Non-small cell lung cancer (NSCLC) is the most prevalent type of lung cancer, which is the leading cause of cancer-related deaths worldwide. Over the past decades, tumour angiogenesis has been intensely studied in the treatment of NSCLC due to its fundamental role in cancer progression. Several anti-angiogenic drugs, such as recombinant endostatin (RE), have been evaluated in several preclinical and clinical trials, with mixed and often disappointing results. However, there is currently an emerging interest in RE due to its ability to create a vascular normalization window, which could further improve treatment efficacy of the standard NSCLC treatment. This review provides an overview of preclinical and clinical studies that combined RE and radiotherapy for NSCLC treatment. Furthermore, it highlights the ongoing challenges that have to be overcome in order to maximize the benefit; as well as the potential advantage of combinations with particle therapy and immunotherapy, which are rapidly gaining momentum in the treatment landscape of NSCLC. Different angiogenic and immunosuppressive effects are observed between particle therapy and conventional X-ray radiotherapy. The combination of RE, particle therapy and immunotherapy presents a promising future therapeutic triad for NSCLC. Globally, lung cancer is the second most commonly diagnosed cancer and leading cause of cancer related deaths . Non-smaDuring the early stages or dormant phases of cancer development, tumours can exist without blood supply for extended periods of time (months to years) . HoweverThe tumour angiogenesis process requires an imbalance between the different major pro- and anti-angiogenic factors, which have been extensively studied and defined over the past decades . This imIn NSCLC specifically, HIF-1α and HIF-2α are commonly overexpressed, correlating with poorer survival and increased microvascular density (MVD), respectively ,31. WorkSince tumour angiogenesis was first proposed in the pioneering work of Folkman in 1971 by the discovery of the first angiogenic factors, anti-angiogenic drugs have been earmarked as a promising cancer strategy . With thThis is particularly important for NSCLC, known as a highly vascularized tumour, where histological evidence of enhanced angiogenesis has been associated with poor prognosis ,49. As aIn this review, we will focus on a drug that was inspired by one of the first anti-angiogenic factors to be discovered, namely endostatin, produced by murine haemangioendothelioma cells in Folkman’s laboratory . It is oEndostatin is a C-terminal fragment cleaved from the NC1 domain of Collagen XVIII via the proteolytic activity of proteinases like elastase, procathepsin L, and matrix metalloproteinases (MMPs) . SeveralMolecular studies have reported that RE can induce the attenuation of focal adhesions, a functional protein complex that links the actin cytoskeleton of the endothelial cells to the underlying basal membrane in human dermal microvascular endothelial cells . In contRE also possesses ATPase activity, which led to the development of an engineered form of endostatin, which exhibits much higher ATPase activity than the wildtype one . It was Collectively, these studies fall under a large blanket of experimental findings that show RE’s effect on the vascular endothelium and successfully served as a validation of the original work by O’Reilly and colleagues . ImportaThe formulation of RE, a protein drug, is coupled with limitations such as poor bioavailability, insoluble and unstable nature, and high production cost. Therefore, several attempts were made to improve RE structurally, that include PEGylation of its N-terminus, the addition of an RGD (Arg-Gly-Asp) sequence that is present in integrin ligands, fusing endostatin to the Fc region of IgG, or the addition of Zinc ,84,85,86RE-loaded nanoparticles have also shown anti-angiogenic effects in vivo; for example, a folic acid-decorated chitosan nanoparticle successfully targeting squamous cell carcinoma (SCC) ,90,91. VAs previously mentioned, the administration of anti-angiogenic therapy as a radiosensitizer seems paradoxical. Blocking the formation of blood vessels could enhance hypoxia within the tumour and contribute to increased radioresistance  98,99].,99.98,99nu/nu mice [56Fe ion radiation [In addition to the dilemma created by the normalization window and the optimal timing between drug administration and fractionated RT, anti-angiogenics have not been investigated in combination with particle therapy. This is a rapidly growing field within RT, where particles such as protons and carbon-ions are used which present physical and radiobiological advantages over conventional high-energy X-rays to treat cancer ,112,113./nu mice . The A54/nu mice . Takahas/nu mice . High-en/nu mice . Dose-de/nu mice . In contadiation . The radThe current status and study progress on the normalization window of RE was comprehensively reviewed by He et al. . RE’s efRE’s vascular normalization capability has also been exhibited in other cancer models. In colon and nasopharyngeal carcinoma-bearing mice, RE treatment inhibited vascular endothelial growth and increased pericyte coverage, which led to tumour vascular normalization ,127. A n60Co γ-rays). Interestingly, there are less preclinical studies compared to clinical evaluations of native RE in combination with RT. 137Cs γ-rays showed pronounced tumour growth in mice bearing A431 cell epidermoid tumour xenografts. RE had little to no effect on tumour cell apoptosis over time, while IR alone significantly increased tumour cell apoptosis. RE combined with RT, however, increased tumour cell apoptosis by a factor of two and blocked tumour revascularization [Tumour growth inhibition was often noted in in vivo models . Normalirization . Finallyrization ,136. By rization .As illustrated in Numerous clinical trials and meta-analyses have demonstrated a significant survival benefit with an acceptable safety profile when treating late-stage NSCLC patients with RE, including synergistic effects with concurrent chemoradiotherapy (CCRT), such as vinorelbine, platinum-based chemotherapy, docetaxel, and etoposide. These improvements were also seen in patients resistant to previous chemotherapy or patients with complete surgical resection ,155,156.et al. clarified via a meta-analysis that the benefits of the addition of RE to CCRT in NSCLC are associated with a significantly higher ORR (objective response rate), disease control rate (DCR), and survival rate compared to CCRT, with similar incidences of main adverse events [et al. concluded that RT combined with endostatin may be a promising strategy for locally advanced NSCLC patients with poor performance status who cannot tolerate chemotherapy [RE combined with photon-based RT has also been studied extensively to validate its function as a hypoxic tumour radiosensitizer . Multiple events . The poootherapy . Howeverotherapy . Continuotherapy . Interesotherapy ,163. Recotherapy . HoweverNext to CCRT, immune checkpoint inhibitors (ICIs) and anti-angiogenic drugs are gaining momentum as a promising combined treatment strategy for NSCLC. Nivolumab, atezolizumab, and pembrolizumab have been approved as second-line treatments for advanced NSCLC . RecentlTargeting angiogenesis as a tumour treatment strategy is undoubtedly complex, since pathological angiogenesis causes numerous changes in the TME. In addition, potential elevated toxicity in chemotherapeutic-anti-angiogenic combinations, and the impact of the normalization window and resulting oxygenation status on RT are still a topic of debate. To assess the ability of an additional variable to potentiate the effect of a treatment, it is of critical importance to first assess the impact of each individual treatment alone and understand the underlying biological mechanisms. To that end, photon-based RT as a standalone target against tumour angiogenesis has been shown to cause angiogenic stimulation when compared to proton therapy (PT) . There iNSCLC is known as a radioresistant cancer due to the presence of cancer stem cells, an epithelial-mesenchymal transition, and its high proportion of hypoxic cell populations ,170. RE Anti-VEGF therapy supresses neovascularization efficiently, whilst mature blood vessels are not as affected. This has been postulated to be attributable to a loss in dependence on growth factor signalling by mature vessels and potential anti-VEGF-A/VEGFR pathway therapy resistance as a consequence . It is c


