for classification between two patient groups. RF method builds an ensemble of decision trees that is in this application used to determine significance of each feature for the classification problem. Panel of the most diagnostically significant features is shown in Fig. The availability of metabolic profiles for CSF and blood provides a unique possibility for the determination of significant differences in metabolite concentrations between these two body fluids. Comparison of concentrations in CSF and blood can provide information about the transfer and metabolism across the blood brain barrier as well as the possibility for the determination of blood-based biomarkers that are representative of changes in the CSF Fig. . As can 31. Major changes in the behaviour of metabolites is selected based on linear regression analysis of distance correlation for a metabolite in delirium-prone group vs control group. Selected in Fig. 37 are shown in Fig. Behaviour of metabolites in the two patient cohorts was further investigated using distance correlation analysisMany of the proteins presented in Fig. Further analysis of the interaction between metabolite in CSF and blood is observed through the analysis of correlation network obtained using distance correlations. This metabolic network across the blood brain barrier is visualized in Fig. Number of metabolites show different interaction networks in the control and delirium-prone cohort. Differences in correlations are specifically indicated for three metabolites showing opposite relative concentration in CSF and blood with PEA for example showing strong correlation with ornithine only in the delirium-prone cohort. Carnitine derivatives as well as number of glycerophospholipids also show significantly different correlation partners across the blood–brain-barrier in the delirium-prone group indicating activation of different metabolic pathways in the two patient groups.Observed changes in PEA levels suggest changes in activity of MAO in delirium-prone patient cohort. MAOB is the only MAO protein found in platelets . For comparison we are providing major features selected using statistical, SAM methods  and ACE2 (PDB structure 6M0J), showed only 51% overall structural similarity. However the comparison of the ACE2—spike protein binding region with MAOB resulted in 90% to 100% structure overlap. Further computational analysis of the spike-protein relation to MAOB protein is shown in Fig. In order to investigate the possibility that the SARS-CoV-2 virus directly influences MAOB function the structures of MAOB and ACE2 (as a known binding target of SARS-CoV-2) were compared33 resulted in ACE2 similarity of 42% and MAOB similarity of 51% with the P-value: 6.67e−01. However, further analysis of subsection of ACE2 involved in binding to Spike protein indicated as 1 and 2  inhibitor42. Interestingly, MAO is involved in the metabolism of polyamines, some of which were significantly altered in the delirium-prone group and in particular PEA.The important features identified in blood and CSF can be grouped as either polyamines, acetylcarnitines or glycerophospholipids. RF ranking determined 3-methylglutarylcarnitine (C5-M-DC) as one of the most significant features in both blood and CSF. Serum levels of acetylcarnitines have previously been shown to progressively decrease from healthy through subjective memory complaint, mild cognitive impairment, up to Alzheimer’s disease (AD)43. PEA has been shown to alter the serotonin transporter by interacting with trace amine-associated receptor 1 (TAAR1)43. Activation of TAAR1 with PEA significantly inhibits uptake and induces efflux of dopamine and norepinephrine.PEA was one of the most profoundly affected metabolites in patients who later experienced postoperative delirium. In delirium-prone individuals PEA concentrations are slightly reduced both in the CSF and blood plasma relative to the control group. Interestingly, for PEA an inverse relationship exists in these two compartments for these two groups with the significantly smaller PEA concentration in CSF relative to blood in delirium-prone cohort. Brain monoamines include common biogenic amines  and trace amines such as PEA18. MAO is an enzyme localised on the outer mitochondrial membrane and it preferentially degrades benzylamine and PEA. The MAO family of proteins oxidizes a number of different amine substrates including small-molecule monoamines, polyamines as well as modified amino acids in proteins, and directly influences number of different metabolites , one of the key enzymes responsible for its metabolism, is a known target for treatment of a variety of neurological conditions including depression, Parkinson’s disease and recently AD44 which is partly due to reduced PEA concentrations.Two MAO subtypes exist: monoamine oxidase A (MAOA) which preferentially oxidizes biogenic amines such as 5-hydroxytryptamine (5-HT), norepinephrine and epinephrine, and monoamine oxidase B (MAOB) which performs oxidative deamination of biogenic and xenobiotic amines. MAOB is particularly important for the metabolism of neuroactive and vasoactive amines in the central nervous system and peripheral tissues. Expression of MAOB increases with age and is associated with increased free radical damage and ROS formation. This in turn leads to a decrease in neuronal mitochondrial function and ultimately neurodegeneration45 (https://www.nice.org.uk/). A number of publications