Significantly improved survival in patients with pulmonary hypertension (PH) has raised interest in maintaining a good quality of long-term survivorship. In this study, health-related quality of life (HRQOL) measurement was used to assess the long-term changes of physical and mental outcomes.A total of 559 consecutive inpatients with PH completed generic HRQOL (Short Form-36) who were diagnosed with PH by right heart catheterization. Assessments were carried out at short-term (1 year), midterm (3 years), and long-term (5 years) follow-ups.Patients with PH suffered more severe impairments in both physical and emotional domains than the U.S. population normative values. Patients with PH due to chronic lung disease had the worst physical component summary (PCS) score, but there was no difference in mental component summary (MCS) score among different PH types. A reduced PCS score was correlated with WHO FC severity and pulmonary vascular resistance (PVR). The Z score showed that the changing trend of mental conditions continuously declined from baseline to midterm and long-term follow-ups, but the PCS score seemed to be stable or improved. Cox regression analysis indicated increased baseline PVR and WHO FC III and IV, and decreased physical subscale of role physical, mental subscale of social functioning, and the MCS score have increased risk of mortality in the long-term follow-up.Patients with PH have poor HRQOL. The long-term change of physical status seemed to be stable, but the mental state was continuously worse. These suggested identifying and intervening mental health progresses is a noteworthy issue in PH chronic management. Pulmonary hypertension (PH) is a pathophysiological disorder that involves multiple clinical conditions and complicates the majority of cardiovascular and respiratory diseases . AccordiOver the past two decades, clinical research in cardiopulmonary disease has broadened from a physiological base to a more comprehensive approach of the health-related endpoint . The MedHowever, long-term information about quality of life, functional result, and mental changes in PH is still limited. The main focus of this study was to gather data from short-term, midterm, and long-term cohorts of survivors of PH by measuring generic HRQOL (SF-36). The secondary objective was to analyze the changing trend of HRQOL from baseline to long-term follow-up. Finally, we explored the prognostic risk factors associated with long-term mortality among physical and mental components.We consecutively enrolled adults patients with PH (≥18 years of age at diagnosis) with an established diagnosis of PH in Shanghai Pulmonary Hospital from November 2010 to January 2018. PH is defined by a mean pulmonary artery pressure (mPAP) of ≥ 25 mmHg at rest, measured by right heart catheterization (RHC) . ClinicaGeneral HRQOL was assessed using the Medical Outcomes Study 36-item Short-Form Health Survey  in all patients . It const-test for means. If the data were not normally distributed, the nonparametric two-sided Mann–Whitney U test was used. Correlations were assessed using either Pearson or Spearman rank, as appropriate. ANOVA tests were used for multiple comparisons to compare the normative mean score of the different disease types . Effect sizes were calculated as Cohen's d, modified using unpaired t-tests for unequal group sizes with Hedges' g equation and d converted from partial η2 in F tests . Continuous variables were expressed as means with corresponding standard deviations, and categorical variables were expressed as numbers and percentages. The proportions were compared using the χ2 test. If the data were normally distributed, two group comparisons were performed with unpaired or paired, two-tailed  F tests . For all setting .p-value of < 0.05 was considered statistically significant.Cox proportional hazards regression was used for determining risk factors for mortality at short-term, midterm, and long-term follow-ups adjusted by gender and age. The prognostic values of the parameters were tested using univariate Cox proportional hazards regression analysis at each follow-up, such as clinical data , baseline hemodynamic variables , decrease in ΔSF-36 in each domain, ΔPCS, and ΔMCS. Variables were all incorporated into the forward stepwise multivariable Cox proportional hazards model if confounders were considered in the univariate analyses or the variables have clinical importance. A HRQOL was examined in 559 consecutive adult inpatients (≥18 years) diagnosed with PH by RHC using SF-36 questionnaires. The baseline demographics and physical characteristics of the study patients are provided in At baseline, the mean time between diagnosis by RHC and administration of the SF-36 was 13 days. HRQOL in patients with PH significantly differed from U.S. normative average values in five of eight domains . In the p = 0.005, Baseline demographic characteristics such as age, gender, 6MWD, NT-proBNP, and hemodynamics  were not significantly associated with each subscale, PCS or MCS. The baseline PCS score markedly decreased with the severity of WHO FC . In partWe used the Z score to interpret the changing trend in health status. As shown in At each follow-up, we used univariate and multivariate Cox proportional hazards regression analyses to test risk factors for mortality adjusted by gender and age. In the short-term follow-up, increased NT-proBNP and baseline PVR, and decreased PF and RE are independent risk factors associated with mortality . AccordiPH is a progressive disease with high morbidity and mortality rates, despite advances in medical therapy. Patients with PH experienced debilitating symptoms, which had negative impacts on their HRQOL in terms of physical capability, psychological wellbeing, and social relations , 22. In p < 0.05) among the different diagnosis types of PH at baseline, which could be due to its poor PF  domain. There was no difference of PCS scores among PAH, CTEPH, and LHD-PH. The reasons for these differences are unclear but may be related to worst PF in the patients with CLD-PH. Compared with several other disease types, patients with CLD-PH might be older, have worse hemodynamics, and have relatively more underlying diseases or complications , which exhibited superior psychometric properties compared with the SF-36 in the assessment of PH patient-reported outcomes . In addiThe patients' point of view regarding their health status is essential in decision-making procedures. In conclusion, the present study documented that patients with PH tend to have impaired HQOL in both physical and mental components using the generic SF-36. Measuring how patients “feel, function, or survive” is a clinically meaningful endpoint. Notably, some subscale scores in the physical component improved; however, the changing trend of the mental condition continuously declined from the baseline to long-term follow-up. Therefore, identifying and intervening mental progresses is a major issue in PH management.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 Ethic Committee of Shanghai Pulmonary Hospital (Number: K16-293). The patients/participants provided their written informed consent to participate in this study.LW and RZ contributed to the study design, study conduct and supervision, scientific overview, data analysis, and editing of the manuscript. J-LL and FX contributed to patient enrollment, data analysis, scientific interpretation, and drafting and editing the original manuscript. H-TLiu, H-TLi, Q-HZ, C-YS, YZ, LY, W-YW, HL, S-GG, RJ, and J-ML contributed to recruitment of participants, data collection and curation, and formal analysis. FX contributed as a statistician. All authors participated in the design of the study and/or patient enrollment, and meet criteria for authorship, reviewed the manuscript, and approved the final version for submission.The study was supported in part by the National Natural Science Foundation of China (82000059) (to LW), the National Natural Science Foundation of Shanghai Scientific and Technological Committee (22ZR1452600) (to RZ), and the Shanghai Pujiang Program (2021PJD060) (to LW).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.


Cognitive impairment is a highly prevalent non-motor feature of Parkinson’s disease (PD). A better understanding of the underlying pathophysiology may help in identifying therapeutic targets to prevent or treat dementia. This study sought to identify metabolic alterations in the prefrontal cortex (PFC), a key region for cognitive functioning that has been implicated in cognitive dysfunction in PD.Proton Magnetic Resonance Spectroscopy was used to investigate metabolic changes in the PFC of a cohort of cognitively normal individuals without PD (CTL), as well as PD participants with either normal cognition (PD-NC), mild cognitive impairment (PD-MCI), or dementia (PDD). Ratios to Creatine (Cre) resonance were obtained for glutamate (Glu), glutamine and glutamate combined (Glx), N-acetylaspartate (NAA), myoinositol (mI), and total choline (Cho), and correlated with cognitive scores across multiple domains  administered to the PD participants only.When individuals retain cognitive capabilities, the presence of Parkinson’s disease does not create metabolic disturbances in the PFC. However, when cognitive symptoms are present, PFC Glu/Cre ratios decrease with significant differences between the PD-NC and PPD groups. In addition, Glu/Cre ratios and memory scores were marginally associated, but not after Bonferroni correction.These preliminary findings indicate that fluctuations in prefrontal glutamate may constitute a biomarker for the progression of cognitive impairments in PD. We caution for larger MRS investigations of carefully defined PD groups. PD withCognitive impairment in PD is associated with disturbances in frontostriatal circuits influenced by dopaminergic abnormalities . However1H-MRS) identifies focal metabolic and neurodegenerative changes  to determine if our cohort of cognitively normal PD patients differ from cognitively normal individuals without PD (CTL) to provide a baseline and confirm/refute previous published studies, and (ii) to determine if varying degrees of cognitive impairment causes metabolic changes in the PFC.Though Participants were recruited from the University of Colorado Hospital Movement Disorders clinics (for the PD groups) as well as fliers posted on campus and signed informed consent to participate in the study approved by the Colorado Multiple Institution Review Board. Inclusion criteria included a diagnosis of probable PD according to the UK Brain Bank Criteria . ClassifPD-NC, PD-MCI, and PDD participants, but not CTL subjects, underwent extensive neuropsychological assessments: Mattis Dementia Rating Scale (DRS-2) ; Montreap = 0.007. Therefore, data were imputed to create five multiple imputations. The imputed data did not significantly differ from the original data . For data reduction purposes, cognitive composite scores were created using principal components analyses for each set of imputed data separately. Summary statistics for the creation of the composite scores are displayed in Cognitive data were checked for missingness and found to violate the assumption that data were missing completely at random (MCAR): Little’s MCAR Test, -Global cognition: MoCA and DRS-2 total scores-Attention: TMT-A and BTA-Language: BNT and verbal fluency-Learning & memory: CVLT trials 1–5 total score and CVLT long delay free recall-Executive function: TMT-B and SDMT oralThe Judgment of Line Orientation Test was the only individual test of visuospatial abilities, so a composite score was not created for it.MR data were obtained using a Siemens Skyra 3 Tesla wide bore whole-body MRI scanner, equipped with high performance gradient coils (maximum gradient amplitude of 45 mT/m and maximum slew rate of 200 T/m/s) and a 12-channel phased-array head/neck coil. The MRS voxels were placed over PFC  as descrvia calls from Gannet to SPM12 . Metabolite ratios were corrected for the voxel CSF content using standardized methods (T1-weighted images were used to determined voxel gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) fractions in the PFC. CSF, GM and WM of the voxels were segmented  methods , since Cp < 0.05 for significance using SPSS . Demographic data were compared between groups by one-way analysis of variance (ANOVA). Chi-squared test was used for categorical data . CSF-corrected metabolite data were compared between groups by t-tests for the PD-NC vs. CTL comparison and one-way analysis of variance (ANOVA) for the PD-NC vs. PD-MCI vs. PDD comparisons. Bonferroni correction for multiple comparisons was used to investigate significant differences. Neuropsychological performance of the PD-NC, PD-MCI, and PDD groups were compared using one-way ANOVA. Bonferroni post hoc tests were performed to assess differences between each group. Stepwise linear regression analysis was computed to assess correlations between 1H-MRS metabolites and pooled composite scores from the five imputations, as well as scores for the JLO test with age of subjects as a covariate. Two models were run. Corrected Glu/Cre was used as a dependent variable, and age as well as composite score were used as explanatory variables. In addition, a False Discovery Rate correction  = 8.315, p = 0.040. Age did not differ significantly between CTL, PD-NC, and PD-MCI participants, but PDD participants were significantly older than the other groups , which is inherently associated with dementia occurrence in PD patients. Therefore, age was used as a covariate for subsequent analyses. Sex was not used as a covariate given that there was no female in one of the PD groups (PDD). Motor severity was also not used as a covariate to avoid multicollinearity issues.Data from nineteen CTL subjects, eleven PD-NC participants, twenty-four PD-MCI participants, and eleven PDD participants were used in the F = 3.444, p = 0.041] in the PFC to be significantly different between the PD groups (Post hoc comparisons showed glutamate to decrease between PD-NC and PD-MCI and also between PD-MCI and PDD groups, but significant differences were only observed between PD-NC and PDD groups (p = 0.044; CSF-corrected Glx/Cre ratio was increased in the PD-NC compared to the CTL group, however, Bonferroni correction for pairwise comparisons indicated this increase was not statistically significant . UnivariD groups . Post ho–16, respectively. When controlling for age, the two variables were marginally linearly related (R2 = 0.047; p = 0.062, p-values were above the critical values created by the FDR procedure (Averaged Glu/Cre ratios and pooled memory composite scores across PD groups were 0.93 and 1.49 10rocedure  this resrocedure .Our results suggest that before cognitive symptoms appear, older people with or without PD exhibit similar neurometabolic profiles in the PFC. But, when cognitive symptoms have appeared, prefrontal glutamate is decreasing along with cognitive worsening.Glutamate abnormalities at the subcortical level have been previously noted in animal models of PD  but not via1H fMRS is therefore a practical method to investigate in vivo dynamics of excitatory neural activity driven by task-related cognitive demand. In healthy adults, elevated dorso-lateral PFC glutamate during a working memory task has also been shown in vivo (We also observed that PFC Glu/Cre ratios were marginally associated with memory tasks indicating that worsening memory skills may be associated with decreasing glutamate contents in the PFC. This finding is not surprising given an extensive literature related to glutamate and memory, embracing human and animal models. An animal study at 14.1T showed task- or stimulation-induced increase in glutamate reflects enhanced excitatory neurotransmission as well as increased metabolic activity . Modulat in vivo . This suIn summary, this exploratory study supports the relationship between cognitive dysfunction in PD and metabolite alteration in PFC, suggesting that dysregulation of frontal glutamate may index cognitive changes in PD patients. Limitations of the current study include a relatively small sample size, cross-sectional nature of our dataset, and possibly the lack of activation of glutamatergic networks during rest.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 the Colorado Multiple Institution Review Board. Inclusion. The patients/participants provided their written informed consent to participate in this study.IB conceptualized and conducted the study, drafted, and reviewed the manuscript. NL-E and FC performed the analyses, drafted, and reviewed the manuscript. MB provided expertise with MRS data acquisition, analyses and interpretation, and reviewed the manuscript. LM helped conducting neuropsychological tests, performed the behavioral analyses, and reviewed the manuscript. EK helped with MRS data analyses and reviewed the manuscript. CM and SR conducted the study and reviewed the manuscript. SH helped with participants’ recruitment and neuropsychological testing, as well as reviewed the manuscript. MG helped with neuropsychological testing battery design and with the consensus on cognitive diagnosis, as well as reviewed the manuscript. BK conceptualized the study and reviewed the manuscript. All authors have 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.