have also investigated the therapeutic effects of MAO inhibitors for other neurological conditions, such as AD, Parkinson’s disease, and depression18. Specifically, MAOB has been proposed as a possible therapeutic target for AD due to its association with aberrant GABA production, but it also has therapeutic relevance for Parkinson’s disease due to its role in dopamine depletion46.MAO inhibitors have been extensively developed and utilized for treatment of depressionhttps://www.nice.org.uk/). Additionally, MAOI has been linked to an increasing suicide, pyrexia, delirium, hypoxia, hypertension and fatal intravascular coagulation49. MAOB is highly expressed in neurons, as well as platelets50 possibly explaining the observed effects of MAOB inhibitors on neurological state as well as blood based complications .Utilization of MAO inhibitors (MAOI) for depression treatment has resulted in a number of side effects including: agitation, irritability, ataxia, movement disorders, insomnia, drowsiness, vivid dreams, cognitive impairment, and slowed speech, hallucinations and paranoid delusions , phenylalanine metabolism (P = 0.068938). Major changes in the network between metabolite in CSF and in blood observed between control and delirium-prone cohort  also experience low blood oxygen levels, elevated urea, acute renal dysfunction52. Differences in the activity of MAOB in surgical delirium-prone patient population were indicated by a change in the concentration of PEA as well as polyamines. Observed changes in the concentrations of carnitine derivatives in this patient cohort can be linked with previously established relationship between acetyl l-carnitine and dopamine through its effect on MAOB expression in the presence of anesthetic37. Significant concentration increases in severely affected COVID-19 patients is observed for both indolacetate and kynurenine as well as apparent increase in metabolising of serotonin in severe cases . In addition to other symptoms, SARS-CoV-2 causes hematological changes which include reduced platelet count53, platelet hyperactivity, changed gene expression in platelets (particularly in relation to protein ubiquitination), altered antigen presentation, and mitochondrial dysfunction in platelets54. Further analysis of the relationship between SARS-CoV-2 and platelets by Manne et al. have shown presence of mRNA of the SARS-CoV-2 N1 gene in isolated platelets from 2 ICU COVID-19 patients even in absence of ACE2 gene in these cells54. Additionally, SARS-CoV-2 mRNA was determined in platelets of COVID-19 patients in the recent study of Zaid et al.55 as well. Study by Zhang et al.56 shows that platelets expressed ACE2, a host cell receptor for SARS-CoV-2, as well as TMPRSS2, a serine protease for Spike protein priming and that the virus associates closely with platelets according to Transmission electron microscopy analysis in an ACE2 dependent manner. This study has also shown that SARS-CoV-2 and its Spike protein directly activates platelets and potentiates their prothrombotic function and inflammatory response. Furthermore, Real et al.57 shows that platelets from HIV infected patients can contain replication-competent HIV virus.Previously reported links between HIV infection and changes in monoamine and acylcarnitine metabolites (as well as inflammatory markers) indicate that viral infection and inflammation can alter monoamine metabolism and mitochondrial energeticsses Fig.  corrobor58.Given the known role of MAO in a number of SARS-CoV-2 symptoms this prompted us to perform a computational analysis examining SARS-CoV-2 virus spike protein interactions with MAO. The preliminary computational structure comparison of MAOB and ACE2 protein performed here determined whether there is a possibility that the SARS-CoV-2 spike protein binds to MAOB Fig. . Althoug16. At the same time, interference with MAOB activity in subjects with overactive MAOB can lead to the side-effects observed in MAOB inhibition as well as SARS-CoV-2 infection. The metabolomic data and symptom similarities of delirium prone patients from a surgical setting and COVID-19 patients indicate potential dysfunction in MAOB. Dexamethasone recently shown to improve survival in severe COVID-19 cases59 has been documented in the past to increase oxidative stress and the expression of MAOA60 and MAOB61 in dopaminergic neurons62. Computational modeling defines a mechanism by which the spike protein could directly bind to MAOB thereby interfering with its normal function and particularly affecting patients with increased MAOB expression. Detailed computational docking analysis shows strongest binding of spike protein to the region of MAOB recently proposed as the membrane mediated substrate entrance to the active site of MAOB62. Further analysis is currently under way to explore in greater detail the role of MAOB in delirium and SARS-CoV-2 infection with further exploration of the effects of sex and other demographic, medical or drug utilization on the delirium related metabolic changes.The over-activity of MAOB can result in the observed PEA concentration decrease and the major changes observed in the polyamines as well as related amino acids in the delirium-prone patients. Pro-inflammatory stimuli, including cytokines lead to MAO-dependent increases in reactive oxygen species causing mitochondrial dysfunctionSignificant differences were observed in a number of mono- and polyamines which led us to investigate in some detail the relationship between the observed changes in operative delirium and delirium caused by SARS-CoV-2 and to propose a hypothetical relationship between monoamine oxidase and the SARS-CoV-2 spike protein. Experimental analysis of the relationship between delirium and SARS-CoV-2 and the possibility for spike-protein binding to monoamine oxidase is currently underway. Further research is required to establish what effect MAOB inhibitors might have on these pathways. There is no evidence at present to support the withholding or increasing MAOB inhibitors in COVID-19 treatment.Supplementary Information.