Deep Convolutional Neural Networks (DCNNs) were originally inspired by principles of biological vision, have evolved into best current computational models of object recognition, and consequently indicate strong architectural and functional parallelism with the ventral visual pathway throughout comparisons with neuroimaging and neural time series data. As recent advances in deep learning seem to decrease this similarity, computational neuroscience is challenged to reverse-engineer the biological plausibility to obtain useful models. While previous studies have shown that biologically inspired architectures are able to amplify the human-likeness of the models, in this study, we investigate a purely data-driven approach. We use human eye tracking data to directly modify training examples and thereby guide the models’ visual attention during object recognition in natural images either toward or away from the focus of human fixations. We compare and validate different manipulation types  through GradCAM saliency maps against human participant eye tracking data. Our results demonstrate that the proposed guided focus manipulation works as intended in the negative direction and non-human-like models focus on significantly dissimilar image parts compared to humans. The observed effects were highly category-specific, enhanced by animacy and face presence, developed only after feedforward processing was completed, and indicated a strong influence on face detection. With this approach, however, no significantly increased human-likeness was found. Possible applications of overt visual attention in DCNNs and further implications for theories of face detection are discussed. Neocognitron was introduced by Deep Convolutional Neural Networks  already improved the model’s similarity to human fMRI activity significantly compared to a subordinate category structure  – the default in computer vision. This finding leads us to the assumption that a data-driven manipulation of training examples based on human eye movements should likewise increase the similarity between in vivo and in silico object recognition. Therefore, in this paper, we focus on increasing the human-likeness of visual attention in DCNNs through manipulated visual inputs, rather than previously reported modified architecture, to demonstrate that there are generally two approaches in the sense of nature  and nurture  to influence visual attention. To achieve this, we apply a fine-tuning approach to a widely used DCNN called AlexNet , we incorporate an Anti-Spotlight control condition  g theory , feedbacg theory . An illug theory , we havepossible methodological advantages and disadvantages of forcing a model to use specific features. As previous works reported performance decreases in noise-trained DCNNs when tested on natural, noise-free images (The third section of this paper will follow up on (iii) e images , here wee images .standard AlexNet = STD-net, Human-Spotlight AlexNet = HS-net, Anti-Spotlight AlexNet = AS-net) with otherwise identical training procedures. The only difference here were the differently blurred versions of the same fine-tuning examples. The obtained models were then tested on the other half of the images. Both eye tracking heatmaps and GradCAMs were also used from incorrectly classified images  to avoid comparisons contingent on subjective responses  were tested. Participants had normal or corrected-to-normal vision, no problems with color vision, and no other eye diseases. One participant had to be excluded from the analyses due to inaccurate eye tracking calibration. The experimental procedure was admitted by the University of Salzburg ethics committee, in line with the declaration of Helsinki, and agreed to by participants via written consent prior to the experiment. Psychology students were compensated for taking part in the study with accredited participation hours.In the eye tracking experiment, a total of 49 participants  with an age ranging from 18 to 62 years  and a Gaussian filter with a standard deviation of 20 px was applied. Lastly, heatmaps of individual participants were averaged and normalized per image.The experimental paradigm see  consiste–4, and a weight learn rate factor of 20 in mentioned layers. All models were trained with varying image versions but identical settings for a maximum of 30 epochs and a validation patience of five epochs. No data augmentation was applied throughout the process. Saliency maps were computed based on the models last convolutional layer using the GradCAM attribution algorithm  and ten times per ratio in the accuracy versus similarity trade-off analysis. Hence, we train and test the model throughout different runs and average all obtained values to improve signal-to-noise ratio and further reduce the influence of individual unmanipulated or manipulated images.To increase the statistical power of both main analyses and overcome the general weakness of commonly used fixed-value comparisons, we estimate the models’ accuracy and similarity by repeating this process with random initialization seeds e.g.,  as well W = 30, SD = 7) was applied to the image followed by another Gaussian filter as an edge taper . For the eye tracking experiment and DCNN testing, a dataset of 360 images in total, consisting of twelve entry-level categories, with six animate categories , six inanimate categories , and 30 images each was used. All images were randomly drawn and preprocessed in identical procedure. Images were cropped to the largest central square and resized to fit AlexNet’s image input layer (227-by-227 px). A manual visual inspection ensured that images did not include multiple categories of interest, overlaid text, and image effects or did not show the object anymore due to cropping. All violations were removed. Furthermore, to identify face presence as well as object and face regions of interest (ROI), two independent raters manually detected and segmented the images of the testing dataset with in-house built MATLAB scripts. The obtained object ROIs (N = 360) and face ROIs (N = 133) were averaged, normalized, and a Gaussian filter was applied  = 147.78, p < 0.001, η2 = 0.77; see M = 89.80, SD = 3.18) and standardly fine-tuned STD-nets . Nevertheless, this significant but marginal accuracy difference demonstrates that DCNNs should principally be able to reach human benchmark performance with the fine-tuning and experimental procedures at hand. As expected, both HS-nets  and AS-nets  were significantly outperformed by human participants  and STD-nets  due to noise added by both guided focus manipulation types. However, contrary to our expectations, AS-nets did not significantly exceed HS-nets even though in the AS manipulation images were blurred to a lesser extent  compared to the HS manipulation . Instead, the opposite was observed, as HS-nets even significantly outperformed AS-nets . Moreover, further exploratory analyses of accuracies across categories and common misclassifications indicated unique behavioral patterns , revealed significant differences between the groups . Generally, in terms of our guided focus manipulation, we expected HS-nets to demonstrate more human-like visual attention compared to their inversely conceptualized AS-net counterparts and commonly fine-tuned STD-nets. Most interestingly, Bonferroni-corrected pairwise Wilcoxon tests indeed revealed that AS-nets (Mdn = 0.10) focused on significantly less human-like image parts compared to STD-nets  and HS-nets . However, no overall significant differences between HS-nets and STD-nets were found . Remarkably, as hypothesized, a significant interaction effect between manipulation type and animacy was identified  and as the overall patterns seemed to remain stable across animacy groups, an additional main effect of animacy may be assumed . Subsequent Bonferroni-corrected pairwise Wilcoxon tests revealed significant differences for both animate and inanimate objects between AS-nets compared to STD-nets  and HS-nets . Generally speaking, most human-like visual attention was obtained with STD-nets and HS-nets categorizing animate objects, while AS-nets categorizing inanimate objects elicited the least human-like attentional processes. Finally, to further pinpoint the central effects of our manipulation, the influence of face presence  on the similarity of visual attention was analyzed. The analysis revealed a significant interaction effect between manipulation type and face presence  with similar patterns to the reported animacy splits. Here again, in both face and non-face images, AS-nets focused on significantly more dissimilar image parts compared to STD-nets  and HS-nets . Taken together, these results imply that the distinct biases for living entities and especially their faces in human gaze patterns may have introduced the opposite effects in AS-nets. As an interim summary, it should be noted that the guided focus approach did not seem to increase the overall fit to human eye tracking data as intended in GradCAMs of HS-nets. However, the single most striking observation to emerge from the data was that HS-nets and AS-nets did show only a marginal accuracy difference and yet the AS manipulation lead to significantly less human-like visual attention. Besides that, it should be noted that individual categories were investigated through exploratory analyses only and no statistical procedures were applied due to the reported accuracy differences.As hypothesized, a Kruskal–Wallis test indicated significant differences between the three models  and ensuing Bonferroni-corrected pairwise Wilcoxon tests further revealed significant differences between AS-nets compared to STD-nets  and HS-nets . These results highlight the importance of faces in human vision, as our AS manipulation seemed to robustly blur them from the fine-tuning dataset and AS-nets thereby shifted their visual attention away from them to classify images without losses in accuracy compared to HS-nets.Following up on the observed effects of face presence, especially in AS-nets, we compared the models’ GradCAMs to face ROIs, which were visually segmented by two independent raters. Therefore, a face detection index  was computed. While data from human participants displayed a strong face bias (Mdn = 2.74), which varied considerably across time, DCNNs generally demonstrated clearly less focused attention towards face regions  and then gradually diminish due to the onset of increasingly feedback processes . The data resembled our proposed time course only partly, as indeed the similarity increased reliably at ∼150–200 ms but seemed to reach a plateau without a noticeable decrease in later time windows see . Splits Finally, to investigate possible methodological advantages and disadvantages of the applied approach, we performed accuracy and similarity analyses for ratios of HS and AS manipulations . We hypothesized that accuracy and similarity change as a function of magnitude in respect to the directionality of the manipulation as smaller manipulation ratios were expected to return both measurements to the STD-net baseline. Hence, while in HS-nets, smaller manipulation ratios  were expected to lead to increases in accuracy but decreases in human-likeness, in AS-nets both accuracy and similarity were expected to increase. The results endorsed this assumption partially see , as AS-nIn this study, we address the question whether it is possible to influence the human-likeness of DCNNs through a purely data-driven approach. In two opposing guided focus manipulations , we used eye tracking data to selectively modify the informational content in the training examples and thereby attempted to shift the models’ visual attention either toward or away from human-relevant features. Subsequently, we compared and validated the resulting models against new eye movement data.Generally, analyses of object recognition accuracy implied that human benchmark performance, often reported for the applied architecture , was neahouse, bed, and flower), as their image examples may display predominantly non-critical, distributed features  and not critical, focal features  as other categories , which would attract fixations more reliably. Naturally, it is difficult to investigate the impact of feedforward and recurrent processing on eye movements as they may be a mere behavioral proxy of the underlying neural processes (Firstly, the overall similarity for individual images indicated category-specific effects. This is in accordance with our previous findings on comparisons of human and DCNN visual attention  and may rocesses . On the rocesses , both inrocesses , this shSecondly, we were able to show that animate objects and face presence both significantly increase the effect of our manipulation. These findings are in line with previous results on visual processing of living beings, as they are known to elicit more stereotypical gaze patterns with more frequent and longer fixations compared to inanimate objects . FollowiWe are aware of the possibility that the guided focus manipulation induces merely cosmetic effects. This would explain the absent increased human-likeness in the HS manipulation, as DCNNs may not be able to process the features emphasized by human eye movements adequately. Therefore, future investigations on the interaction effect of biologically inspired architectures and human-relevant data will be needed. However, we believe that the human-likeness of DCNNs may not yet be directly linked to their performance, as manifest, accuracy-relevant, and latent, similarity-relevant information does not necessarily have to be identical. This further strengthens our confidence that the manipulation should principally work in both directions but is challenging to implement and verify. Thus, due to numerous limitations, the effect may have been solely found in the less challenging direction. While saliency maps were criticized to be highly noisy , our aveThe 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 the University of Salzburg Ethics Committee. The patients/participants provided their written informed consent to participate in this study.LD, SD, and WG contributed to the conception and design of the study. LD and SD programmed the experiment and collected the eye tracking data. LD wrote the first draft of the manuscript and implemented the computational models. LD and WG analyzed and interpreted the data. All authors contributed to manuscript revision, read, and approved the submitted version.


E-Mental Health in older age Ulrich Hegerl, Caroline Oehler Department of Psychiatry, Psychosomatics, and Psychotherapy, Goethe Universität Frankfurt/M, Germany European Alliance against Depression e.V. (www.EAAD.net) The implementation and uptake of digital tools for self management or psychotherapy for people suffering from depression or other mental disorders has gained momentum during the Covid-19 pandemia. While studies using waiting list or treatment as usual control groups are of limited value, meta-analyses of RCTs with face-to-face psychotherapy as control condition have found a comparable antidepressant effect, especially when the interventions were provided together with professional guidance. The iFightDepression-tool offered by the European Alliance against Depression (EAAD) is available in 10 different languages and is broadly used in several European countries. Data will be presented concerning the attitude of older people concerning iCBT and also concerning effects of age, guidance, and gender on both adherence to the iFightDepression-tool and antidepressant effects.No significant relationships.


Mayo Clinic, Rochester, United States), and an end-to-end cervical cancer screening platform for resource poor settings . We provide practical recommendations for implementers, and discuss future challenges and novel approaches ). Growth in global clinical AI research continues unabated, and introduction of vertically integrated teams and development practices can increase the translational potential of future clinical AI projects.Substantial interest and investment in clinical artificial intelligence (AI) research has not resulted in widespread translation to deployed AI solutions. Current attention has focused on bias and explainability in AI algorithm development, external validity and model generalisability, and lack of equity and representation in existing data. While of great importance, these considerations also reflect a model-centric approach seen in published clinical AI research, which focuses on optimising architecture and performance of an AI model on best available datasets. However, even robustly built models using state-of-the-art algorithms may fail once tested in realistic environments due to unpredictability of real-world conditions, out-of-dataset scenarios, characteristics of deployment infrastructure, and lack of added value to clinical workflows relative to cost and potential clinical risks. In this perspective, we define a vertically integrated approach to AI development that incorporates early, cross-disciplinary, consideration of impact evaluation, data lifecycles, and AI production, and explore its implementation in two contrasting AI development pipelines: a scalable “AI factory” ( In contrast, widespread translation of AI research into implementation remains conspicuously absent, particularly when considering AI for clinical decision-making, diagnosis, or prediction8. For example, while high-profile studies demonstrate superiority of AI-assisted cancer detection compared to clinicians9, there has been failure to replicate accuracy in larger studies, with limited prospective, real-world validation and poor potential for clinical utility10.Multiple indicators over the past five years demonstrate accelerating interest in the application of artificial intelligence (AI) to human health, including exponentially increasing published research since 201613, where focus is on improving algorithm performance for given internal and external datasets, referred to as a ‘model-centric’ approach14. Research waste, in the form of algorithms that will never see clinical utilisation, continues to increase17. Unrepresentative data and model bias contribute to these failings19, and the push for equitable data accumulation and incremental architectural gains are important for progressing AI as a whole20. However, less consideration is given to real-world factors that maintain importance throughout any AI development pathway, including the real-time data lifecycles that support predictions, heterogeneous software and hardware infrastructure that host AI models, and quantification of impact on patients and clinical workflows. Significance of these factors can be seen in failures of previous real-world evaluations of state-of-the-art algorithms, that have been unable to achieve anticipated performance due to infrastructural and data problems21, or lack of added value within everyday workflows23.Most clinical AI research is conducted on existing, retrospective datasets25. Clearly, intricacies and risks inherent to patient data are not comparable to non-healthcare sectors, but lessons can be taken from differing approaches to AI, focussing on value generation, cross-disciplinary collaboration, and a holistic approach to practicalities external to algorithm design26.In contrast, the use of AI in non-healthcare enterprises has achieved greater success, demonstrating clear return-on-investmentMayo Clinic, Rochester, United States), and a focused, embedded AI system for cervical cancer screening in a low-resource setting . We discuss challenges and provide recommendations that can help future AI projects cross the gap from pages of medical journals to patient bedsides.In this article, we identify practical features of AI development, that have crucial importance for translation, and define their vertical integration within an AI ‘supply chain’. We demonstrate how vertically integrated approaches can work in practice, through the lens of two successful, contrasting, real-world pipelines: a major, scalable, AI platform in a high-resource setting  is sparser, while evidence of tangible impact on patients or cost-effectiveness is severely lacking. Implementation should aim to generate value for end-users and patients. This could derive from AI-assisted insights with no real-world equivalent (such as associations in complex data), or from augmenting existing skilled clinicians. The latter is particularly relevant for low-income, low-resource environments, or high-resource areas where workstreams are bottlenecked by particular tasks. Impact evaluation therefore includes establishing and estimating clear end-user or patient-centred outcome targets before building a model, as well as planning and monitoring for unintended, post-deployment effects such as over-investigation and over-treatment, or costs from safety-netting high-risk decisions37. Practical examples can be seen in Learning Healthcare Systems, where analysis is built into daily practice38. A lifecycle view emphasises data flows, where data is constantly produced during routine care, and where use and utility of data is often time-constrained. In contrast, model-building is traditionally performed on static datasets that have passed through often unreproducible, proprietary, processing steps. More data, and external data, is not necessarily useful, as additional features may only be available in research settings or through manual collection. While the importance of representative training data is well recognised, other factors can impede successful deployment. These include: (1) differences in how data is acquired and processed, between curated datasets and live implementation environments39 ; (2) software and hardware requirements to stream data to a model, which may vary from simple DICOM ingestion to integrating multi-modal data from multiple devices; and (3) a more comprehensive scope of raw data and signals in a live environment, that are not considered by a model, but provide additional insights to a diagnostician (thus reducing relative AI performance).A healthcare data lifecycle describes generation, curation and aggregation, and maintenance of patient data that is used by consumers (such as clinicians and researchers) and patients themselves6). Similarly, a model can integrate into physical devices with their own computational power and interface . Embedding models takes advantage of discrete workflows and simpler data lifecycles but are ‘locked-in’ to specific uses. A model can instead be served as a module within a larger system, communicating with other modules via interoperable data formats. This approach is scalable, and maintains control over compute, heterogeneous data input/output, flexibility in technology, and resilience in upgrading. However, this comes with high set-up costs and complex development, typically requiring whole organisational buy-in. For models requiring rich data from multimodal sources, this may be the only viable route. Other production considerations include ability to monitor for software bugs and hardware failures, and observe changes in real-world circumstances and data distributions that may cause model performance to deteriorate (‘model drift’) with potential for harm. Models brought to production can encounter difficulties in data interoperability and hardware compatibility, particularly in complex clinical software environments.Production describes the process of bringing an AI model to active deployment. Technical requirements in this stage , usually call on the expertise of deployment engineers and software developers. As a result, once a promising algorithm has been evaluated by researchers, it will require additional expertise to insert it into the midst of competing software and hardware infrastructure. Successful production enables data ingestion by a model, provides computational power, presents an interface to observe the model working, and returns insights to users. In practice, no two production environments look the same. For example, a model can be embedded into a software application with its own codebase, internal data lifecycle, and user interface. Many imaging algorithms deploy into commonly used radiology workflow software  utilises multiple Fast Healthcare Interoperability Resource (FHIR) application programming interfaces (APIs) to receive EHR and device/wearables data, with additional APIs integrating imaging and signal (e.g. electrocardiogram) data. Data is hosted in the cloud , where reproducible harmonization and quality assurance enables data consistency. Model-builders can access data through the “Discover” component, which provides a development environment with compute infrastructure and software tools. Trained models are passed to “Validate”, which facilitates silent evaluation on prospective data streams, and automates