Models trained on bone-suppressed CXRs improved detection of TB-consistent findings and resulted in compact clustering of the data points in the feature space signifying that bone suppression improved the model sensitivity toward TB classification.Chest X-rays (CXRs) are the most commonly performed diagnostic examination to detect cardiopulmonary abnormalities. However, the presence of bony structures such as ribs and clavicles can obscure subtle abnormalities, resulting in diagnostic errors. This study aims to build a deep learning (DL)-based bone suppression model that identifies and removes these occluding bony structures in frontal CXRs to assist in reducing errors in radiological interpretation, including DL workflows, related to detecting manifestations consistent with tuberculosis (TB). Several bone suppression models with various deep architectures are trained and optimized using the proposed combined loss function and their performances are evaluated in a cross-institutional test setting using several metrics such as mean absolute error (MAE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and multiscale structural similarity measure (MS–SSIM). The best-performing model (ResNet–BS)  is used to suppress bones in the publicly available Shenzhen and Montgomery TB CXR collections. A VGG-16 model is pretrained on a large collection of publicly available CXRs. The CXR-pretrained model is then fine-tuned individually on the non-bone-suppressed and bone-suppressed CXRs of Shenzhen and Montgomery TB CXR collections to classify them as showing normal lungs or TB manifestations. The performances of these models are compared using several performance metrics such as accuracy, the area under the curve (AUC), sensitivity, specificity, precision, F-score, and Matthews correlation coefficient (MCC), analyzed for statistical significance, and their predictions are qualitatively interpreted through class-selective relevance maps (CRMs). It is observed that the models trained on bone-suppressed CXRs  significantly outperformed ( The World Health Organization (WHO) reports that millions of people suffer from lung-related diseases and their complications worldwide . Chest XBone suppression involves subtracting the bones from the CXRs to create a soft-tissue image. It would be of potential use to radiologists and CADx systems in screening for subtle lung abnormalities by increasing the quality of soft tissue visibility. A common practice for suppressing bony structures involves the use of dual-energy subtraction (DES) chest radiography. The DES-based radiographic acquisition is performed to improve diagnosis by producing two different images, thereby separating the bony structures from the soft tissues. However, compared to conventional CXRs, DES has several limitations, namely, (a) DES radiography exposes the subject to slightly higher radiation doses compared to conventional CXR acquisition protocols and is not recommended for patients younger than 16 years of age ; (b) DESLiterature review reveals several image processing techniques for automated detection and removal of bony structures in CXRs ,7. In onInspired by their superior performance in natural and medical image recognition tasks ,15, convBone suppression would help detect TB-consistent findings that often manifest in the apical lung regions so that their visibility is not obstructed by the occlusion of ribs and clavicles ,19. The In this study, we propose a systematic methodology toward training customized ConvNet-based bone suppression models and evaluating their performance toward classifying and detecting TB-consistent findings in CXRs: First, we retrain an ImageNet-trained VGG-16  model onThe contributions of this retrospective study are highlighted as follows:(i) This is the first study to propose and compare the performance of several customized ConvNet-based bone suppression models with a diversified architecture, including a sequential ConvNet model, an autoencoder (AE) model, a residual learning (RL) model, and a residual network (ResNet) model toward suppressing bones in CXRs.(ii) This study performs rigorous empirical evaluations, statistical significance analysis, and qualitative evaluation of the bone suppression and classification models. (iii) The models proposed in this study are not limited to the task of CXR bone suppression but can potentially be extended to other image denoising problems.The rest of the study is organized as follows: The materials and methods are further divided into the following sub-sections: (i) datasets and their characteristics, (ii) bone suppression models, (iii) evaluating bone suppression models, (iv) histogram similarity assessment, (v) classification models, and (vi) statistical analysis.The following CXR collections are used in this study:(i) JSRT CXR: The JSRT  released(ii) NIH–CC–DES CXR: A set of 27 DES CXRs is acquired as a part of routine clinical care using the GE Discovery XR656 digital radiography system . The DESn = 326) is used in this study.(iii) Shenzhen TB CXR: This de-identified dataset contains 326 CXRs with normal lungs and 336 abnormal CXRs showing various TB manifestations . The CXRn = 58) is used in this study. (iv) Montgomery TB CXR: The CXR images and their associated radiology reports in this collection are acquired through the TB control program of the Department of Health and Human Services of Montgomery County, Maryland, USA . The col(v) Radiological Society of North America (RSNA) CXR: A subset of the NIH CXR dataset  is curat(vi) Pediatric pneumonia CXR: A collection of 4273 CXRs, acquired from children 1 to 5 years of age, showing bacterial and viral pneumonia manifestations, and 1493 normal CXRs is publicly available .The researchers from the Budapest University of Technology and Economics used their in-house clavicle and rib–shadow removal algorithms to suppress the bones in the 247 JSRT CXRs and made the bone-suppressed soft-tissue images publicly available . Affine Several ConvNet-based bone suppression models with varying architecture are trained on this augmented dataset. We evaluated their performance with the cross-institutional NIH–CC–DES test set. During training, we allocated 10% of the training data for validation using a fixed seed. Four different model architectures are proposed toward the task of bone suppression in CXRs as follows: (a) Autoencoder (AE) model (AE–BS) where BS denotes bone suppression; (b) Sequential ConvNet model (ConvNet–BS); (c) Residual learning model (RL–BS); and (d) Residual network model (ResNet–BS). The architectures of these models are as follows:(i) AE–BS Model: The AE–BS model is a convolutional denoising AE with symmetrical encoder and decoder layers. The encoder consists of three convolutional layers with 16, 32, and 64 filters, respectively. The size of the input is decreased twice at the encoder layers and increased correspondingly in the decoder layers. As opposed to the conventional denoising AEs, the noise in the proposed AE–BS model represents the bony structures. The model trains on the original CXRs and their bone-suppressed counterparts to predict a bone-suppressed soft-tissue image. (ii) ConvNet–BS model: The ConvNet–BS model is a sequential model consisting of seven convolutional layers having 16, 32, 64, 128, 256, 512, and 1 filter, respectively. Zero paddings are used to preserve the dimensions of the input image at all convolutional layers. Lasso regularization (L1) penalties are used at each convolutional layer to induce penalty on weights that seldom contribute to learning meaningful feature representations. This helps in improving model sparsity and generalizing to unseen data. The deepest convolutional layer with the sigmoidal activation produces the bone-suppressed soft-tissue image. (iii) RL–BS model: The architecture of the RL–BS model consists of eight convolutional layers having 8, 16, 32, 64, 128, 256, 512, and 1 filter, respectively. Zero paddings are used at all convolutional layers to preserve the dimensions of the input image. The RL–BS model learns the residual error between the predicted bone-suppressed image and its corresponding ground truth. The deepest convolutional slayer produces bone-suppressed images. (iv) ResNet–BS model: The architecture of the proposed ResNet–BS model is illustrated in Inspired by , ReLU acThe bone suppression models are trained to suppress the bony structures in the CXRs and produce soft-tissue images. This can be treated as an image denoising problem in which the bones are considered noise. To obtain superior bone suppression results, we aim to reduce the error between the predicted bone-suppressed image and its ground truth and maximize the structural similarity. The selection of the loss function plays a prominent role in the bone suppression task. combined loss. Other pixel-based evaluation metrics used in this study include peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). The mean-squared error (MSE), also known as L2 loss, is a pixel loss measure that computes the sum of the squared distance between the predicted image and its ground truth. However, MSE does not interpret the quality of the predicted image. The MAE, otherwise called L1 loss, computes the sum of absolute differences between the ground truth and the predicted image. Studies in the literature reported that, unlike MSE, MAE provides a more natural measure of average error and is useful in performing intercomparisons of average model performance errors [In this study, the performance of the proposed bone suppression models is evaluated through constructing a loss function that benefits from the combination of mean absolute error (MAE) and multiscale structural similarity index measure (MS–SSIM) losses, herein referred to as e errors . PSNR coe errors  reveals e errors  reveals e errors ,37,38. WWe set the value of Ω = 0.84 after empirical evaluations. Greater weight is given to MS–SSIM since we want the bone-suppressed image to be highly similar  to the ground truth. The MAE is given lower significance in this measure since it focuses on overall luminance and contrast in the image, which is expected to change due to bone (white pixels) suppression.The histograms of the ground truth and the bone-suppressed image