assessment of model bias by evaluating across population subgroups, and in benchmark datasets for sociodemographic characteristics. “Validate” reports performance across a range of scenarios, including calibration and potential for bias in marginalized populations.One strategy for vertical integration is to build an entire organisation-wide infrastructure around AI development. The Mayo Clinic AI factory hosts cross-disciplinary expertise and a development platform, that minimises distance from concept to implementationDeliver” component. Model outputs are translated into insights via pre-defined rules, sent to end-users using the existing messaging APIs interfacing with EHR and devices. End-users may be presented with flags, personalized care plans, or access to relevant guidance. Finally, outcome indicators belong to the same data lifecycle and are used to estimate potential impact, or measure intended and unintended post-deployment impacts. The entire platform is supported by a cross-disciplinary team who work with end-users to identify areas of maximal impact, and to vet feasibility with respect to data and production requirements.Integration of software and computational hardware onto a single platform simplifies production, as implementation can be enabled by switching on a “nference, Cambridge, USA)49. A “data behind glass” approach places authorized sub-tenants in encrypted containers (under Mayo control) but does not allow data to leave containers, preventing merging data with external sources for re-identification.A major challenge in implementing a ‘whole systems’ platform is patient privacy. In addition to removing known identifying elements from multimodal data, the platform employs best-on-class de-identification protocols for EHR data 51, and is now undergoing pilot implementation and validation under Food and Drug Administration breakthrough designation. Development is summarised in Table Launch of the platform in 2020 has resulted in a rich development pipeline52, prediction of post-surgical mortality53, and real-time monitoring of COVID-19 interventions54. More than 200 additional models are in different stages of development maturity55, while the platform additionally hosts and accelerates start-ups to market readiness56. Vertical integration of data, modelling and validation, production, and clinical impact evaluation into a single platform bridges the gap between algorithms and implementation.Other use-cases evaluated on the platform include portable ECG assessmentIn an opposing, resource-poor scenario, a vertically integrated approach means prioritising infrastructure, and planning for challenging environments where there may not be easy paths to translating model outputs into actions. In such settings, a focus on model-building may produce an algorithm with excellent performance across multiple datasets, but will not address implementation barriers Fig. .Fig. 4Pi57. Existing algorithms are trained on datasets for high-resource economies with different demographic and data quality characteristics (including cleaner slide preparation) and are designed to integrate with Western cytopathological workflows and expensive devices58. Despite a clear use-case for AI, development must contend with a lack of infrastructure. The locally developed approach taken by William et al.59 has involved parallel development of hardware and software to support local data lifecycles, with portable training, validation, and production environments, and a health record for outcomes data  contributing to late diagnosisIn summary, cytopathological images are acquired through 3D-printed components integrating a microscope slide scanner, networked to an image storage system with labelling/training environment. With parallels to the Mayo Clinic platform , the same locally applicable data flows are used for model validation. Models are calibrated to local images, include artefact handling, with ability to run on low-end hardware. The production environment sits alongside training software on mobile, low-power devices, and integrates the same data flows. Outputs are added to an electronic record that can be viewed by patients or clinicians and linked to treatment and outcomes. Finally, acquired images can be manually assessed to re-validate the model and enrich the dataset.1, where there is also significant lack of diagnostic resource. Vertical integration promotes local infrastructure – a pre-requisite for representative data and implementation environments. With care, LLMIC AI development can achieve substantially over par return-on-investment, when compared to sums invested into AI for well-functioning clinical pathways in high-resource areas.There is a paucity of AI research in low to low-middle income countries (LLMIC)Artificial intelligence has captured the imagination of clinicians and researchers, funders, and commercial investors; but without widespread translation to clinical impact, we risk disillusionment and a collapse in willingness to invest resources.Vertical integration describes a holistic approach to AI, which engages with all supply components of a planned product at conception, employs teams with cross-disciplinary expertise, and adopts a strategic recognition that model-building in isolation, while often a substantial academic achievement, is not always a practical one.1 and devices8, may reflect lower implementation requirements from standardized data (DICOM) and pre-existing assisted reporting environments. In addition, the components we describe are not in themselves novel. For companies producing AI software-as-medical-devices, a focus on elements such as software-embedding and value demonstration is necessitated by commercial and regulatory drivers. However, a common feature of the clinical AI research translational gap remains separation of dataset experimentation with ability to operationalise models, as seen in lack of candidates for clinical translation amongst hundreds of COVID-19 models with high reported accuracy61.Practically, approaches will vary across settings. The distance between model-building and other components is a spectrum that differs across data types and clinical environments. The described cases represent two extremes: a large-scale transformation across an organisation, and a planned approach to maximise potential for operationalization in a resource-poor setting. In many other cases, substantial infrastructural changes are unnecessary, as deployment requirements are lower. For example, the dominance of radiomics in development maturity62.Addressing additional, specific challenges can also help transform current approaches. First, priority for AI funding should be given to proposals with integrated roadmaps to implementation. Statistical methodology could be supplemented by understanding of informatics infrastructure, involvement of deployment experts, and assessment or estimation of longer-term impact. This can be seen in practice, where NHS Transformation in the UK funds projects that fulfil urgent care priorities, and demonstrate feasibility of workflow deployment63. A diametrically opposite approach is decentralisation using federated architectures to manage data environments across multiple organisations, each hosting local cross-disciplinary teams, as being developed by the London Medical Imaging & AI Centre for Value Based Healthcare (AI4BH)65. While centralisation provides economies of scale for technical specialisation, decentralisation aims to harness synergies through proximity to domain experts and data sources for local requirements.Second, transition to an entirely vertically integrated platform like Mayo Clinic requires whole organisation buy-in. While this can produce ground-breaking results, the required organisational transformation and investment may be unfeasible. Centralisation may be an alternative in regional healthcare networks, accumulating cross-disciplinary expertise from multiple centres, while creating population-level data flows, often through the use of the commercial platform providers67. Vertical integration actively supports meeting these regulatory requirements but may also benefit from guidance to address challenging issues such as model and dataset drift, and on-going quality and risk management systems. This dynamic post-translational management stage has been termed ‘MLOps’ by non-healthcare industries68.Finally, medical device regulation and safety monitoring requires reconsideration. Proposed regulation in the USA and UK for a ‘product lifecycle approach’ will consider data flows and production practices Even externally validated and accurate AI models cannot compensate for practical problems that preclude deployment into real-world workflows. Clinical AI development must vertically integrate cross-disciplinary teams and supply chain components that directly support model implementation. This broad approach is adaptable to different settings and can help improve translation of clinical AI research into clinical workflows.Further information on research design is available in the Reporting Summary


BMI, waist circumference, ALT values, HOMA-IR values, free androgen index levels, hyperandrogenism, and triglycerides were associated with significantly higher risk-adjusted odds of NAFLD among patients with PCOS. Meta-regression showed that rises in NAFLD prevalence were mediated through increases in metabolic syndrome prevalence and higher levels of HOMA-IR, free androgen index, and total testosterone. Conclusion: The prevalence of NAFLD (43%) among PCOS patients is high despite their average young age, with several metabolic and PCOS-specific factors influencing its occurrence. Screening programs may aid in detecting metabolic-associated fatty liver disease and prevent its consequences. Further work is required to establish the burden of liver-related outcomes once NAFLD has progressed in the PCOS population.Background: The metabolic effects of polycystic ovary syndrome (PCOS) may increase the risk of non-alcoholic fatty liver disease (NAFLD). However, the burden of NAFLD in PCOS has not been unequivocally defined. This systematic review (SR), meta-analysis (MA) assessed NAFLD’s prevalence, and risk factors in patients with PCOS. Methods: A literature search was performed in MEDLINE, Scopus, and Scielo. First, we performed a MA of proportions to estimate the prevalence of NAFLD in PCOS. Second, we performed meta-analyses of precalculated adjusted odds ratios to examine NAFLD risk factors. Finally, we performed a meta-regression to model how the estimated prevalence changed with changes in prespecified variables. Results: We identified 817 articles from the database searches. Thirty-six were included. MA of proportions found a pooled NAFLD prevalence of 43%  with high heterogeneity (I The increasing burden of non-alcoholic fatty liver disease (NAFLD), which now is estimated to affect approximately 25% of the global population , has becPolycystic ovary syndrome (PCOS) is characterized by hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphology . It is cThis study aimed to assess NAFLD’s prevalence and risk factors in patients with PCOS.The present SR and MA was performed following the recommendations from the Cochrane Handbook for Systematic reviews of interventions, and the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement ,8. HowevTo accomplish this paper’s objectives, this SR and MA answered the following question:What is the prevalence and factors associated with NAFLD in patients with PCOS who have no concurrent viral hepatitis co-infection or significant alcohol use?Papers were considered eligible for inclusion in this SR if they assessed the presence of NAFLD in patients with PCOS.We included observational studies, including cohort, case–control studies, and case series. We also considered randomized controlled trials. These studies were included if the proportion of patients with NAFLD among PCOS patients was assessed. In addition, articles that assessed the factors associated with NAFLD were also considered eligible. Case reports were not considered eligible for inclusion.The participants were women with PCOS diagnosed by standard criteria . ArticleStudies were excluded if they included patients with either clinically relevant concomitant alcohol consumption or a history of viral hepatitis co-infection.The prevalence of NAFLD in PCOS patients, and the factors associated (risk factors) with such prevalence were the outcomes of interest in this systematic review.NAFLD diagnosis was based on the detection of hepatic steatosis, defined as the presence of significant steatosis demonstrated either by biopsy or a non-invasive test. For this SR, biopsy, non-invasive imaging tests, and blood biomarkers/panels were considered appropriate diagnostic methods, and thereby eligible for inclusion .Non-invasive imaging tests included right upper quadrant ultrasound, computer tomographic (CT) scan, magnetic resonance imaging (MRI) techniques, or vibration-controlled transient elastography (VCTE)-based attenuation parameter (CAP) measurements. Non-invasive blood biomarkers and panels are described elsewhere . If studRegarding the risk factors for NAFLD in PCOS patients, we were interested in adjusted measures of association between PCOS and NAFLD. Therefore, reported measures relating to patient characteristics, relevant clinical data, and comorbidities were extracted from the included studies. The extracted risk factors had to result from a multivariable regression analysis performed in a PCOS population with NAFLD as the outcome variable and reporting adjusted odds ratios with 95% confidence intervals for the covariates of interest. We did not pre-specify risk factors of interest; instead, we extracted those available in the included studies. We collected the risk factors  that were available in the included articles. If two studies or more reported the same factor, we considered it appropriate for meta-analysis.Following experts’ recommendations ,13, we oWe did not attempt to explore the grey literature in this systematic review.The initial phase of the study selection process was performed blindly and independently based solely on titles and abstracts. With this purpose, the results from the search strategies were imported into Rayyan . Then, tIn cases of overlapping populations , the publication with the larger sample size or greater data granularity was selected for inclusion in the SR.We collected the data of interest from each study into a pre-designed data collection form. The data was collected as reported in each study and included: authors, year of publication, study design, region of origin, number of patients (in comparative studies: number of PCOS and control patients), comorbidity information, and relevant lab values. We extracted and charted data only from PCOS patients. In the case of comparatives studies, the control group’s data were not considered nor extracted.We also extracted each study’s objectives and inclusion/exclusion criteria. This information was extracted and collected in the same form as reported in each study and is available in We used the Methodological Index for Non-Randomized Studies (MINORS)  tool to Information from each study was summarized descriptively. The prevalence (proportion) of NAFLD in PCOS was obtained by dividing the number of NAFLD cases registered in PCOS groups by the total number of patients with PCOS. We used the “metaprop_one” command  in Stata2 statistic and the overall pooled estimate .The “metaprop_one” command is appropriate to pool proportions in a meta-analysis of proportions to estimate the prevalence of an event of interest, such as in this SR. In brief, confidence intervals for the individual studies were calculated using exact confidence limits for a binomial proportion. Pooled event rates  were estimated through a meta-analysis of binomial data with the Freeman-Tukey double arcsine transformation of proportions . The resRisk factors of NAFLD among patients with PCOS were meta-analyzed when possible. When at least two studies reported the same factor with its corresponding odds ratio from a multivariable regression analysis, we combined such effect estimates (adjusted odds ratios) to produce a pooled OR in a meta-analysis. To meta-analyze these precalculated effect estimates, we used the log-odds ratios with their 95% confidence intervals as inputs to the analysis; however, the results of these meta-analyses are presented on the ratio scale (adjusted ORs with 95% CI).2 test, corresponding to low (I2 < 25%), medium (I2 = 25–75%), and high (I2 > 75%) heterogeneity.In both meta-analyses , heterogeneity was evaluated using the IWe did not assess for publication bias because traditional methods (the funnel plot) have been proven inaccurate and unreliable for meta-analysis of proportions, such as in this case .To explore the heterogeneity of the pooled prevalence obtained from the proportion meta-analysis, we performed subgroup analyses stratified by study design, geographical location, and NAFLD and PCOS diagnostic methods/criteria.Using the effect estimates  and its standard errors obtained from the proportion meta-analysis, we performed a meta-regression analysis to explore if selected study-level summary data could influence the estimated prevalence.We extracted the prevalence of metabolic syndrome and the mean values of HOMA-IR, free androgen index, and total testosterone (nmol/L) from the studies where they were available. We assumed that any effect on the estimated prevalence was mediated through changes in the variables of interest. Therefore, we performed a random-effects meta-regression to estimate the coefficients, β, which indicate how the estimated prevalence changed with a unit increase in the proposed explanatory variables. A REML algorithm estimated the between-study variance in this model. The results of the meta-regression were presented in “Bubble plots” with fitted meta-regression lines, with circles representing the estimates from each study, sized according to each estimate’s precision.All statistical analyses were performed in Stata statistical software v.14.We identified 817 articles from the electronic database searches, of which 48 were considered eligible for inclusion in the present SR. After applying all inclusion and exclusion criteria, 36 were included ,52,53,54As shown in As mentioned earlier, we extracted and charted data only from PCOS patients. The studies included in this SR recruited 7374 individuals, of which 5021 had a diagnosis of PCOS and 2156 were controls without PCOS. The diagnosis of PCOS was similar across studies. In 31 (86%), the Rotterdam criteria were used to diagnose PCOS. The NIH criteria were used in 4 (11%) studies. One study reported using the Androgen Excess and PCOS Society criteria to diagnose the condition.n = 15)  .As shown in n = 5021), 2072 (41.2%) were diagnosed with NAFLD.Overall, of the PCOS patients included for analysis   with high heterogeneity (I= 97.2%) .Sub-group analysesWe performed subgroup analyses to explore the pooled NAFLD prevalence by a number of categorical study characteristics. Meta-regression analysisp = 0.008), total testosterone , and HOMA-IR  influenced increases in the prevalence of NAFLD among PCOS patients. Similarly, a higher prevalence of metabolic syndrome was associated with a higher prevalence of NAFLD   .Meta-analysis of risk factors for NAFLD in patients with PCOSOf the 36 studies included in the SR, 12 reported effect estimates resulting from multivariable regression analyses and appropriate to combine in a meta-analysis. The factors pooled in the meta-analyses of risk factors were BMI, waist circumference, ALT values, HOMA-IR values, HDL levels, free androgen index levels, hyperandrogenism, and triglycerides. As shown in n = 5021 PCOS patients), two key points can be extracted: (1) the burden of NAFLD in PCOS patients is concerning, with an estimated pooled prevalence of 43% ; (2) obesity (BMI and waist circumference), metabolic abnormalities , and PCOS specific hallmarks (hyperandrogenism and free androgen index) were identified as risk factors for NAFLD in PCOS populations. Meta-regression analysis showed metabolic features and PCOS-specific characteristics as potential effect modifiers, with rises in NAFLD prevalence mediated through increases in metabolic syndrome prevalence and higher levels of HOMA-IR, free androgen index, and total testosterone  among PCOS patients with several metabolic and PCOS-specific factors influencing its occurrence. As the burden of NAFLD appears to be high, screening programs may aid in detecting metabolic-associated fatty liver disease, and prevent its consequences in a population where this condition has been commonly overlooked.