predicted by the proposed models are plotted and compared to observe their tonal distributions. Various metrics including correlation, intersection, chi-square distance, Bhattacharyya distance, and Earthmover distance (EMD) are used to compare these histograms and provide a measure of similarity. The higher the value of correlation and intersection, the closer (or more similar) is the histogram of the image pairs. This implies that the histogram of the predicted bone-suppressed image closely matches that of the ground truth. For distance-based metrics including chi-square, Bhattacharyya, and EMD, a smaller value indicates a superior match between the histogram pairs, signifying that the predicted bone-suppressed image closely matches that of the ground truth. The mathematical formulations of these metrics can be found in the literature .In this study, an ImageNet-pretrained VGG-16 model  is retraDuring model training, data are augmented with random horizontal and vertical pixel shifts (−5 to 5 pixels), horizontal mirroring, and rotations (−10 to 10 degrees) to introduce data diversity into the training process and reduce overfitting to the training data. Class weights are used to penalize majority classes and reduce class imbalance errors. The models are trained and evaluated using stochastic gradient descent (SGD) optimization to estimate learning error and classification performance. Callbacks are used to store checkpoints of the models. The model weights delivering superior performance with their respective validation fold are used for further analysis.n = 68) of the Shenzhen TB dataset were provided by another expert radiologist with more than 30 years of experience. The web-based, VGG Image Annotator tool [The ground truth disease annotations for the Montgomery TB dataset were provided by an expert radiologist with more than 45 years of experience. The ground truth disease annotations for a subset  Accuracy, (b) AUC, (c) Sensitivity, (d) Specificity, (e) Precision, (f) F-measure, and (g) Matthews correlation coefficient (MCC). The predictions of the best-performing models trained on the baseline and bone-suppressed data are interpreted through CRM-based visualization. A WindowsWe performed statistical analyses to identify the existence of a statistically significant difference in performance between the models. For the bone suppression task, we used 95% confidence intervals (CI) as the “Wilson” score interval for the MS–SSIM metric to compare the performance of the proposed bone suppression models and estimate their precision through the error margin. For the classification task, we used one-way analysis of variance (ANOVA)  to invesn = 27). The performance achieved by the various bone suppression models is shown in Recall that the proposed bone suppression models are trained on the augmented JSRT dataset and its bone-suppressed counterpart. The performance of the trained models is evaluated with the cross-institutional NIH–CC–DES test set  and the ConvNet–BS and RL–BS models for the PSNR metric (p < 0.05). For other metrics, the ResNet–BS model demonstrated superior performance than the CNN–BS, and RL–BS models. It is observed that the 95% CI for the MS–SSIM metric achieved by the ResNet–BS model demonstrates a tighter error margin and hence higher precision, compared to the other models. The ResNet–BS model demonstrated the least values for the combined loss, MAE, and MS–SSIMWe observed from The best performing ResNet–BS model is further used to suppress bones in Shenzhen and Montgomery TB CXR collections. Recall that for the classification task, the CXRs in the Shenzhen and Montgomery TB CXR collections are split at the patient level into four equal folds for performing cross-validation studies. The mean performance of the cross-validated models is given in p-values for Shapiro–Wilk and Levene analyses are greater than 0.05 (Shapiro–Wilk (p) = 0.1022 and Levene (p) = 0.1206). This demonstrated that the assumptions of the data normality and variance homogeneity are satisfied. Through one-way ANOVA analysis, we observed that a statistically significant difference existed in the AUC values achieved by the baseline and bone-suppressed models  = 5.943, p = 0.005). This underscored the fact that the AUC values obtained by the bone-suppressed models are significantly superior to those achieved by the baseline models. We performed similar analyses using the MCC metric. We observed from Shapiro–Wilk and Levene analyses that the assumptions of the normal distribution of data and variance homogeneity hold valid (Shapiro–Wilk (p) = 0.7780 and Levene (p) = 0.4268). We observed that there existed a statistically significant difference in the MCC values obtained by the baseline and bone-suppressed models  = 17.58, p = 0.00573). This demonstrated that the MCC values obtained by the bone-suppressed models are significantly higher compared to the baseline models.We performed one-way ANOVA to analyze the existence of a statistically significant difference in the AUC and MCC metrics achieved by the baseline and bone-suppressed models trained on the Shenzhen TB CXR collection. One-way ANOVA assumes normality of data and homogeneity of variances. For the AUC metric, we observed that the p) = 0.4102 and Levene (p) = 0.5510) and (ii) a statistically significant difference existed in the AUC values obtained by the baseline and bone-suppressed models  = 11.13, p = 0.0157). Analyzing the MCC values