This study develops an arithmetic optimization algorithm with deep-learning-based histopathological breast cancer classification (AOADL-HBCC) technique for healthcare decision making. The AOADL-HBCC technique employs noise removal based on median filtering (MF) and a contrast enhancement process. In addition, the presented AOADL-HBCC technique applies an AOA with a SqueezeNet model to derive feature vectors. Finally, a deep belief network (DBN) classifier with an Adamax hyperparameter optimizer is applied for the breast cancer classification process.Histopathological images are commonly used imaging modalities for breast cancer. As manual analysis of histopathological images is difficult, automated tools utilizing artificial intelligence (AI) and deep learning (DL) methods should be modelled. The recent advancements in DL approaches will be helpful in establishing maximal image classification performance in numerous application zones. This study develops an arithmetic optimization algorithm with deep-learning-based histopathological breast cancer classification (AOADL-HBCC) technique for healthcare decision making. The AOADL-HBCC technique employs noise removal based on median filtering (MF) and a contrast enhancement process. In addition, the presented AOADL-HBCC technique applies an AOA with a SqueezeNet model to derive feature vectors. Finally, a deep belief network (DBN) classifier with an Adamax hyperparameter optimizer is applied for the breast cancer classification process. In order to exhibit the enhanced breast cancer classification results of the AOADL-HBCC methodology, this comparative study states that the AOADL-HBCC technique displays better performance than other recent methodologies, with a maximum accuracy of 96.77%. Cancer is one of the most serious health concerns that threaten the health and lives of individuals . The morToday, machine learning (ML) is fruitfully enforced in text classification, image recognition, and object recognition. With the progression of computer-aided diagnosis (CAD) technology, ML is effectively implemented in breast cancer diagnosis . HistopaThis study develops an arithmetic optimization algorithm with deep-learning-based histopathological breast cancer classification (AOADL-HBCC) technique for healthcare decision making. The presented AOADL-HBCC technique mainly aims to recognize the presence of breast cancer in HIs. At the primary level, the AOADL-HBCC technique employs noise removal based on median filtering (MF) and a contrast enhancement process. In addition, the presented AOADL-HBCC technique applies an AOA with a SqueezeNet model to derive feature vectors. Finally, a deep belief network (DBN) classifier with an Adamax hyperparameter optimizer is applied for the breast cancer classification process. In order to exhibit the enhanced breast cancer classification results of the AOADL-HBCC approach, a wide range of simulations was performed. Shankar et al.  establisKhan et al.  examinedSingh et al.  examinedIn this work, an automated breast cancer classification method, named the AOADL-HBCC technique, was developed using HIs. The presented AOADL-HBCC technique mainly aims to recognize the presence of breast cancer in HIs. It encompasses a series of processes, namely SqueezeNet feature extraction, AOA hyperparameter tuning, DBN classification, and an Adamax optimizer. In this study, the presented AOADL-HBCC technique utilized an AOA with a SqueezeNet model to derive feature vectors. Presently, GoogLeNet, ResNet, VGG, AlexNet, etc., are signature techniques of DNN . HoweverCompared with AlexNet and VGGNet, the SqueezeNet architecture has a smaller number of parameters. The fire module was the primary approach from SqueezeNet. This approach was classified into expand and squeeze structures. The squeeze encompasses To adjust the hyperparameters of the SqueezeNet method, an AOA was implemented in this work. The AOA starts with a number of arbitrary populations of objects as candidates (immersed objects) . Here, tD), acceleration (A), and volume (V), to thi object numbers:In this expression, Next, the cost value of the candidate is evaluated and stored as TF) to reach exploration–exploitation:In this case, d) density is used to offer a global–local search:In Equation (7), In Equation (8), Here, thi object to the succeeding round is accomplished as follows:Here, In Equation (12), In this expression, Finally, the value of each object was assessed through a cost function and returned the optimal solution once the end state was satisfied.The AOA method extracts a fitness function (FF) to receive enhanced classifier outcomes. It sets a positive value that signifies the superior outcome of the candidate’s solutions. In this work, the minimized classifier error rate is indicated as the FF, as provided in Equation (16).Finally, an Adamax optimizer with the DBN method was applied for the breast cancer classification process (Algorithm 1). A DBN is a stack of RBM, excluding the primary RBM that has an undirected connection . SignifiThen, iteratively learning the weight of the network, the up-down approach was used to finetune the network weight. The wake-sleep approach is an unsupervised algorithm applied to train NNs from two phases: the “wake” phase was implemented on the feedforward path to compute weight and the “sleep” phase was executed on the feedback path. The up-down approach was executed to network for decreasing underfit that could usually be detected by a greedily trained network. Particularly in the primary phase, the weight on the directed connection was from named parameters or generative weight that can be adjusted by updating the weight utilizing CD, calculating the wake-phase probability, and sampling the states. Then, the prior layer was stochastically stimulated with top-down connections called inference weights or parameters. The sleep-stage probability was calculated, the state was sampled, and the result was estimated.For optimizing the training efficacy of the DBN, the Adamax optimizer was employed for altering the hyperparameter values :(20)wti=Algorithm 1. Pseudocode of Adamaxwhile  end whiledisplaying In this expression, η denotes the learning rate, This section examines the breast cancer classification results of the AOADL-HBCC model on a benchmark dataset . The datThe proposed model was simulated using Python 3.6.5 tools on PC i5-8600k, GeForce 1050Ti 4 GB, 16 GB RAM, 250 GB SSD, and 1 TB HDD. The parameter settings were given as follows: learning rate: 0.01, dropout: 0.5, batch size: 5, epoch count: 50, and activation: ReLU.The confusion matrices of the AOADL-HBCC model on the 100× dataset are reported in The TACC and VACC of the AOADL-HBCC technique under the 100× dataset are inspected on BCC performance in The TLS and VLS of the AOADL-HBCC approach under the 100× dataset are tested on BCC performance in A clear precision–recall investigation of the AOADL-HBCC methodology under the test database is given in A brief ROC analysis of the AOADL-HBCC approach under the test database is shown in The confusion matrices of the AOADL-HBCC approach on the 200× database are given in The TACC and VACC of the AOADL-HBCC method under the 200× dataset are inspected on BCC performance in The TLS and VLS of the AOADL-HBCC approach under the 200× dataset are tested on BCC performance in A clear precision–recall inspection of the AOADL-HBCC methodology under the test database is shown in A brief ROC study of the AOADL-HBCC system under the test database is given in A detailed comparative study of the AOADL-HBCC model with recent DL models is reported in Although the DTLRO-HCBC model reached near-optimal In this work, an automated breast cancer classification model, named the AOADL-HBCC technique, was developed on HIs. The presented AOADL-HBCC technique mainly aims to recognize the presence of breast cancer in HIs. At the primary level, the AOADL-HBCC technique exploited MF-based noise removal and a contrast enhancement process. In addition, the presented AOADL-HBCC technique utilized an AOA with a SqueezeNet model to derive feature vectors. Lastly, an Adamax optimizer with a DBN model was applied for the breast cancer classification process. In order to exhibit the enhanced breast cancer classification results of the AOADL-HBCC methodology, a wide range of simulations were performed. A comparative study indicated the better performance of the AOADL-HBCC technique over other recent methodologies, with a maximum accuracy of 96.77%. Therefore, the AOADL-HBCC technique can be employed for timely and accurate BC classification. In the future, ensemble-learning-based DL classifiers can be involved to boost the overall performance of the AOADL-HBCC technique. In addition, the performance of the proposed model can be tested on large-scale real-time databases.