led to the observation that (i) the assumptions of normal distribution of data and homogeneity of variances are satisfied (Shapiro–Wilk (p) = 0.6767 and Levene (p) = 0.808) and (ii) there existed a statistically significant difference in the MCC values obtained by the baseline and bone-suppressed models  = 10.48, p = 0.0177). This underscored the fact that the AUC and MCC values obtained by the bone-suppressed models are significantly higher than the baseline models. These statistical evaluations demonstrated the fact that the classification performance achieved by the bone-suppressed models toward TB detection significantly outperformed those trained on non-bone-suppressed images. A similar analysis is performed using the AUC and MCC metrics achieved by the cross-validated baseline and bone-suppressed models that are trained on the Montgomery TB CXR collection. Analyses of the AUC metric led to the observation that (i) the assumptions of data normality and variance homogeneity hold valid  CXR modality-specific model pretraining, (ii) model customization suiting the problem, (iii) statistical validation, (iv) localization studies with expert annotations conforming to the problem, and (v) feature embedding visualization. CXR modality-specific pretraining: Previous studies reveal that compared to using ImageNet weights, CXR modality-specific model pretraining results in learning meaningful modality-specific features that can be transferred to improve performance in a relevant classification task ,46. We pModel customization: Residual networks are one of the most commonly used backbones for computer vision tasks including segmentation, classification, and object detection . The usep < 0.05) existed in the MCC values obtained using the baseline and bone-suppressed models toward classifying the CXRs in the Shenzhen and Montgomery TB CXR collections. This demonstrated that the bone-suppressed models that are trained and evaluated individually on the Shenzhen and Montgomery TB CXR collections statistically significantly outperformed their baseline counterparts. Statistical validation: Studies in the literature that accomplish bone suppression in CXRs have not performed a quantitative assessment of the bone-suppressed images by comparing them to their respective ground truths . StatistLocalization studies: We observed from the CRM-based localization study that the bone-suppressed models learned meaningful feature representations conforming to the expert knowledge of the problem under study. On the other hand, the baseline models, though demonstrating good classification accuracy, revealed poor TB-consistent ROI localization. These models learned the surrounding context irrelevant to the problem to classify the CXRs to their respective classes. This led to an important observation that the model accuracy is not related to its disease-specific ROI localization ability.The average CRMs obtained using the Shenzhen and Montgomery TB CXR datasets, collected from TB clinics in two different countries, showed upper lung predominance. These observations conform to the findings in the literature  that disFeature embedding visualization: We visualized the feature space learned by the baseline and bone-suppressed models using the t-SNE dimensionality reduction algorithm that embeds the learned high-dimensional features into the 2D space. To this end, we observed that the bone-suppressed model demonstrated a compact clustering of the features learned for the TB and normal classes. The decision boundary between the normal and TB categories are well defined, showing that meaningful feature embeddings are learned by the bone-suppressed models.Limitations: This study, however, suffers from the following limitations: (i) To train and validate the proposed bone-suppression models, we used limited data that may not encompass a wide range of bone structure variability. With the increased availability of bone-suppressed CXRs in frontal and lateral projections, it would be possible to train deeper architectures with sufficient data diversity to build confidence in the models and improve their generalization to real-world data, and (ii) this study does not empirically identify the best classification model but investigates the impact of bone suppression on improving the classification performance in different TB datasets and substantiates the need for bone suppression toward improving TB detection. The impact of this approach on patient triage and treatment planning can only be theorized. Deriving guidance for them is beyond the scope of this study. In sum, models trained on bone-suppressed CXRs improved detection of TB-consistent findings resulted in compact clustering of the data points in the feature space, signifying that bone suppression improved the model sensitivity toward TB classification. The models proposed in this study are not limited to improving TB detection. The results suggest that the proposed ResNet–BS bone suppression model could be extended to other CXR applications such as improved performance in detecting and differentiating lung nodules, pneumonia, COVID-19, and other pulmonary abnormalities. This could further enhance the utility of digital CXRs for the evaluation of pulmonary disorders for underserved patients in low-resource or remote locations. We believe our results will improve human visual interpretation of TB findings, as well as automated detection in AI-driven workflows.